rna sequence datasets: Topics by Science.gov

Sample records for rna sequence datasets

SPAR: small RNA-seq portal for analysis of sequencing experiments.

PubMed

Kuksa, Pavel P; Amlie-Wolf, Alexandre; Katanic, Živadin; Valladares, Otto; Wang, Li-San; Leung, Yuk Yee

2018-05-04

The introduction of new high-throughput small RNA sequencing protocols that generate large-scale genomics datasets along with increasing evidence of the significant regulatory roles of small non-coding RNAs (sncRNAs) have highlighted the urgent need for tools to analyze and interpret large amounts of small RNA sequencing data. However, it remains challenging to systematically and comprehensively discover and characterize sncRNA genes and specifically-processed sncRNA products from these datasets. To fill this gap, we present Small RNA-seq Portal for Analysis of sequencing expeRiments (SPAR), a user-friendly web server for interactive processing, analysis, annotation and visualization of small RNA sequencing data. SPAR supports sequencing data generated from various experimental protocols, including smRNA-seq, short total RNA sequencing, microRNA-seq, and single-cell small RNA-seq. Additionally, SPAR includes publicly available reference sncRNA datasets from our DASHR database and from ENCODE across 185 human tissues and cell types to produce highly informative small RNA annotations across all major small RNA types and other features such as co-localization with various genomic features, precursor transcript cleavage patterns, and conservation. SPAR allows the user to compare the input experiment against reference ENCODE/DASHR datasets. SPAR currently supports analyses of human (hg19, hg38) and mouse (mm10) sequencing data. SPAR is freely available at https://www.lisanwanglab.org/SPAR.
MIPE: A metagenome-based community structure explorer and SSU primer evaluation tool

PubMed Central

Zhou, Quan

2017-01-01

An understanding of microbial community structure is an important issue in the field of molecular ecology. The traditional molecular method involves amplification of small subunit ribosomal RNA (SSU rRNA) genes by polymerase chain reaction (PCR). However, PCR-based amplicon approaches are affected by primer bias and chimeras. With the development of high-throughput sequencing technology, unbiased SSU rRNA gene sequences can be mined from shotgun sequencing-based metagenomic or metatranscriptomic datasets to obtain a reflection of the microbial community structure in specific types of environment and to evaluate SSU primers. However, the use of short reads obtained through next-generation sequencing for primer evaluation has not been well resolved. The software MIPE (MIcrobiota metagenome Primer Explorer) was developed to adapt numerous short reads from metagenomes and metatranscriptomes. Using metagenomic or metatranscriptomic datasets as input, MIPE extracts and aligns rRNA to reveal detailed information on microbial composition and evaluate SSU rRNA primers. A mock dataset, a real Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) test dataset, two PrimerProspector test datasets and a real metatranscriptomic dataset were used to validate MIPE. The software calls Mothur (v1.33.3) and the SILVA database (v119) for the alignment and classification of rRNA genes from a metagenome or metatranscriptome. MIPE can effectively extract shotgun rRNA reads from a metagenome or metatranscriptome and is capable of classifying these sequences and exhibiting sensitivity to different SSU rRNA PCR primers. Therefore, MIPE can be used to guide primer design for specific environmental samples. PMID:28350876
Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

PubMed

Rizzetto, Simone; Eltahla, Auda A; Lin, Peijie; Bull, Rowena; Lloyd, Andrew R; Ho, Joshua W K; Venturi, Vanessa; Luciani, Fabio

2017-10-06

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCRαβ reconstruction was achieved for 6 datasets (81% - 100%) with at least 0.25 millions (PE) reads of length >50 bp, while it failed for datasets with <30 bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.
cGRNB: a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets.

PubMed

Xu, Huayong; Yu, Hui; Tu, Kang; Shi, Qianqian; Wei, Chaochun; Li, Yuan-Yuan; Li, Yi-Xue

2013-01-01

We are witnessing rapid progress in the development of methodologies for building the combinatorial gene regulatory networks involving both TFs (Transcription Factors) and miRNAs (microRNAs). There are a few tools available to do these jobs but most of them are not easy to use and not accessible online. A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts. In this work, we compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. We streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. As a result, we released the cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module. In this article we propose cGRNB, a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets. The cGRNB web-server is free and available online at http://www.scbit.org/cgrnb.
Analysis of plant-derived miRNAs in animal small RNA datasets

PubMed Central

2012-01-01

Background Plants contain significant quantities of small RNAs (sRNAs) derived from various sRNA biogenesis pathways. Many of these sRNAs play regulatory roles in plants. Previous analysis revealed that numerous sRNAs in corn, rice and soybean seeds have high sequence similarity to animal genes. However, exogenous RNA is considered to be unstable within the gastrointestinal tract of many animals, thus limiting potential for any adverse effects from consumption of dietary RNA. A recent paper reported that putative plant miRNAs were detected in animal plasma and serum, presumably acquired through ingestion, and may have a functional impact in the consuming organisms. Results To address the question of how common this phenomenon could be, we searched for plant miRNAs sequences in public sRNA datasets from various tissues of mammals, chicken and insects. Our analyses revealed that plant miRNAs were present in the animal sRNA datasets, and significantly miR168 was extremely over-represented. Furthermore, all or nearly all (>96%) miR168 sequences were monocot derived for most datasets, including datasets for two insects reared on dicot plants in their respective experiments. To investigate if plant-derived miRNAs, including miR168, could accumulate and move systemically in insects, we conducted insect feeding studies for three insects including corn rootworm, which has been shown to be responsive to plant-produced long double-stranded RNAs. Conclusions Our analyses suggest that the observed plant miRNAs in animal sRNA datasets can originate in the process of sequencing, and that accumulation of plant miRNAs via dietary exposure is not universal in animals. PMID:22873950
Predicting protein-binding regions in RNA using nucleotide profiles and compositions.

PubMed

Choi, Daesik; Park, Byungkyu; Chae, Hanju; Lee, Wook; Han, Kyungsook

2017-03-14

Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use. We developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others. Sequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding .
Classifying next-generation sequencing data using a zero-inflated Poisson model.

PubMed

Zhou, Yan; Wan, Xiang; Zhang, Baoxue; Tong, Tiejun

2018-04-15

With the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification. Witten proposed a Poisson linear discriminant analysis (PLDA) to classify the RNA-seq data in 2011. Note, however, that the count datasets are frequently characterized by excess zeros in real RNA-seq or microRNA sequence data (i.e. when the sequence depth is not enough or small RNAs with the length of 18-30 nucleotides). Therefore, it is desired to develop a new model to analyze RNA-seq data with an excess of zeros. In this paper, we propose a Zero-Inflated Poisson Logistic Discriminant Analysis (ZIPLDA) for RNA-seq data with an excess of zeros. The new method assumes that the data are from a mixture of two distributions: one is a point mass at zero, and the other follows a Poisson distribution. We then consider a logistic relation between the probability of observing zeros and the mean of the genes and the sequencing depth in the model. Simulation studies show that the proposed method performs better than, or at least as well as, the existing methods in a wide range of settings. Two real datasets including a breast cancer RNA-seq dataset and a microRNA-seq dataset are also analyzed, and they coincide with the simulation results that our proposed method outperforms the existing competitors. The software is available at http://www.math.hkbu.edu.hk/∼tongt. xwan@comp.hkbu.edu.hk or tongt@hkbu.edu.hk. Supplementary data are available at Bioinformatics online.
VaDiR: an integrated approach to Variant Detection in RNA.

PubMed

Neums, Lisa; Suenaga, Seiji; Beyerlein, Peter; Anders, Sara; Koestler, Devin; Mariani, Andrea; Chien, Jeremy

2018-02-01

Advances in next-generation DNA sequencing technologies are now enabling detailed characterization of sequence variations in cancer genomes. With whole-genome sequencing, variations in coding and non-coding sequences can be discovered. But the cost associated with it is currently limiting its general use in research. Whole-exome sequencing is used to characterize sequence variations in coding regions, but the cost associated with capture reagents and biases in capture rate limit its full use in research. Additional limitations include uncertainty in assigning the functional significance of the mutations when these mutations are observed in the non-coding region or in genes that are not expressed in cancer tissue. We investigated the feasibility of uncovering mutations from expressed genes using RNA sequencing datasets with a method called Variant Detection in RNA(VaDiR) that integrates 3 variant callers, namely: SNPiR, RVBoost, and MuTect2. The combination of all 3 methods, which we called Tier 1 variants, produced the highest precision with true positive mutations from RNA-seq that could be validated at the DNA level. We also found that the integration of Tier 1 variants with those called by MuTect2 and SNPiR produced the highest recall with acceptable precision. Finally, we observed a higher rate of mutation discovery in genes that are expressed at higher levels. Our method, VaDiR, provides a possibility of uncovering mutations from RNA sequencing datasets that could be useful in further functional analysis. In addition, our approach allows orthogonal validation of DNA-based mutation discovery by providing complementary sequence variation analysis from paired RNA/DNA sequencing datasets.
CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment.

PubMed

Oh, Jeongsu; Choi, Chi-Hwan; Park, Min-Kyu; Kim, Byung Kwon; Hwang, Kyuin; Lee, Sang-Heon; Hong, Soon Gyu; Nasir, Arshan; Cho, Wan-Sup; Kim, Kyung Mo

2016-01-01

High-throughput sequencing can produce hundreds of thousands of 16S rRNA sequence reads corresponding to different organisms present in the environmental samples. Typically, analysis of microbial diversity in bioinformatics starts from pre-processing followed by clustering 16S rRNA reads into relatively fewer operational taxonomic units (OTUs). The OTUs are reliable indicators of microbial diversity and greatly accelerate the downstream analysis time. However, existing hierarchical clustering algorithms that are generally more accurate than greedy heuristic algorithms struggle with large sequence datasets. To keep pace with the rapid rise in sequencing data, we present CLUSTOM-CLOUD, which is the first distributed sequence clustering program based on In-Memory Data Grid (IMDG) technology-a distributed data structure to store all data in the main memory of multiple computing nodes. The IMDG technology helps CLUSTOM-CLOUD to enhance both its capability of handling larger datasets and its computational scalability better than its ancestor, CLUSTOM, while maintaining high accuracy. Clustering speed of CLUSTOM-CLOUD was evaluated on published 16S rRNA human microbiome sequence datasets using the small laboratory cluster (10 nodes) and under the Amazon EC2 cloud-computing environments. Under the laboratory environment, it required only ~3 hours to process dataset of size 200 K reads regardless of the complexity of the human microbiome data. In turn, one million reads were processed in approximately 20, 14, and 11 hours when utilizing 20, 30, and 40 nodes on the Amazon EC2 cloud-computing environment. The running time evaluation indicates that CLUSTOM-CLOUD can handle much larger sequence datasets than CLUSTOM and is also a scalable distributed processing system. The comparative accuracy test using 16S rRNA pyrosequences of a mock community shows that CLUSTOM-CLOUD achieves higher accuracy than DOTUR, mothur, ESPRIT-Tree, UCLUST and Swarm. CLUSTOM-CLOUD is written in JAVA and is freely available at http://clustomcloud.kopri.re.kr.
CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment

PubMed Central

Park, Min-Kyu; Kim, Byung Kwon; Hwang, Kyuin; Lee, Sang-Heon; Hong, Soon Gyu; Nasir, Arshan; Cho, Wan-Sup; Kim, Kyung Mo

2016-01-01

High-throughput sequencing can produce hundreds of thousands of 16S rRNA sequence reads corresponding to different organisms present in the environmental samples. Typically, analysis of microbial diversity in bioinformatics starts from pre-processing followed by clustering 16S rRNA reads into relatively fewer operational taxonomic units (OTUs). The OTUs are reliable indicators of microbial diversity and greatly accelerate the downstream analysis time. However, existing hierarchical clustering algorithms that are generally more accurate than greedy heuristic algorithms struggle with large sequence datasets. To keep pace with the rapid rise in sequencing data, we present CLUSTOM-CLOUD, which is the first distributed sequence clustering program based on In-Memory Data Grid (IMDG) technology–a distributed data structure to store all data in the main memory of multiple computing nodes. The IMDG technology helps CLUSTOM-CLOUD to enhance both its capability of handling larger datasets and its computational scalability better than its ancestor, CLUSTOM, while maintaining high accuracy. Clustering speed of CLUSTOM-CLOUD was evaluated on published 16S rRNA human microbiome sequence datasets using the small laboratory cluster (10 nodes) and under the Amazon EC2 cloud-computing environments. Under the laboratory environment, it required only ~3 hours to process dataset of size 200 K reads regardless of the complexity of the human microbiome data. In turn, one million reads were processed in approximately 20, 14, and 11 hours when utilizing 20, 30, and 40 nodes on the Amazon EC2 cloud-computing environment. The running time evaluation indicates that CLUSTOM-CLOUD can handle much larger sequence datasets than CLUSTOM and is also a scalable distributed processing system. The comparative accuracy test using 16S rRNA pyrosequences of a mock community shows that CLUSTOM-CLOUD achieves higher accuracy than DOTUR, mothur, ESPRIT-Tree, UCLUST and Swarm. CLUSTOM-CLOUD is written in JAVA and is freely available at http://clustomcloud.kopri.re.kr. PMID:26954507
Identification of characteristic oligonucleotides in the bacterial 16S ribosomal RNA sequence dataset

NASA Technical Reports Server (NTRS)

Zhang, Zhengdong; Willson, Richard C.; Fox, George E.

2002-01-01

MOTIVATION: The phylogenetic structure of the bacterial world has been intensively studied by comparing sequences of 16S ribosomal RNA (16S rRNA). This database of sequences is now widely used to design probes for the detection of specific bacteria or groups of bacteria one at a time. The success of such methods reflects the fact that there are local sequence segments that are highly characteristic of particular organisms or groups of organisms. It is not clear, however, the extent to which such signature sequences exist in the 16S rRNA dataset. A better understanding of the numbers and distribution of highly informative oligonucleotide sequences may facilitate the design of hybridization arrays that can characterize the phylogenetic position of an unknown organism or serve as the basis for the development of novel approaches for use in bacterial identification. RESULTS: A computer-based algorithm that characterizes the extent to which any individual oligonucleotide sequence in 16S rRNA is characteristic of any particular bacterial grouping was developed. A measure of signature quality, Q(s), was formulated and subsequently calculated for every individual oligonucleotide sequence in the size range of 5-11 nucleotides and for 15mers with reference to each cluster and subcluster in a 929 organism representative phylogenetic tree. Subsequently, the perfect signature sequences were compared to the full set of 7322 sequences to see how common false positives were. The work completed here establishes beyond any doubt that highly characteristic oligonucleotides exist in the bacterial 16S rRNA sequence dataset in large numbers. Over 16,000 15mers were identified that might be useful as signatures. Signature oligonucleotides are available for over 80% of the nodes in the representative tree.
JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets.

PubMed

Ner-Gaon, Hadas; Melchior, Ariel; Golan, Nili; Ben-Haim, Yael; Shay, Tal

2017-05-01

Recent advances in single-cell RNA-sequencing (scRNA-seq) technology increase the understanding of immune differentiation and activation processes, as well as the heterogeneity of immune cell types. Although the number of available immune-related scRNA-seq datasets increases rapidly, their large size and various formats render them hard for the wider immunology community to use, and read-level data are practically inaccessible to the non-computational immunologist. To facilitate datasets reuse, we created the JingleBells repository for immune-related scRNA-seq datasets ready for analysis and visualization of reads at the single-cell level (http://jinglebells.bgu.ac.il/). To this end, we collected the raw data of publicly available immune-related scRNA-seq datasets, aligned the reads to the relevant genome, and saved aligned reads in a uniform format, annotated for cell of origin. We also added scripts and a step-by-step tutorial for visualizing each dataset at the single-cell level, through the commonly used Integrated Genome Viewer (www.broadinstitute.org/igv/). The uniform scRNA-seq format used in JingleBells can facilitate reuse of scRNA-seq data by computational biologists. It also enables immunologists who are interested in a specific gene to visualize the reads aligned to this gene to estimate cell-specific preferences for splicing, mutation load, or alleles. Thus JingleBells is a resource that will extend the usefulness of scRNA-seq datasets outside the programming aficionado realm. Copyright © 2017 by The American Association of Immunologists, Inc.
RNA design using simulated SHAPE data.

PubMed

Lotfi, Mohadeseh; Zare-Mirakabad, Fatemeh; Montaseri, Soheila

2018-05-03

It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research. The challenge of finding an RNA sequence for a given structure is known as the RNA design problem. Although there are several algorithms to solve this problem, they mainly consider hard constraints, such as minimum free energy, to evaluate the predicted sequences. Recently, SHAPE data has emerged as a new soft constraint for RNA secondary structure prediction. To take advantage of this new experimental constraint, we report here a new method for accurate design of RNA sequences based on their secondary structures using SHAPE data as pseudo-free energy. We then compare our algorithm with four others: INFO-RNA, ERD, MODENA and RNAifold 2.0. Our algorithm precisely predicts 26 out of 29 new sequences for the structures extracted from the Rfam dataset, while the other four algorithms predict no more than 22 out of 29. The proposed algorithm is comparable to the above algorithms on RNA-SSD datasets, where they can predict up to 33 appropriate sequences for RNA secondary structures out of 34.
miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets

PubMed Central

Paicu, Claudia; Mohorianu, Irina; Stocks, Matthew; Xu, Ping; Coince, Aurore; Billmeier, Martina; Dalmay, Tamas; Moulton, Vincent; Moxon, Simon

2017-01-01

Abstract Motivation MicroRNAs are a class of ∼21–22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure. Results We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway. Availability and Implementation miRCat2 is part of the UEA small RNA Workbench and is freely available from http://srna-workbench.cmp.uea.ac.uk/. Contact v.moulton@uea.ac.uk or s.moxon@uea.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:28407097
Geoseq: a tool for dissecting deep-sequencing datasets.

PubMed

Gurtowski, James; Cancio, Anthony; Shah, Hardik; Levovitz, Chaya; George, Ajish; Homann, Robert; Sachidanandam, Ravi

2010-10-12

Datasets generated on deep-sequencing platforms have been deposited in various public repositories such as the Gene Expression Omnibus (GEO), Sequence Read Archive (SRA) hosted by the NCBI, or the DNA Data Bank of Japan (ddbj). Despite being rich data sources, they have not been used much due to the difficulty in locating and analyzing datasets of interest. Geoseq http://geoseq.mssm.edu provides a new method of analyzing short reads from deep sequencing experiments. Instead of mapping the reads to reference genomes or sequences, Geoseq maps a reference sequence against the sequencing data. It is web-based, and holds pre-computed data from public libraries. The analysis reduces the input sequence to tiles and measures the coverage of each tile in a sequence library through the use of suffix arrays. The user can upload custom target sequences or use gene/miRNA names for the search and get back results as plots and spreadsheet files. Geoseq organizes the public sequencing data using a controlled vocabulary, allowing identification of relevant libraries by organism, tissue and type of experiment. Analysis of small sets of sequences against deep-sequencing datasets, as well as identification of public datasets of interest, is simplified by Geoseq. We applied Geoseq to, a) identify differential isoform expression in mRNA-seq datasets, b) identify miRNAs (microRNAs) in libraries, and identify mature and star sequences in miRNAS and c) to identify potentially mis-annotated miRNAs. The ease of using Geoseq for these analyses suggests its utility and uniqueness as an analysis tool.
Prediction of constitutive A-to-I editing sites from human transcriptomes in the absence of genomic sequences

PubMed Central

2013-01-01

Background Adenosine-to-inosine (A-to-I) RNA editing is recognized as a cellular mechanism for generating both RNA and protein diversity. Inosine base pairs with cytidine during reverse transcription and therefore appears as guanosine during sequencing of cDNA. Current approaches of RNA editing identification largely depend on the comparison between transcriptomes and genomic DNA (gDNA) sequencing datasets from the same individuals, and it has been challenging to identify editing candidates from transcriptomes in the absence of gDNA information. Results We have developed a new strategy to accurately predict constitutive RNA editing sites from publicly available human RNA-seq datasets in the absence of relevant genomic sequences. Our approach establishes new parameters to increase the ability to map mismatches and to minimize sequencing/mapping errors and unreported genome variations. We identified 695 novel constitutive A-to-I editing sites that appear in clusters (named “editing boxes”) in multiple samples and which exhibit spatial and dynamic regulation across human tissues. Some of these editing boxes are enriched in non-repetitive regions lacking inverted repeat structures and contain an extremely high conversion frequency of As to Is. We validated a number of editing boxes in multiple human cell lines and confirmed that ADAR1 is responsible for the observed promiscuous editing events in non-repetitive regions, further expanding our knowledge of the catalytic substrate of A-to-I RNA editing by ADAR enzymes. Conclusions The approach we present here provides a novel way of identifying A-to-I RNA editing events by analyzing only RNA-seq datasets. This method has allowed us to gain new insights into RNA editing and should also aid in the identification of more constitutive A-to-I editing sites from additional transcriptomes. PMID:23537002
A computational method for estimating the PCR duplication rate in DNA and RNA-seq experiments.

PubMed

Bansal, Vikas

2017-03-14

PCR amplification is an important step in the preparation of DNA sequencing libraries prior to high-throughput sequencing. PCR amplification introduces redundant reads in the sequence data and estimating the PCR duplication rate is important to assess the frequency of such reads. Existing computational methods do not distinguish PCR duplicates from "natural" read duplicates that represent independent DNA fragments and therefore, over-estimate the PCR duplication rate for DNA-seq and RNA-seq experiments. In this paper, we present a computational method to estimate the average PCR duplication rate of high-throughput sequence datasets that accounts for natural read duplicates by leveraging heterozygous variants in an individual genome. Analysis of simulated data and exome sequence data from the 1000 Genomes project demonstrated that our method can accurately estimate the PCR duplication rate on paired-end as well as single-end read datasets which contain a high proportion of natural read duplicates. Further, analysis of exome datasets prepared using the Nextera library preparation method indicated that 45-50% of read duplicates correspond to natural read duplicates likely due to fragmentation bias. Finally, analysis of RNA-seq datasets from individuals in the 1000 Genomes project demonstrated that 70-95% of read duplicates observed in such datasets correspond to natural duplicates sampled from genes with high expression and identified outlier samples with a 2-fold greater PCR duplication rate than other samples. The method described here is a useful tool for estimating the PCR duplication rate of high-throughput sequence datasets and for assessing the fraction of read duplicates that correspond to natural read duplicates. An implementation of the method is available at https://github.com/vibansal/PCRduplicates .
Harnessing NGS and Big Data Optimally: Comparison of miRNA Prediction from Assembled versus Non-assembled Sequencing Data--The Case of the Grass Aegilops tauschii Complex Genome.

PubMed

Budak, Hikmet; Kantar, Melda

2015-07-01

MicroRNAs (miRNAs) are small, endogenous, non-coding RNA molecules that regulate gene expression at the post-transcriptional level. As high-throughput next generation sequencing (NGS) and Big Data rapidly accumulate for various species, efforts for in silico identification of miRNAs intensify. Surprisingly, the effect of the input genomics sequence on the robustness of miRNA prediction was not evaluated in detail to date. In the present study, we performed a homology-based miRNA and isomiRNA prediction of the 5D chromosome of bread wheat progenitor, Aegilops tauschii, using two distinct sequence data sets as input: (1) raw sequence reads obtained from 454-GS FLX Titanium sequencing platform and (2) an assembly constructed from these reads. We also compared this method with a number of available plant sequence datasets. We report here the identification of 62 and 22 miRNAs from raw reads and the assembly, respectively, of which 16 were predicted with high confidence from both datasets. While raw reads promoted sensitivity with the high number of miRNAs predicted, 55% (12 out of 22) of the assembly-based predictions were supported by previous observations, bringing specificity forward compared to the read-based predictions, of which only 37% were supported. Importantly, raw reads could identify several repeat-related miRNAs that could not be detected with the assembly. However, raw reads could not capture 6 miRNAs, for which the stem-loops could only be covered by the relatively longer sequences from the assembly. In summary, the comparison of miRNA datasets obtained by these two strategies revealed that utilization of raw reads, as well as assemblies for in silico prediction, have distinct advantages and disadvantages. Consideration of these important nuances can benefit future miRNA identification efforts in the current age of NGS and Big Data driven life sciences innovation.
Genome-Wide Association Study of a Validated Case Definition of Gulf War Illness in a Population-Representative Sample

DTIC Science & Technology

2013-09-01

sequence dataset. All procedures were performed by personnel in the IIMT UT Southwestern Genomics and Microarray Core using standard protocols. More... sequencing run, samples were demultiplexed using standard algorithms in the Genomics and Microarray Core and processed into individual sample Illumina single... Sequencing (RNA-Seq), using Illumina’s multiplexing mRNA-Seq to generate full sequence libraries from the poly-A tailed RNA to a read depth of 30
antaRNA: ant colony-based RNA sequence design.

PubMed

Kleinkauf, Robert; Mann, Martin; Backofen, Rolf

2015-10-01

RNA sequence design is studied at least as long as the classical folding problem. Although for the latter the functional fold of an RNA molecule is to be found ,: inverse folding tries to identify RNA sequences that fold into a function-specific target structure. In combination with RNA-based biotechnology and synthetic biology ,: reliable RNA sequence design becomes a crucial step to generate novel biochemical components. In this article ,: the computational tool antaRNA is presented. It is capable of compiling RNA sequences for a given structure that comply in addition with an adjustable full range objective GC-content distribution ,: specific sequence constraints and additional fuzzy structure constraints. antaRNA applies ant colony optimization meta-heuristics and its superior performance is shown on a biological datasets. http://www.bioinf.uni-freiburg.de/Software/antaRNA CONTACT: backofen@informatik.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Identification of novel RNA viruses in alfalfa (Medicago sativa): an Alphapartitivirus, a Deltapartitivirus, and a Marafivirus.

PubMed

Kim, Hyein; Park, Dongbin; Hahn, Yoonsoo

2018-01-05

Genomic RNA molecules of plant RNA viruses are often co-isolated with the host RNAs, and their sequences can be detected in plant transcriptome datasets. Here, an alfalfa (Medicago sativa) transcriptome dataset was analyzed and three new RNA viruses were identified, which were named Medicago sativa alphapartitivirus 1 (MsAPV1), Medicago sativa deltapartitivirus 1 (MsDPV1), and Medicago sativa marafivirus 1 (MsMV1). The RNA-dependent RNA polymerases of MsAPV1, MsDPV1, and MsMV1 showed about 68%, 58%, and 46% amino acid sequence identity, respectively, with their closest virus species. Sequence similarity and phylogenetic analyses indicated that MsAPV1, MsDPV1, and MsMV1 were novel RNA virus species that belong to the genus Alphapartitivirus of the family Partitiviridae, the genus Deltapartitivirus of the family Partitiviridae, and the genus Marafivirus of the family Tymoviridae, respectively. The bioinformatics procedure applied in this study may facilitate the identification of novel RNA viruses from plant transcriptome data. Copyright © 2017 Elsevier B.V. All rights reserved.
Improved detection of CXCR4-using HIV by V3 genotyping: application of population-based and "deep" sequencing to plasma RNA and proviral DNA.

PubMed

Swenson, Luke C; Moores, Andrew; Low, Andrew J; Thielen, Alexander; Dong, Winnie; Woods, Conan; Jensen, Mark A; Wynhoven, Brian; Chan, Dennison; Glascock, Christopher; Harrigan, P Richard

2010-08-01

Tropism testing should rule out CXCR4-using HIV before treatment with CCR5 antagonists. Currently, the recombinant phenotypic Trofile assay (Monogram) is most widely utilized; however, genotypic tests may represent alternative methods. Independent triplicate amplifications of the HIV gp120 V3 region were made from either plasma HIV RNA or proviral DNA. These underwent standard, population-based sequencing with an ABI3730 (RNA n = 63; DNA n = 40), or "deep" sequencing with a Roche/454 Genome Sequencer-FLX (RNA n = 12; DNA n = 12). Position-specific scoring matrices (PSSMX4/R5) (-6.96 cutoff) and geno2pheno[coreceptor] (5% false-positive rate) inferred tropism from V3 sequence. These methods were then independently validated with a separate, blinded dataset (n = 278) of screening samples from the maraviroc MOTIVATE trials. Standard sequencing of HIV RNA with PSSM yielded 69% sensitivity and 91% specificity, relative to Trofile. The validation dataset gave 75% sensitivity and 83% specificity. Proviral DNA plus PSSM gave 77% sensitivity and 71% specificity. "Deep" sequencing of HIV RNA detected >2% inferred-CXCR4-using virus in 8/8 samples called non-R5 by Trofile, and <2% in 4/4 samples called R5. Triplicate analyses of V3 standard sequence data detect greater proportions of CXCR4-using samples than previously achieved. Sequencing proviral DNA and "deep" V3 sequencing may also be useful tools for assessing tropism.
Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kwon, ChangHyuk, E-mail: netbuyer@hanmail.net; Tak, Hyosun, E-mail: chuberry@naver.com; Rho, Mina, E-mail: minarho@hanyang.ac.kr

2014-03-28

Highlights: • piRNA sequences were mapped to human mitochondrial (mt) genome. • We inspected small RNA-Seq datasets from somatic cell mt subcellular fractions. • Piwi and piRNA transcripts are present in mammalian somatic cancer cell mt fractions. - Abstract: Piwi-interacting RNAs (piRNAs) are 26–31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWImore » and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies.« less
SearchSmallRNA: a graphical interface tool for the assemblage of viral genomes using small RNA libraries data.

PubMed

de Andrade, Roberto R S; Vaslin, Maite F S

2014-03-07

Next-generation parallel sequencing (NGS) allows the identification of viral pathogens by sequencing the small RNAs of infected hosts. Thus, viral genomes may be assembled from host immune response products without prior virus enrichment, amplification or purification. However, mapping of the vast information obtained presents a bioinformatics challenge. In order to by pass the need of line command and basic bioinformatics knowledge, we develop a mapping software with a graphical interface to the assemblage of viral genomes from small RNA dataset obtained by NGS. SearchSmallRNA was developed in JAVA language version 7 using NetBeans IDE 7.1 software. The program also allows the analysis of the viral small interfering RNAs (vsRNAs) profile; providing an overview of the size distribution and other features of the vsRNAs produced in infected cells. The program performs comparisons between each read sequenced present in a library and a chosen reference genome. Reads showing Hamming distances smaller or equal to an allowed mismatched will be selected as positives and used to the assemblage of a long nucleotide genome sequence. In order to validate the software, distinct analysis using NGS dataset obtained from HIV and two plant viruses were used to reconstruct viral whole genomes. SearchSmallRNA program was able to reconstructed viral genomes using NGS of small RNA dataset with high degree of reliability so it will be a valuable tool for viruses sequencing and discovery. It is accessible and free to all research communities and has the advantage to have an easy-to-use graphical interface. SearchSmallRNA was written in Java and is freely available at http://www.microbiologia.ufrj.br/ssrna/.
SearchSmallRNA: a graphical interface tool for the assemblage of viral genomes using small RNA libraries data

PubMed Central

2014-01-01

Background Next-generation parallel sequencing (NGS) allows the identification of viral pathogens by sequencing the small RNAs of infected hosts. Thus, viral genomes may be assembled from host immune response products without prior virus enrichment, amplification or purification. However, mapping of the vast information obtained presents a bioinformatics challenge. Methods In order to by pass the need of line command and basic bioinformatics knowledge, we develop a mapping software with a graphical interface to the assemblage of viral genomes from small RNA dataset obtained by NGS. SearchSmallRNA was developed in JAVA language version 7 using NetBeans IDE 7.1 software. The program also allows the analysis of the viral small interfering RNAs (vsRNAs) profile; providing an overview of the size distribution and other features of the vsRNAs produced in infected cells. Results The program performs comparisons between each read sequenced present in a library and a chosen reference genome. Reads showing Hamming distances smaller or equal to an allowed mismatched will be selected as positives and used to the assemblage of a long nucleotide genome sequence. In order to validate the software, distinct analysis using NGS dataset obtained from HIV and two plant viruses were used to reconstruct viral whole genomes. Conclusions SearchSmallRNA program was able to reconstructed viral genomes using NGS of small RNA dataset with high degree of reliability so it will be a valuable tool for viruses sequencing and discovery. It is accessible and free to all research communities and has the advantage to have an easy-to-use graphical interface. Availability and implementation SearchSmallRNA was written in Java and is freely available at http://www.microbiologia.ufrj.br/ssrna/. PMID:24607237
Cross-species inference of long non-coding RNAs greatly expands the ruminant transcriptome.

PubMed

Bush, Stephen J; Muriuki, Charity; McCulloch, Mary E B; Farquhar, Iseabail L; Clark, Emily L; Hume, David A

2018-04-24

mRNA-like long non-coding RNAs (lncRNAs) are a significant component of mammalian transcriptomes, although most are expressed only at low levels, with high tissue-specificity and/or at specific developmental stages. Thus, in many cases lncRNA detection by RNA-sequencing (RNA-seq) is compromised by stochastic sampling. To account for this and create a catalogue of ruminant lncRNAs, we compared de novo assembled lncRNAs derived from large RNA-seq datasets in transcriptional atlas projects for sheep and goats with previous lncRNAs assembled in cattle and human. We then combined the novel lncRNAs with the sheep transcriptional atlas to identify co-regulated sets of protein-coding and non-coding loci. Few lncRNAs could be reproducibly assembled from a single dataset, even with deep sequencing of the same tissues from multiple animals. Furthermore, there was little sequence overlap between lncRNAs that were assembled from pooled RNA-seq data. We combined positional conservation (synteny) with cross-species mapping of candidate lncRNAs to identify a consensus set of ruminant lncRNAs and then used the RNA-seq data to demonstrate detectable and reproducible expression in each species. In sheep, 20 to 30% of lncRNAs were located close to protein-coding genes with which they are strongly co-expressed, which is consistent with the evolutionary origin of some ncRNAs in enhancer sequences. Nevertheless, most of the lncRNAs are not co-expressed with neighbouring protein-coding genes. Alongside substantially expanding the ruminant lncRNA repertoire, the outcomes of our analysis demonstrate that stochastic sampling can be partly overcome by combining RNA-seq datasets from related species. This has practical implications for the future discovery of lncRNAs in other species.
Modified RNA-seq method for microbial community and diversity analysis using rRNA in different types of environmental samples

PubMed Central

Yan, Yong-Wei; Zou, Bin; Zhu, Ting; Hozzein, Wael N.

2017-01-01

RNA-seq-based SSU (small subunit) rRNA (ribosomal RNA) analysis has provided a better understanding of potentially active microbial community within environments. However, for RNA-seq library construction, high quantities of purified RNA are typically required. We propose a modified RNA-seq method for SSU rRNA-based microbial community analysis that depends on the direct ligation of a 5’ adaptor to RNA before reverse-transcription. The method requires only a low-input quantity of RNA (10–100 ng) and does not require a DNA removal step. The method was initially tested on three mock communities synthesized with enriched SSU rRNA of archaeal, bacterial and fungal isolates at different ratios, and was subsequently used for environmental samples of high or low biomass. For high-biomass salt-marsh sediments, enriched SSU rRNA and total nucleic acid-derived RNA-seq datasets revealed highly consistent community compositions for all of the SSU rRNA sequences, and as much as 46.4%-59.5% of 16S rRNA sequences were suitable for OTU (operational taxonomic unit)-based community and diversity analyses with complete coverage of V1-V2 regions. OTU-based community structures for the two datasets were also highly consistent with those determined by all of the 16S rRNA reads. For low-biomass samples, total nucleic acid-derived RNA-seq datasets were analyzed, and highly active bacterial taxa were also identified by the OTU-based method, notably including members of the previously underestimated genus Nitrospira and phylum Acidobacteria in tap water, members of the phylum Actinobacteria on a shower curtain, and members of the phylum Cyanobacteria on leaf surfaces. More than half of the bacterial 16S rRNA sequences covered the complete region of primer 8F, and non-coverage rates as high as 38.7% were obtained for phylum-unclassified sequences, providing many opportunities to identify novel bacterial taxa. This modified RNA-seq method will provide a better snapshot of diverse microbial communities, most notably by OTU-based analysis, even communities with low-biomass samples. PMID:29016661
Identification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with Idiopathic Pulmonary Fibrosis.

PubMed

Vukmirovic, Milica; Herazo-Maya, Jose D; Blackmon, John; Skodric-Trifunovic, Vesna; Jovanovic, Dragana; Pavlovic, Sonja; Stojsic, Jelena; Zeljkovic, Vesna; Yan, Xiting; Homer, Robert; Stefanovic, Branko; Kaminski, Naftali

2017-01-12

Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues. We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average ~62 million reads (53.4% of ~116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR < 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR < 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter® we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four. Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF.
IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies.

PubMed

Lagkouvardos, Ilias; Joseph, Divya; Kapfhammer, Martin; Giritli, Sabahattin; Horn, Matthias; Haller, Dirk; Clavel, Thomas

2016-09-23

The SRA (Sequence Read Archive) serves as primary depository for massive amounts of Next Generation Sequencing data, and currently host over 100,000 16S rRNA gene amplicon-based microbial profiles from various host habitats and environments. This number is increasing rapidly and there is a dire need for approaches to utilize this pool of knowledge. Here we created IMNGS (Integrated Microbial Next Generation Sequencing), an innovative platform that uniformly and systematically screens for and processes all prokaryotic 16S rRNA gene amplicon datasets available in SRA and uses them to build sample-specific sequence databases and OTU-based profiles. Via a web interface, this integrative sequence resource can easily be queried by users. We show examples of how the approach allows testing the ecological importance of specific microorganisms in different hosts or ecosystems, and performing targeted diversity studies for selected taxonomic groups. The platform also offers a complete workflow for de novo analysis of users' own raw 16S rRNA gene amplicon datasets for the sake of comparison with existing data. IMNGS can be accessed at www.imngs.org.
SEXCMD: Development and validation of sex marker sequences for whole-exome/genome and RNA sequencing.

PubMed

Jeong, Seongmun; Kim, Jiwoong; Park, Won; Jeon, Hongmin; Kim, Namshin

2017-01-01

Over the last decade, a large number of nucleotide sequences have been generated by next-generation sequencing technologies and deposited to public databases. However, most of these datasets do not specify the sex of individuals sampled because researchers typically ignore or hide this information. Male and female genomes in many species have distinctive sex chromosomes, XX/XY and ZW/ZZ, and expression levels of many sex-related genes differ between the sexes. Herein, we describe how to develop sex marker sequences from syntenic regions of sex chromosomes and use them to quickly identify the sex of individuals being analyzed. Array-based technologies routinely use either known sex markers or the B-allele frequency of X or Z chromosomes to deduce the sex of an individual. The same strategy has been used with whole-exome/genome sequence data; however, all reads must be aligned onto a reference genome to determine the B-allele frequency of the X or Z chromosomes. SEXCMD is a pipeline that can extract sex marker sequences from reference sex chromosomes and rapidly identify the sex of individuals from whole-exome/genome and RNA sequencing after training with a known dataset through a simple machine learning approach. The pipeline counts total numbers of hits from sex-specific marker sequences and identifies the sex of the individuals sampled based on the fact that XX/ZZ samples do not have Y or W chromosome hits. We have successfully validated our pipeline with mammalian (Homo sapiens; XY) and avian (Gallus gallus; ZW) genomes. Typical calculation time when applying SEXCMD to human whole-exome or RNA sequencing datasets is a few minutes, and analyzing human whole-genome datasets takes about 10 minutes. Another important application of SEXCMD is as a quality control measure to avoid mixing samples before bioinformatics analysis. SEXCMD comprises simple Python and R scripts and is freely available at https://github.com/lovemun/SEXCMD.
X-MATE: a flexible system for mapping short read data

PubMed Central

Pearson, John V.; Cloonan, Nicole; Grimmond, Sean M.

2011-01-01

Summary: Accurate and complete mapping of short-read sequencing to a reference genome greatly enhances the discovery of biological results and improves statistical predictions. We recently presented RNA-MATE, a pipeline for the recursive mapping of RNA-Seq datasets. With the rapid increase in genome re-sequencing projects, progression of available mapping software and the evolution of file formats, we now present X-MATE, an updated version of RNA-MATE, capable of mapping both RNA-Seq and DNA datasets and with improved performance, output file formats, configuration files, and flexibility in core mapping software. Availability: Executables, source code, junction libraries, test data and results and the user manual are available from http://grimmond.imb.uq.edu.au/X-MATE/. Contact: n.cloonan@uq.edu.au; s.grimmond@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics Online. PMID:21216778
TaxI: a software tool for DNA barcoding using distance methods

PubMed Central

Steinke, Dirk; Vences, Miguel; Salzburger, Walter; Meyer, Axel

2005-01-01

DNA barcoding is a promising approach to the diagnosis of biological diversity in which DNA sequences serve as the primary key for information retrieval. Most existing software for evolutionary analysis of DNA sequences was designed for phylogenetic analyses and, hence, those algorithms do not offer appropriate solutions for the rapid, but precise analyses needed for DNA barcoding, and are also unable to process the often large comparative datasets. We developed a flexible software tool for DNA taxonomy, named TaxI. This program calculates sequence divergences between a query sequence (taxon to be barcoded) and each sequence of a dataset of reference sequences defined by the user. Because the analysis is based on separate pairwise alignments this software is also able to work with sequences characterized by multiple insertions and deletions that are difficult to align in large sequence sets (i.e. thousands of sequences) by multiple alignment algorithms because of computational restrictions. Here, we demonstrate the utility of this approach with two datasets of fish larvae and juveniles from Lake Constance and juvenile land snails under different models of sequence evolution. Sets of ribosomal 16S rRNA sequences, characterized by multiple indels, performed as good as or better than cox1 sequence sets in assigning sequences to species, demonstrating the suitability of rRNA genes for DNA barcoding. PMID:16214755
Bellerophon: A program to detect chimeric sequences in multiple sequence alignments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huber, Thomas; Faulkner, Geoffrey; Hugenholtz, Philip

2003-12-23

Bellerophon is a program for detecting chimeric sequences in multiple sequence datasets by an adaption of partial treeing analysis. Bellerophon was specifically developed to detect 16S rRNA gene chimeras in PCR-clone libraries of environmental samples but can be applied to other nucleotide sequence alignments.
Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

PubMed Central

Griffith, Malachi; Walker, Jason R.; Spies, Nicholas C.; Ainscough, Benjamin J.; Griffith, Obi L.

2015-01-01

Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki. PMID:26248053
The sponge microbiome project.

PubMed

Moitinho-Silva, Lucas; Nielsen, Shaun; Amir, Amnon; Gonzalez, Antonio; Ackermann, Gail L; Cerrano, Carlo; Astudillo-Garcia, Carmen; Easson, Cole; Sipkema, Detmer; Liu, Fang; Steinert, Georg; Kotoulas, Giorgos; McCormack, Grace P; Feng, Guofang; Bell, James J; Vicente, Jan; Björk, Johannes R; Montoya, Jose M; Olson, Julie B; Reveillaud, Julie; Steindler, Laura; Pineda, Mari-Carmen; Marra, Maria V; Ilan, Micha; Taylor, Michael W; Polymenakou, Paraskevi; Erwin, Patrick M; Schupp, Peter J; Simister, Rachel L; Knight, Rob; Thacker, Robert W; Costa, Rodrigo; Hill, Russell T; Lopez-Legentil, Susanna; Dailianis, Thanos; Ravasi, Timothy; Hentschel, Ute; Li, Zhiyong; Webster, Nicole S; Thomas, Torsten

2017-10-01

Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere. © The Authors 2017. Published by Oxford University Press.
Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification

PubMed Central

Deng, Yue; Bao, Feng; Yang, Yang; Ji, Xiangyang; Du, Mulong; Zhang, Zhengdong

2017-01-01

Abstract The automated transcript discovery and quantification of high-throughput RNA sequencing (RNA-seq) data are important tasks of next-generation sequencing (NGS) research. However, these tasks are challenging due to the uncertainties that arise in the inference of complete splicing isoform variants from partially observed short reads. Here, we address this problem by explicitly reducing the inherent uncertainties in a biological system caused by missing information. In our approach, the RNA-seq procedure for transforming transcripts into short reads is considered an information transmission process. Consequently, the data uncertainties are substantially reduced by exploiting the information transduction capacity of information theory. The experimental results obtained from the analyses of simulated datasets and RNA-seq datasets from cell lines and tissues demonstrate the advantages of our method over state-of-the-art competitors. Our algorithm is an open-source implementation of MaxInfo. PMID:28911101
TranslatomeDB: a comprehensive database and cloud-based analysis platform for translatome sequencing data

PubMed Central

Liu, Wanting; Xiang, Lunping; Zheng, Tingkai; Jin, Jingjie

2018-01-01

Abstract Translation is a key regulatory step, linking transcriptome and proteome. Two major methods of translatome investigations are RNC-seq (sequencing of translating mRNA) and Ribo-seq (ribosome profiling). To facilitate the investigation of translation, we built a comprehensive database TranslatomeDB (http://www.translatomedb.net/) which provides collection and integrated analysis of published and user-generated translatome sequencing data. The current version includes 2453 Ribo-seq, 10 RNC-seq and their 1394 corresponding mRNA-seq datasets in 13 species. The database emphasizes the analysis functions in addition to the dataset collections. Differential gene expression (DGE) analysis can be performed between any two datasets of same species and type, both on transcriptome and translatome levels. The translation indices translation ratios, elongation velocity index and translational efficiency can be calculated to quantitatively evaluate translational initiation efficiency and elongation velocity, respectively. All datasets were analyzed using a unified, robust, accurate and experimentally-verifiable pipeline based on the FANSe3 mapping algorithm and edgeR for DGE analyzes. TranslatomeDB also allows users to upload their own datasets and utilize the identical unified pipeline to analyze their data. We believe that our TranslatomeDB is a comprehensive platform and knowledgebase on translatome and proteome research, releasing the biologists from complex searching, analyzing and comparing huge sequencing data without needing local computational power. PMID:29106630
Discriminative Prediction of A-To-I RNA Editing Events from DNA Sequence

PubMed Central

Sun, Jiangming; Singh, Pratibha; Bagge, Annika; Valtat, Bérengère; Vikman, Petter; Spégel, Peter; Mulder, Hindrik

2016-01-01

RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem. Using 41 nucleotide long DNA sequences, we show that novel A-to-I RNA editing events can be predicted from known A-to-I RNA editing events intra- and interspecies. The validity of the proposed method was verified in an independent experimental dataset. Using our approach, 203 202 putative A-to-I RNA editing events were predicted in the whole human genome. Out of these, 9% were previously reported. The remaining sites require further validation, e.g., by targeted deep sequencing. In conclusion, the approach described here is a useful tool to identify potential A-to-I RNA editing events without the requirement of extensive RNA sequencing. PMID:27764195
RNA inverse folding using Monte Carlo tree search.

PubMed

Yang, Xiufeng; Yoshizoe, Kazuki; Taneda, Akito; Tsuda, Koji

2017-11-06

Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases. We present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding. MCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA .
Polyester: simulating RNA-seq datasets with differential transcript expression.

PubMed

Frazee, Alyssa C; Jaffe, Andrew E; Langmead, Ben; Leek, Jeffrey T

2015-09-01

Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data. Polyester is an R package designed to simulate RNA-seq data, beginning with an experimental design and ending with collections of RNA-seq reads. Its main advantage is the ability to simulate reads indicating isoform-level differential expression across biological replicates for a variety of experimental designs. Data generated by Polyester is a reasonable approximation to real RNA-seq data and standard differential expression workflows can recover differential expression set in the simulation by the user. Polyester is freely available from Bioconductor (http://bioconductor.org/). jtleek@gmail.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Comparison of alternative approaches for analysing multi-level RNA-seq data

PubMed Central

Mohorianu, Irina; Bretman, Amanda; Smith, Damian T.; Fowler, Emily K.; Dalmay, Tamas

2017-01-01

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments. PMID:28792517
SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.

PubMed

Tsuchiya, Mariko; Amano, Kojiro; Abe, Masaya; Seki, Misato; Hase, Sumitaka; Sato, Kengo; Sakakibara, Yasubumi

2016-06-15

Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs. We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5'-end processing and 3'-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain. The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502. yasu@bio.keio.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data.

PubMed

Orenstein, Yaron; Wang, Yuhao; Berger, Bonnie

2016-06-15

Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset. We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale. Software and models are freely available at http://rck.csail.mit.edu/ bab@mit.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
The next generation of melanocyte data: Genetic, epigenetic, and transcriptional resource datasets and analysis tools.

PubMed

Loftus, Stacie K

2018-05-01

The number of melanocyte- and melanoma-derived next generation sequence genome-scale datasets have rapidly expanded over the past several years. This resource guide provides a summary of publicly available sources of melanocyte cell derived whole genome, exome, mRNA and miRNA transcriptome, chromatin accessibility and epigenetic datasets. Also highlighted are bioinformatic resources and tools for visualization and data queries which allow researchers a genome-scale view of the melanocyte. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Genome-Wide Analysis of A-to-I RNA Editing.

PubMed

Savva, Yiannis A; Laurent, Georges St; Reenan, Robert A

2016-01-01

Adenosine (A)-to-inosine (I) RNA editing is a fundamental posttranscriptional modification that ensures the deamination of A-to-I in double-stranded (ds) RNA molecules. Intriguingly, the A-to-I RNA editing system is particularly active in the nervous system of higher eukaryotes, altering a plethora of noncoding and coding sequences. Abnormal RNA editing is highly associated with many neurological phenotypes and neurodevelopmental disorders. However, the molecular mechanisms underlying RNA editing-mediated pathogenesis still remain enigmatic and have attracted increasing attention from researchers. Over the last decade, methods available to perform genome-wide transcriptome analysis, have evolved rapidly. Within the RNA editing field researchers have adopted next-generation sequencing technologies to identify RNA-editing sites within genomes and to elucidate the underlying process. However, technical challenges associated with editing site discovery have hindered efforts to uncover comprehensive editing site datasets, resulting in the general perception that the collections of annotated editing sites represent only a small minority of the total number of sites in a given organism, tissue, or cell type of interest. Additionally to doubts about sensitivity, existing RNA-editing site lists often contain high percentages of false positives, leading to uncertainty about their validity and usefulness in downstream studies. An accurate investigation of A-to-I editing requires properly validated datasets of editing sites with demonstrated and transparent levels of sensitivity and specificity. Here, we describe a high signal-to-noise method for RNA-editing site detection using single-molecule sequencing (SMS). With this method, authentic RNA-editing sites may be differentiated from artifacts. Machine learning approaches provide a procedure to improve upon and experimentally validate sequencing outcomes through use of computationally predicted, iterative feedback loops. Subsequent use of extensive Sanger sequencing validations can generate accurate editing site lists. This approach has broad application and accurate genome-wide editing analysis of various tissues from clinical specimens or various experimental organisms is now a possibility.
A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa

PubMed Central

Petegrosso, Raphael; Tolar, Jakub

2018-01-01

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC. PMID:29630593
Inaugural Genomics Automation Congress and the coming deluge of sequencing data.

PubMed

Creighton, Chad J

2010-10-01

Presentations at Select Biosciences's first 'Genomics Automation Congress' (Boston, MA, USA) in 2010 focused on next-generation sequencing and the platforms and methodology around them. The meeting provided an overview of sequencing technologies, both new and emerging. Speakers shared their recent work on applying sequencing to profile cells for various levels of biomolecular complexity, including DNA sequences, DNA copy, DNA methylation, mRNA and microRNA. With sequencing time and costs continuing to drop dramatically, a virtual explosion of very large sequencing datasets is at hand, which will probably present challenges and opportunities for high-level data analysis and interpretation, as well as for information technology infrastructure.
De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris

PubMed Central

Niu, Shan-Ce; Xu, Qing; Zhang, Guo-Qiang; Zhang, Yong-Qiang; Tsai, Wen-Chieh; Hsu, Jui-Ling; Liang, Chieh-Kai; Luo, Yi-Bo; Liu, Zhong-Jian

2016-01-01

Orchids are renowned for their spectacular flowers and ecological adaptations. After the sequencing of the genome of the tropical epiphytic orchid Phalaenopsis equestris, we combined Illumina HiSeq2000 for RNA-Seq and Trinity for de novo assembly to characterize the transcriptomes for 11 diverse P. equestris tissues representing the root, stem, leaf, flower buds, column, lip, petal, sepal and three developmental stages of seeds. Our aims were to contribute to a better understanding of the molecular mechanisms driving the analysed tissue characteristics and to enrich the available data for P. equestris. Here, we present three databases. The first dataset is the RNA-Seq raw reads, which can be used to execute new experiments with different analysis approaches. The other two datasets allow different types of searches for candidate homologues. The second dataset includes the sets of assembled unigenes and predicted coding sequences and proteins, enabling a sequence-based search. The third dataset consists of the annotation results of the aligned unigenes versus the Nonredundant (Nr) protein database, Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG) databases with low e-values, enabling a name-based search. PMID:27673730
Ultra Deep Sequencing of Listeria monocytogenes sRNA Transcriptome Revealed New Antisense RNAs

PubMed Central

Behrens, Sebastian; Widder, Stefanie; Mannala, Gopala Krishna; Qing, Xiaoxing; Madhugiri, Ramakanth; Kefer, Nathalie; Mraheil, Mobarak Abu; Rattei, Thomas; Hain, Torsten

2014-01-01

Listeria monocytogenes, a gram-positive pathogen, and causative agent of listeriosis, has become a widely used model organism for intracellular infections. Recent studies have identified small non-coding RNAs (sRNAs) as important factors for regulating gene expression and pathogenicity of L. monocytogenes. Increased speed and reduced costs of high throughput sequencing (HTS) techniques have made RNA sequencing (RNA-Seq) the state-of-the-art method to study bacterial transcriptomes. We created a large transcriptome dataset of L. monocytogenes containing a total of 21 million reads, using the SOLiD sequencing technology. The dataset contained cDNA sequences generated from L. monocytogenes RNA collected under intracellular and extracellular condition and additionally was size fractioned into three different size ranges from <40 nt, 40–150 nt and >150 nt. We report here, the identification of nine new sRNAs candidates of L. monocytogenes and a reevaluation of known sRNAs of L. monocytogenes EGD-e. Automatic comparison to known sRNAs revealed a high recovery rate of 55%, which was increased to 90% by manual revision of the data. Moreover, thorough classification of known sRNAs shed further light on their possible biological functions. Interestingly among the newly identified sRNA candidates are antisense RNAs (asRNAs) associated to the housekeeping genes purA, fumC and pgi and potentially their regulation, emphasizing the significance of sRNAs for metabolic adaptation in L. monocytogenes. PMID:24498259
fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets.

PubMed

Madrigal, Pedro

2017-03-01

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate reproducibility of biological or technical replicates, and to compare different datasets to identify their potential correlations. Here we present fCCAC, an application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We show how this method differs from other measures of correlation, and exemplify how it can reveal shared covariance between histone modifications and DNA binding proteins, such as the relationship between the H3K4me3 chromatin mark and its epigenetic writers and readers. An R/Bioconductor package is available at http://bioconductor.org/packages/fCCAC/ . pmb59@cam.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
DSAP: deep-sequencing small RNA analysis pipeline.

PubMed

Huang, Po-Jung; Liu, Yi-Chung; Lee, Chi-Ching; Lin, Wei-Chen; Gan, Richie Ruei-Chi; Lyu, Ping-Chiang; Tang, Petrus

2010-07-01

DSAP is an automated multiple-task web service designed to provide a total solution to analyzing deep-sequencing small RNA datasets generated by next-generation sequencing technology. DSAP uses a tab-delimited file as an input format, which holds the unique sequence reads (tags) and their corresponding number of copies generated by the Solexa sequencing platform. The input data will go through four analysis steps in DSAP: (i) cleanup: removal of adaptors and poly-A/T/C/G/N nucleotides; (ii) clustering: grouping of cleaned sequence tags into unique sequence clusters; (iii) non-coding RNA (ncRNA) matching: sequence homology mapping against a transcribed sequence library from the ncRNA database Rfam (http://rfam.sanger.ac.uk/); and (iv) known miRNA matching: detection of known miRNAs in miRBase (http://www.mirbase.org/) based on sequence homology. The expression levels corresponding to matched ncRNAs and miRNAs are summarized in multi-color clickable bar charts linked to external databases. DSAP is also capable of displaying miRNA expression levels from different jobs using a log(2)-scaled color matrix. Furthermore, a cross-species comparative function is also provided to show the distribution of identified miRNAs in different species as deposited in miRBase. DSAP is available at http://dsap.cgu.edu.tw.
Comprehensive discovery of noncoding RNAs in acute myeloid leukemia cell transcriptomes.

PubMed

Zhang, Jin; Griffith, Malachi; Miller, Christopher A; Griffith, Obi L; Spencer, David H; Walker, Jason R; Magrini, Vincent; McGrath, Sean D; Ly, Amy; Helton, Nichole M; Trissal, Maria; Link, Daniel C; Dang, Ha X; Larson, David E; Kulkarni, Shashikant; Cordes, Matthew G; Fronick, Catrina C; Fulton, Robert S; Klco, Jeffery M; Mardis, Elaine R; Ley, Timothy J; Wilson, Richard K; Maher, Christopher A

2017-11-01

To detect diverse and novel RNA species comprehensively, we compared deep small RNA and RNA sequencing (RNA-seq) methods applied to a primary acute myeloid leukemia (AML) sample. We were able to discover previously unannotated small RNAs using deep sequencing of a library method using broader insert size selection. We analyzed the long noncoding RNA (lncRNA) landscape in AML by comparing deep sequencing from multiple RNA-seq library construction methods for the sample that we studied and then integrating RNA-seq data from 179 AML cases. This identified lncRNAs that are completely novel, differentially expressed, and associated with specific AML subtypes. Our study revealed the complexity of the noncoding RNA transcriptome through a combined strategy of strand-specific small RNA and total RNA-seq. This dataset will serve as an invaluable resource for future RNA-based analyses. Copyright © 2017 ISEH – Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.
QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.

PubMed

Liu, Lian; Zhang, Shao-Wu; Huang, Yufei; Meng, Jia

2017-08-31

As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m 1 A-Seq, Par-CLIP, RIP-Seq, etc.
TranslatomeDB: a comprehensive database and cloud-based analysis platform for translatome sequencing data.

PubMed

Liu, Wanting; Xiang, Lunping; Zheng, Tingkai; Jin, Jingjie; Zhang, Gong

2018-01-04

Translation is a key regulatory step, linking transcriptome and proteome. Two major methods of translatome investigations are RNC-seq (sequencing of translating mRNA) and Ribo-seq (ribosome profiling). To facilitate the investigation of translation, we built a comprehensive database TranslatomeDB (http://www.translatomedb.net/) which provides collection and integrated analysis of published and user-generated translatome sequencing data. The current version includes 2453 Ribo-seq, 10 RNC-seq and their 1394 corresponding mRNA-seq datasets in 13 species. The database emphasizes the analysis functions in addition to the dataset collections. Differential gene expression (DGE) analysis can be performed between any two datasets of same species and type, both on transcriptome and translatome levels. The translation indices translation ratios, elongation velocity index and translational efficiency can be calculated to quantitatively evaluate translational initiation efficiency and elongation velocity, respectively. All datasets were analyzed using a unified, robust, accurate and experimentally-verifiable pipeline based on the FANSe3 mapping algorithm and edgeR for DGE analyzes. TranslatomeDB also allows users to upload their own datasets and utilize the identical unified pipeline to analyze their data. We believe that our TranslatomeDB is a comprehensive platform and knowledgebase on translatome and proteome research, releasing the biologists from complex searching, analyzing and comparing huge sequencing data without needing local computational power. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

PubMed Central

Sul, Woo Jun; Cole, James R.; Jesus, Ederson da C.; Wang, Qiong; Farris, Ryan J.; Fish, Jordan A.; Tiedje, James M.

2011-01-01

High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-of-magnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained ∼1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples. PMID:21873204
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

PubMed

Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2012-05-10

RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues. Our results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.
Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing

PubMed Central

2012-01-01

Background RNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods. Results Differential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices. Conclusions This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates. PMID:22985019
Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing.

PubMed

Robles, José A; Qureshi, Sumaira E; Stephen, Stuart J; Wilson, Susan R; Burden, Conrad J; Taylor, Jennifer M

2012-09-17

RNA sequencing (RNA-Seq) has emerged as a powerful approach for the detection of differential gene expression with both high-throughput and high resolution capabilities possible depending upon the experimental design chosen. Multiplex experimental designs are now readily available, these can be utilised to increase the numbers of samples or replicates profiled at the cost of decreased sequencing depth generated per sample. These strategies impact on the power of the approach to accurately identify differential expression. This study presents a detailed analysis of the power to detect differential expression in a range of scenarios including simulated null and differential expression distributions with varying numbers of biological or technical replicates, sequencing depths and analysis methods. Differential and non-differential expression datasets were simulated using a combination of negative binomial and exponential distributions derived from real RNA-Seq data. These datasets were used to evaluate the performance of three commonly used differential expression analysis algorithms and to quantify the changes in power with respect to true and false positive rates when simulating variations in sequencing depth, biological replication and multiplex experimental design choices. This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. We found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.
Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor.

PubMed

Crow, Megan; Paul, Anirban; Ballouz, Sara; Huang, Z Josh; Gillis, Jesse

2018-02-28

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. Meta-analysis is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. We first measure the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. We then apply this to novel interneuron subtypes, finding that 24/45 subtypes have evidence of replication, which enables the identification of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data.
Phylogeny of Kinorhyncha Based on Morphology and Two Molecular Loci

PubMed Central

Sørensen, Martin V.; Dal Zotto, Matteo; Rho, Hyun Soo; Herranz, Maria; Sánchez, Nuria; Pardos, Fernando; Yamasaki, Hiroshi

2015-01-01

The phylogeny of Kinorhyncha was analyzed using morphology and the molecular loci 18S rRNA and 28S rRNA. The different datasets were analyzed separately and in combination, using maximum likelihood and Bayesian Inference. Bayesian inference of molecular sequence data in combination with morphology supported the division of Kinorhyncha into two major clades: Cyclorhagida comb. nov. and Allomalorhagida nom. nov. The latter clade represents a new kinorhynch class, and accommodates Dracoderes, Franciscideres, a yet undescribed genus which is closely related with Franciscideres, and the traditional homalorhagid genera. Homalorhagid monophyly was not supported by any analyses with molecular sequence data included. Analysis of the combined molecular and morphological data furthermore supported a cyclorhagid clade which included all traditional cyclorhagid taxa, except Dracoderes that no longer should be considered a cyclorhagid genus. Accordingly, Cyclorhagida is divided into three main lineages: Echinoderidae, Campyloderidae, and a large clade, ‘Kentrorhagata’, which except for species of Campyloderes, includes all species with a midterminal spine present in adult individuals. Maximum likelihood analysis of the combined datasets produced a rather unresolved tree that was not regarded in the following discussion. Results of the analyses with only molecular sequence data included were incongruent at different points. However, common for all analyses was the support of several major clades, i.e., Campyloderidae, Kentrorhagata, Echinoderidae, Dracoderidae, Pycnophyidae, and a clade with Paracentrophyes + New Genus and Franciscideres (in those analyses where the latter was included). All molecular analyses including 18S rRNA sequence data furthermore supported monophyly of Allomalorhagida. Cyclorhagid monophyly was only supported in analyses of combined 18S rRNA and 28S rRNA (both ML and BI), and only in a restricted dataset where taxa with incomplete information from 28S rRNA had been omitted. Analysis of the morphological data produced results that were similar with those from the combined molecular and morphological analysis. E.g., the morphological data also supported exclusion of Dracoderes from Cyclorhagida. The main differences between the morphological analysis and analyses based on the combined datasets include: 1) Homalorhagida appears as monophyletic in the morphological tree only, 2) the morphological analyses position Franciscideres and the new genus within Cyclorhagida near Zelinkaderidae and Cateriidae, whereas analyses including molecular data place the two genera inside Allomalorhagida, and 3) species of Campyloderes appear in a basal trichotomy within Kentrorhagata in the morphological tree, whereas analysis of the combined datasets places species of Campyloderes as a sister clade to Echinoderidae and Kentrorhagata. PMID:26200115

ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data

PubMed Central

Krestel, Ralf; Ohler, Uwe; Vingron, Martin; Marsico, Annalisa

2017-01-01

Abstract RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM’s model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image. PMID:28977546
Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies

PubMed Central

Zhao, Shanrong; Prenger, Kurt; Smith, Lance

2013-01-01

RNA-Seq is becoming a promising replacement to microarrays in transcriptome profiling and differential gene expression study. Technical improvements have decreased sequencing costs and, as a result, the size and number of RNA-Seq datasets have increased rapidly. However, the increasing volume of data from large-scale RNA-Seq studies poses a practical challenge for data analysis in a local environment. To meet this challenge, we developed Stormbow, a cloud-based software package, to process large volumes of RNA-Seq data in parallel. The performance of Stormbow has been tested by practically applying it to analyse 178 RNA-Seq samples in the cloud. In our test, it took 6 to 8 hours to process an RNA-Seq sample with 100 million reads, and the average cost was $3.50 per sample. Utilizing Amazon Web Services as the infrastructure for Stormbow allows us to easily scale up to handle large datasets with on-demand computational resources. Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. Stormbow can be freely downloaded and can be used out of box to process Illumina RNA-Seq datasets. PMID:25937948
Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies.

PubMed

Zhao, Shanrong; Prenger, Kurt; Smith, Lance

2013-01-01

RNA-Seq is becoming a promising replacement to microarrays in transcriptome profiling and differential gene expression study. Technical improvements have decreased sequencing costs and, as a result, the size and number of RNA-Seq datasets have increased rapidly. However, the increasing volume of data from large-scale RNA-Seq studies poses a practical challenge for data analysis in a local environment. To meet this challenge, we developed Stormbow, a cloud-based software package, to process large volumes of RNA-Seq data in parallel. The performance of Stormbow has been tested by practically applying it to analyse 178 RNA-Seq samples in the cloud. In our test, it took 6 to 8 hours to process an RNA-Seq sample with 100 million reads, and the average cost was $3.50 per sample. Utilizing Amazon Web Services as the infrastructure for Stormbow allows us to easily scale up to handle large datasets with on-demand computational resources. Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. Stormbow can be freely downloaded and can be used out of box to process Illumina RNA-Seq datasets.
Identification of Habitat-Specific Biomes of Aquatic Fungal Communities Using a Comprehensive Nearly Full-Length 18S rRNA Dataset Enriched with Contextual Data

PubMed Central

Panzer, Katrin; Yilmaz, Pelin; Weiß, Michael; Reich, Lothar; Richter, Michael; Wiese, Jutta; Schmaljohann, Rolf; Labes, Antje; Imhoff, Johannes F.; Glöckner, Frank Oliver; Reich, Marlis

2015-01-01

Molecular diversity surveys have demonstrated that aquatic fungi are highly diverse, and that they play fundamental ecological roles in aquatic systems. Unfortunately, comparative studies of aquatic fungal communities are few and far between, due to the scarcity of adequate datasets. We combined all publicly available fungal 18S ribosomal RNA (rRNA) gene sequences with new sequence data from a marine fungi culture collection. We further enriched this dataset by adding validated contextual data. Specifically, we included data on the habitat type of the samples assigning fungal taxa to ten different habitat categories. This dataset has been created with the intention to serve as a valuable reference dataset for aquatic fungi including a phylogenetic reference tree. The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases. PMID:26226014
TriageTools: tools for partitioning and prioritizing analysis of high-throughput sequencing data.

PubMed

Fimereli, Danai; Detours, Vincent; Konopka, Tomasz

2013-04-01

High-throughput sequencing is becoming a popular research tool but carries with it considerable costs in terms of computation time, data storage and bandwidth. Meanwhile, some research applications focusing on individual genes or pathways do not necessitate processing of a full sequencing dataset. Thus, it is desirable to partition a large dataset into smaller, manageable, but relevant pieces. We present a toolkit for partitioning raw sequencing data that includes a method for extracting reads that are likely to map onto pre-defined regions of interest. We show the method can be used to extract information about genes of interest from DNA or RNA sequencing samples in a fraction of the time and disk space required to process and store a full dataset. We report speedup factors between 2.6 and 96, depending on settings and samples used. The software is available at http://www.sourceforge.net/projects/triagetools/.
RoboOligo: software for mass spectrometry data to support manual and de novo sequencing of post-transcriptionally modified ribonucleic acids

PubMed Central

Sample, Paul J.; Gaston, Kirk W.; Alfonzo, Juan D.; Limbach, Patrick A.

2015-01-01

Ribosomal ribonucleic acid (RNA), transfer RNA and other biological or synthetic RNA polymers can contain nucleotides that have been modified by the addition of chemical groups. Traditional Sanger sequencing methods cannot establish the chemical nature and sequence of these modified-nucleotide containing oligomers. Mass spectrometry (MS) has become the conventional approach for determining the nucleotide composition, modification status and sequence of modified RNAs. Modified RNAs are analyzed by MS using collision-induced dissociation tandem mass spectrometry (CID MS/MS), which produces a complex dataset of oligomeric fragments that must be interpreted to identify and place modified nucleosides within the RNA sequence. Here we report the development of RoboOligo, an interactive software program for the robust analysis of data generated by CID MS/MS of RNA oligomers. There are three main functions of RoboOligo: (i) automated de novo sequencing via the local search paradigm. (ii) Manual sequencing with real-time spectrum labeling and cumulative intensity scoring. (iii) A hybrid approach, coined ‘variable sequencing’, which combines the user intuition of manual sequencing with the high-throughput sampling of automated de novo sequencing. PMID:25820423
Complete genome sequencing of the luminescent bacterium, Vibrio qinghaiensis sp. Q67 using PacBio technology

NASA Astrophysics Data System (ADS)

Gong, Liang; Wu, Yu; Jian, Qijie; Yin, Chunxiao; Li, Taotao; Gupta, Vijai Kumar; Duan, Xuewu; Jiang, Yueming

2018-01-01

Vibrio qinghaiensis sp.-Q67 (Vqin-Q67) is a freshwater luminescent bacterium that continuously emits blue-green light (485 nm). The bacterium has been widely used for detecting toxic contaminants. Here, we report the complete genome sequence of Vqin-Q67, obtained using third-generation PacBio sequencing technology. Continuous long reads were attained from three PacBio sequencing runs and reads >500 bp with a quality value of >0.75 were merged together into a single dataset. This resultant highly-contiguous de novo assembly has no genome gaps, and comprises two chromosomes with substantial genetic information, including protein-coding genes, non-coding RNA, transposon and gene islands. Our dataset can be useful as a comparative genome for evolution and speciation studies, as well as for the analysis of protein-coding gene families, the pathogenicity of different Vibrio species in fish, the evolution of non-coding RNA and transposon, and the regulation of gene expression in relation to the bioluminescence of Vqin-Q67.
A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy.

PubMed

Gao, Xiang; Lin, Huaiying; Revanna, Kashi; Dong, Qunfeng

2017-05-10

Species-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement. We have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our method does not need any training datasets specific for different taxonomic groups. Instead only a reference database is required for aligning to the query sequences, making our method easily applicable for different regions of the 16S rRNA gene or other phylogenetic marker genes. Reliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA .
Transcriptogenomics identification and characterization of RNA editing sites in human primary monocytes using high-depth next generation sequencing data.

PubMed

Leong, Wai-Mun; Ripen, Adiratna Mat; Mirsafian, Hoda; Mohamad, Saharuddin Bin; Merican, Amir Feisal

2018-06-07

High-depth next generation sequencing data provide valuable insights into the number and distribution of RNA editing events. Here, we report the RNA editing events at cellular level of human primary monocyte using high-depth whole genomic and transcriptomic sequencing data. We identified over a ten thousand putative RNA editing sites and 69% of the sites were A-to-I editing sites. The sites enriched in repetitive sequences and intronic regions. High-depth sequencing datasets revealed that 90% of the canonical sites were edited at lower frequencies (<0.7). Single and multiple human monocytes and brain tissues samples were analyzed through genome sequence independent approach. The later approach was observed to identify more editing sites. Monocytes was observed to contain more C-to-U editing sites compared to brain tissues. Our results establish comparable pipeline that can address current limitations as well as demonstrate the potential for highly sensitive detection of RNA editing events in single cell type. Copyright © 2018 Elsevier Inc. All rights reserved.
Bellerophon: a program to detect chimeric sequences in multiple sequence alignments.

PubMed

Huber, Thomas; Faulkner, Geoffrey; Hugenholtz, Philip

2004-09-22

Bellerophon is a program for detecting chimeric sequences in multiple sequence datasets by an adaption of partial treeing analysis. Bellerophon was specifically developed to detect 16S rRNA gene chimeras in PCR-clone libraries of environmental samples but can be applied to other nucleotide sequence alignments. Bellerophon is available as an interactive web server at http://foo.maths.uq.edu.au/~huber/bellerophon.pl
Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, CY; Yang, H; Wei, CL

Tea is one of the most popular non-alcoholic beverages worldwide. However, the tea plant, Camellia sinensis, is difficult to culture in vitro, to transform, and has a large genome, rendering little genomic information available. Recent advances in large-scale RNA sequencing (RNA-seq) provide a fast, cost-effective, and reliable approach to generate large expression datasets for functional genomic analysis, which is especially suitable for non-model species with un-sequenced genomes. Using high-throughput Illumina RNA-seq, the transcriptome from poly (A){sup +} RNA of C. sinensis was analyzed at an unprecedented depth (2.59 gigabase pairs). Approximate 34.5 million reads were obtained, trimmed, and assembled intomore » 127,094 unigenes, with an average length of 355 bp and an N50 of 506 bp, which consisted of 788 contig clusters and 126,306 singletons. This number of unigenes was 10-fold higher than existing C. sinensis sequences deposited in GenBank (as of August 2010). Sequence similarity analyses against six public databases (Uniprot, NR and COGs at NCBI, Pfam, InterPro and KEGG) found 55,088 unigenes that could be annotated with gene descriptions, conserved protein domains, or gene ontology terms. Some of the unigenes were assigned to putative metabolic pathways. Targeted searches using these annotations identified the majority of genes associated with several primary metabolic pathways and natural product pathways that are important to tea quality, such as flavonoid, theanine and caffeine biosynthesis pathways. Novel candidate genes of these secondary pathways were discovered. Comparisons with four previously prepared cDNA libraries revealed that this transcriptome dataset has both a high degree of consistency with previous EST data and an approximate 20 times increase in coverage. Thirteen unigenes related to theanine and flavonoid synthesis were validated. Their expression patterns in different organs of the tea plant were analyzed by RT-PCR and quantitative real time PCR (qRT-PCR). An extensive transcriptome dataset has been obtained from the deep sequencing of tea plant. The coverage of the transcriptome is comprehensive enough to discover all known genes of several major metabolic pathways. This transcriptome dataset can serve as an important public information platform for gene expression, genomics, and functional genomic studies in C. sinensis.« less
Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

PubMed Central

2011-01-01

Background Tea is one of the most popular non-alcoholic beverages worldwide. However, the tea plant, Camellia sinensis, is difficult to culture in vitro, to transform, and has a large genome, rendering little genomic information available. Recent advances in large-scale RNA sequencing (RNA-seq) provide a fast, cost-effective, and reliable approach to generate large expression datasets for functional genomic analysis, which is especially suitable for non-model species with un-sequenced genomes. Results Using high-throughput Illumina RNA-seq, the transcriptome from poly (A)+ RNA of C. sinensis was analyzed at an unprecedented depth (2.59 gigabase pairs). Approximate 34.5 million reads were obtained, trimmed, and assembled into 127,094 unigenes, with an average length of 355 bp and an N50 of 506 bp, which consisted of 788 contig clusters and 126,306 singletons. This number of unigenes was 10-fold higher than existing C. sinensis sequences deposited in GenBank (as of August 2010). Sequence similarity analyses against six public databases (Uniprot, NR and COGs at NCBI, Pfam, InterPro and KEGG) found 55,088 unigenes that could be annotated with gene descriptions, conserved protein domains, or gene ontology terms. Some of the unigenes were assigned to putative metabolic pathways. Targeted searches using these annotations identified the majority of genes associated with several primary metabolic pathways and natural product pathways that are important to tea quality, such as flavonoid, theanine and caffeine biosynthesis pathways. Novel candidate genes of these secondary pathways were discovered. Comparisons with four previously prepared cDNA libraries revealed that this transcriptome dataset has both a high degree of consistency with previous EST data and an approximate 20 times increase in coverage. Thirteen unigenes related to theanine and flavonoid synthesis were validated. Their expression patterns in different organs of the tea plant were analyzed by RT-PCR and quantitative real time PCR (qRT-PCR). Conclusions An extensive transcriptome dataset has been obtained from the deep sequencing of tea plant. The coverage of the transcriptome is comprehensive enough to discover all known genes of several major metabolic pathways. This transcriptome dataset can serve as an important public information platform for gene expression, genomics, and functional genomic studies in C. sinensis. PMID:21356090
ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data

PubMed Central

Li, You; Heavican, Tayla B.; Vellichirammal, Neetha N.; Iqbal, Javeed

2017-01-01

Abstract The RNA-Seq technology has revolutionized transcriptome characterization not only by accurately quantifying gene expression, but also by the identification of novel transcripts like chimeric fusion transcripts. The ‘fusion’ or ‘chimeric’ transcripts have improved the diagnosis and prognosis of several tumors, and have led to the development of novel therapeutic regimen. The fusion transcript detection is currently accomplished by several software packages, primarily relying on sequence alignment algorithms. The alignment of sequencing reads from fusion transcript loci in cancer genomes can be highly challenging due to the incorrect mapping induced by genomic alterations, thereby limiting the performance of alignment-based fusion transcript detection methods. Here, we developed a novel alignment-free method, ChimeRScope that accurately predicts fusion transcripts based on the gene fingerprint (as k-mers) profiles of the RNA-Seq paired-end reads. Results on published datasets and in-house cancer cell line datasets followed by experimental validations demonstrate that ChimeRScope consistently outperforms other popular methods irrespective of the read lengths and sequencing depth. More importantly, results on our in-house datasets show that ChimeRScope is a better tool that is capable of identifying novel fusion transcripts with potential oncogenic functions. ChimeRScope is accessible as a standalone software at (https://github.com/ChimeRScope/ChimeRScope/wiki) or via the Galaxy web-interface at (https://galaxy.unmc.edu/). PMID:28472320
lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

PubMed

Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

2015-01-01

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.
Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

PubMed

Wrzeszczynski, Kazimierz O; Frank, Mayu O; Koyama, Takahiko; Rhrissorrakrai, Kahn; Robine, Nicolas; Utro, Filippo; Emde, Anne-Katrin; Chen, Bo-Juen; Arora, Kanika; Shah, Minita; Vacic, Vladimir; Norel, Raquel; Bilal, Erhan; Bergmann, Ewa A; Moore Vogel, Julia L; Bruce, Jeffrey N; Lassman, Andrew B; Canoll, Peter; Grommes, Christian; Harvey, Steve; Parida, Laxmi; Michelini, Vanessa V; Zody, Michael C; Jobanputra, Vaidehi; Royyuru, Ajay K; Darnell, Robert B

2017-08-01

To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. NCT02725684.
High-Throughput Single-Cell RNA Sequencing and Data Analysis.

PubMed

Sagar; Herman, Josip Stefan; Pospisilik, John Andrew; Grün, Dominic

2018-01-01

Understanding biological systems at a single cell resolution may reveal several novel insights which remain masked by the conventional population-based techniques providing an average readout of the behavior of cells. Single-cell transcriptome sequencing holds the potential to identify novel cell types and characterize the cellular composition of any organ or tissue in health and disease. Here, we describe a customized high-throughput protocol for single-cell RNA-sequencing (scRNA-seq) combining flow cytometry and a nanoliter-scale robotic system. Since scRNA-seq requires amplification of a low amount of endogenous cellular RNA, leading to substantial technical noise in the dataset, downstream data filtering and analysis require special care. Therefore, we also briefly describe in-house state-of-the-art data analysis algorithms developed to identify cellular subpopulations including rare cell types as well as to derive lineage trees by ordering the identified subpopulations of cells along the inferred differentiation trajectories.
Estimating Bacterial Diversity for Ecological Studies: Methods, Metrics, and Assumptions

PubMed Central

Birtel, Julia; Walser, Jean-Claude; Pichon, Samuel; Bürgmann, Helmut; Matthews, Blake

2015-01-01

Methods to estimate microbial diversity have developed rapidly in an effort to understand the distribution and diversity of microorganisms in natural environments. For bacterial communities, the 16S rRNA gene is the phylogenetic marker gene of choice, but most studies select only a specific region of the 16S rRNA to estimate bacterial diversity. Whereas biases derived from from DNA extraction, primer choice and PCR amplification are well documented, we here address how the choice of variable region can influence a wide range of standard ecological metrics, such as species richness, phylogenetic diversity, β-diversity and rank-abundance distributions. We have used Illumina paired-end sequencing to estimate the bacterial diversity of 20 natural lakes across Switzerland derived from three trimmed variable 16S rRNA regions (V3, V4, V5). Species richness, phylogenetic diversity, community composition, β-diversity, and rank-abundance distributions differed significantly between 16S rRNA regions. Overall, patterns of diversity quantified by the V3 and V5 regions were more similar to one another than those assessed by the V4 region. Similar results were obtained when analyzing the datasets with different sequence similarity thresholds used during sequences clustering and when the same analysis was used on a reference dataset of sequences from the Greengenes database. In addition we also measured species richness from the same lake samples using ARISA Fingerprinting, but did not find a strong relationship between species richness estimated by Illumina and ARISA. We conclude that the selection of 16S rRNA region significantly influences the estimation of bacterial diversity and species distributions and that caution is warranted when comparing data from different variable regions as well as when using different sequencing techniques. PMID:25915756
Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods.

PubMed

Dal Molin, Alessandra; Baruzzo, Giacomo; Di Camillo, Barbara

2017-01-01

The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results.
Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest.

PubMed

Du, Xiuquan; Hu, Changlin; Yao, Yu; Sun, Shiwei; Zhang, Yanping

2017-12-12

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features.
Beta-Poisson model for single-cell RNA-seq data analyses.

PubMed

Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Rantalainen, Mattias; Pawitan, Yudi

2016-07-15

Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Metaxa: a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets.

PubMed

Bengtsson, Johan; Eriksson, K Martin; Hartmann, Martin; Wang, Zheng; Shenoy, Belle Damodara; Grelet, Gwen-Aëlle; Abarenkov, Kessy; Petri, Anna; Rosenblad, Magnus Alm; Nilsson, R Henrik

2011-10-01

The ribosomal small subunit (SSU) rRNA gene has emerged as an important genetic marker for taxonomic identification in environmental sequencing datasets. In addition to being present in the nucleus of eukaryotes and the core genome of prokaryotes, the gene is also found in the mitochondria of eukaryotes and in the chloroplasts of photosynthetic eukaryotes. These three sets of genes are conceptually paralogous and should in most situations not be aligned and analyzed jointly. To identify the origin of SSU sequences in complex sequence datasets has hitherto been a time-consuming and largely manual undertaking. However, the present study introduces Metaxa ( http://microbiology.se/software/metaxa/ ), an automated software tool to extract full-length and partial SSU sequences from larger sequence datasets and assign them to an archaeal, bacterial, nuclear eukaryote, mitochondrial, or chloroplast origin. Using data from reference databases and from full-length organelle and organism genomes, we show that Metaxa detects and scores SSU sequences for origin with very low proportions of false positives and negatives. We believe that this tool will be useful in microbial and evolutionary ecology as well as in metagenomics.
A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets.

PubMed

Koren, Omry; Knights, Dan; Gonzalez, Antonio; Waldron, Levi; Segata, Nicola; Knight, Rob; Huttenhower, Curtis; Ley, Ruth E

2013-01-01

Recent analyses of human-associated bacterial diversity have categorized individuals into 'enterotypes' or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.
A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets

PubMed Central

Waldron, Levi; Segata, Nicola; Knight, Rob; Huttenhower, Curtis; Ley, Ruth E.

2013-01-01

Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes. PMID:23326225
From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data.

PubMed

Bottini, Silvia; Hamouda-Tekaya, Nedra; Tanasa, Bogdan; Zaragosi, Laure-Emmanuelle; Grandjean, Valerie; Repetto, Emanuela; Trabucchi, Michele

2017-05-19

Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells. We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data

PubMed Central

Bottini, Silvia; Hamouda-Tekaya, Nedra; Tanasa, Bogdan; Zaragosi, Laure-Emmanuelle; Grandjean, Valerie; Repetto, Emanuela

2017-01-01

Abstract Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked ImmunoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells. We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification. PMID:28108660
GeNets: a unified web platform for network-based genomic analyses.

PubMed

Li, Taibo; Kim, April; Rosenbluh, Joseph; Horn, Heiko; Greenfeld, Liraz; An, David; Zimmer, Andrew; Liberzon, Arthur; Bistline, Jon; Natoli, Ted; Li, Yang; Tsherniak, Aviad; Narayan, Rajiv; Subramanian, Aravind; Liefeld, Ted; Wong, Bang; Thompson, Dawn; Calvo, Sarah; Carr, Steve; Boehm, Jesse; Jaffe, Jake; Mesirov, Jill; Hacohen, Nir; Regev, Aviv; Lage, Kasper

2018-06-18

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.
Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells.

PubMed

Kwon, ChangHyuk; Tak, Hyosun; Rho, Mina; Chang, Hae Ryung; Kim, Yon Hui; Kim, Kyung Tae; Balch, Curt; Lee, Eun Kyung; Nam, Seungyoon

2014-03-28

Piwi-interacting RNAs (piRNAs) are 26-31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
Whole transcriptome analysis of the poultry red mite Dermanyssus gallinae (De Geer, 1778).

PubMed

Schicht, Sabine; Qi, Weihong; Poveda, Lucy; Strube, Christina

2014-03-01

SUMMARY Although the poultry red mite Dermanyssus gallinae (De Geer, 1778) is the major parasitic pest in poultry farming causing substantial economic losses every year, nucleotide data are rare in the public databases. Therefore, de novo sequencing covering the transcriptome of D. gallinae was carried out resulting in a dataset of 232 097 singletons and 42 130 contiguous sequences (contigs) which were subsequently clustered into 24 140 isogroups consisting of 35 788 isotigs. After removal of sequences possibly originating from bacteria or the chicken host, 267 464 sequences (231 657 singletons, 56 contigs and 35 751 isotigs) remained, of which 10·3% showed homology to proteins derived from other organisms. The most significant Blast top-hit species was the mite Metaseiulus occidentalis followed by the tick Ixodes scapularis. To gain functional knowledge of D. gallinae transcripts, sequences were mapped to Gene Ontology terms, Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways and parsed to InterProScan. The transcriptome dataset provides new insights in general mite genetics and lays a foundation for future studies on stage-specific transcriptomics as well as genomic, proteomic, and metabolomic explorations and might provide new perspectives to control this parasitic mite by identifying possible drug targets or vaccine candidates. It is also worth noting that in different tested species of the class Arachnida no 28S rRNA was detectable in the rRNA profile, indicating that 28S rRNA might consists of two separate, hydrogen-bonded fragments, whose (heat-induced) disruption may led to co-migration with 18S rRNA.
Isoform-level gene expression patterns in single-cell RNA-sequencing data.

PubMed

Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Pawitan, Yudi; Rantalainen, Mattias

2018-02-27

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16,562 isoform-pairs from 4,929 genes. Among those, 26% of the discovered patterns were significant (p<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. The effect of drop-out events, mean expression level, and properties of the expression distribution on the performances of ISOP were also investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoformlevel preference, commitment and heterogeneity in single-cell RNA-sequencing data. The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. mattias.rantalainen@ki.se. Supplementary data are available at Bioinformatics online.
Total Extracellular Small RNA Profiles from Plasma, Saliva, and Urine of Healthy Subjects

PubMed Central

Yeri, Ashish; Courtright, Amanda; Reiman, Rebecca; Carlson, Elizabeth; Beecroft, Taylor; Janss, Alex; Siniard, Ashley; Richholt, Ryan; Balak, Chris; Rozowsky, Joel; Kitchen, Robert; Hutchins, Elizabeth; Winarta, Joseph; McCoy, Roger; Anastasi, Matthew; Kim, Seungchan; Huentelman, Matthew; Van Keuren-Jensen, Kendall

2017-01-01

Interest in circulating RNAs for monitoring and diagnosing human health has grown significantly. There are few datasets describing baseline expression levels for total cell-free circulating RNA from healthy control subjects. In this study, total extracellular RNA (exRNA) was isolated and sequenced from 183 plasma samples, 204 urine samples and 46 saliva samples from 55 male college athletes ages 18–25 years. Many participants provided more than one sample, allowing us to investigate variability in an individual’s exRNA expression levels over time. Here we provide a systematic analysis of small exRNAs present in each biofluid, as well as an analysis of exogenous RNAs. The small RNA profile of each biofluid is distinct. We find that a large number of RNA fragments in plasma (63%) and urine (54%) have sequences that are assigned to YRNA and tRNA fragments respectively. Surprisingly, while many miRNAs can be detected, there are few miRNAs that are consistently detected in all samples from a single biofluid, and profiles of miRNA are different for each biofluid. Not unexpectedly, saliva samples have high levels of exogenous sequence that can be traced to bacteria. These data significantly contribute to the current number of sequenced exRNA samples from normal healthy individuals. PMID:28303895
Modeling bias and variation in the stochastic processes of small RNA sequencing

PubMed Central

Etheridge, Alton; Sakhanenko, Nikita; Galas, David

2017-01-01

Abstract The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data. PMID:28369495
Computational analysis of ribonomics datasets identifies long non-coding RNA targets of γ-herpesviral miRNAs.

PubMed

Sethuraman, Sunantha; Thomas, Merin; Gay, Lauren A; Renne, Rolf

2018-05-29

Ribonomics experiments involving crosslinking and immuno-precipitation (CLIP) of Ago proteins have expanded the understanding of the miRNA targetome of several organisms. These techniques, collectively referred to as CLIP-seq, have been applied to identifying the mRNA targets of miRNAs expressed by Kaposi's Sarcoma-associated herpes virus (KSHV) and Epstein-Barr virus (EBV). However, these studies focused on identifying only those RNA targets of KSHV and EBV miRNAs that are known to encode proteins. Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are also targeted by miRNAs. In this study, we performed a systematic re-analysis of published datasets from KSHV- and EBV-driven cancers. We used CLIP-seq data from lymphoma cells or EBV-transformed B cells, and a crosslinking, ligation and sequencing of hybrids dataset from KSHV-infected endothelial cells, to identify novel lncRNA targets of viral miRNAs. Here, we catalog the lncRNA targetome of KSHV and EBV miRNAs, and provide a detailed in silico analysis of lncRNA-miRNA binding interactions. Viral miRNAs target several hundred lncRNAs, including a subset previously shown to be aberrantly expressed in human malignancies. In addition, we identified thousands of lncRNAs to be putative targets of human miRNAs, suggesting that miRNA-lncRNA interactions broadly contribute to the regulation of gene expression.
Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies.

PubMed

Thorsen, Jonathan; Brejnrod, Asker; Mortensen, Martin; Rasmussen, Morten A; Stokholm, Jakob; Al-Soud, Waleed Abu; Sørensen, Søren; Bisgaard, Hans; Waage, Johannes

2016-11-25

There is an immense scientific interest in the human microbiome and its effects on human physiology, health, and disease. A common approach for examining bacterial communities is high-throughput sequencing of 16S rRNA gene hypervariable regions, aggregating sequence-similar amplicons into operational taxonomic units (OTUs). Strategies for detecting differential relative abundance of OTUs between sample conditions include classical statistical approaches as well as a plethora of newer methods, many borrowing from the related field of RNA-seq analysis. This effort is complicated by unique data characteristics, including sparsity, sequencing depth variation, and nonconformity of read counts to theoretical distributions, which is often exacerbated by exploratory and/or unbalanced study designs. Here, we assess the robustness of available methods for (1) inference in differential relative abundance analysis and (2) beta-diversity-based sample separation, using a rigorous benchmarking framework based on large clinical 16S microbiome datasets from different sources. Running more than 380,000 full differential relative abundance tests on real datasets with permuted case/control assignments and in silico-spiked OTUs, we identify large differences in method performance on a range of parameters, including false positive rates, sensitivity to sparsity and case/control balances, and spike-in retrieval rate. In large datasets, methods with the highest false positive rates also tend to have the best detection power. For beta-diversity-based sample separation, we show that library size normalization has very little effect and that the distance metric is the most important factor in terms of separation power. Our results, generalizable to datasets from different sequencing platforms, demonstrate how the choice of method considerably affects analysis outcome. Here, we give recommendations for tools that exhibit low false positive rates, have good retrieval power across effect sizes and case/control proportions, and have low sparsity bias. Result output from some commonly used methods should be interpreted with caution. We provide an easily extensible framework for benchmarking of new methods and future microbiome datasets.
YM500v2: a small RNA sequencing (smRNA-seq) database for human cancer miRNome research

PubMed Central

Cheng, Wei-Chung; Chung, I-Fang; Tsai, Cheng-Fong; Huang, Tse-Shun; Chen, Chen-Yang; Wang, Shao-Chuan; Chang, Ting-Yu; Sun, Hsing-Jen; Chao, Jeffrey Yung-Chuan; Cheng, Cheng-Chung; Wu, Cheng-Wen; Wang, Hsei-Wei

2015-01-01

We previously presented YM500, which is an integrated database for miRNA quantification, isomiR identification, arm switching discovery and novel miRNA prediction from 468 human smRNA-seq datasets. Here in this updated YM500v2 database (http://ngs.ym.edu.tw/ym500/), we focus on the cancer miRNome to make the database more disease-orientated. New miRNA-related algorithms developed after YM500 were included in YM500v2, and, more significantly, more than 8000 cancer-related smRNA-seq datasets (including those of primary tumors, paired normal tissues, PBMC, recurrent tumors, and metastatic tumors) were incorporated into YM500v2. Novel miRNAs (miRNAs not included in the miRBase R21) were not only predicted by three independent algorithms but also cleaned by a new in silico filtration strategy and validated by wetlab data such as Cross-Linked ImmunoPrecipitation sequencing (CLIP-seq) to reduce the false-positive rate. A new function ‘Meta-analysis’ is additionally provided for allowing users to identify real-time differentially expressed miRNAs and arm-switching events according to customer-defined sample groups and dozens of clinical criteria tidying up by proficient clinicians. Cancer miRNAs identified hold the potential for both basic research and biotech applications. PMID:25398902
RNA Framework: an all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications.

PubMed

Incarnato, Danny; Morandi, Edoardo; Simon, Lisa Marie; Oliviero, Salvatore

2018-06-09

RNA is emerging as a key regulator of a plethora of biological processes. While its study has remained elusive for decades, the recent advent of high-throughput sequencing technologies provided the unique opportunity to develop novel techniques for the study of RNA structure and post-transcriptional modifications. Nonetheless, most of the required downstream bioinformatics analyses steps are not easily reproducible, thus making the application of these techniques a prerogative of few laboratories. Here we introduce RNA Framework, an all-in-one toolkit for the analysis of most NGS-based RNA structure probing and post-transcriptional modification mapping experiments. To prove the extreme versatility of RNA Framework, we applied it to both an in-house generated DMS-MaPseq dataset, and to a series of literature available experiments. Notably, when starting from publicly available datasets, our software easily allows replicating authors' findings. Collectively, RNA Framework provides the most complete and versatile toolkit to date for a rapid and streamlined analysis of the RNA epistructurome. RNA Framework is available for download at: http://www.rnaframework.com.
Leveraging genome-wide datasets to quantify the functional role of the anti-Shine-Dalgarno sequence in regulating translation efficiency.

PubMed

Hockenberry, Adam J; Pah, Adam R; Jewett, Michael C; Amaral, Luís A N

2017-01-01

Studies dating back to the 1970s established that sequence complementarity between the anti-Shine-Dalgarno (aSD) sequence on prokaryotic ribosomes and the 5' untranslated region of mRNAs helps to facilitate translation initiation. The optimal location of aSD sequence binding relative to the start codon, the full extents of the aSD sequence and the functional form of the relationship between aSD sequence complementarity and translation efficiency have not been fully resolved. Here, we investigate these relationships by leveraging the sequence diversity of endogenous genes and recently available genome-wide estimates of translation efficiency. We show that-after accounting for predicted mRNA structure-aSD sequence complementarity increases the translation of endogenous mRNAs by roughly 50%. Further, we observe that this relationship is nonlinear, with translation efficiency maximized for mRNAs with intermediate levels of aSD sequence complementarity. The mechanistic insights that we observe are highly robust: we find nearly identical results in multiple datasets spanning three distantly related bacteria. Further, we verify our main conclusions by re-analysing a controlled experimental dataset. © 2017 The Authors.
YM500: a small RNA sequencing (smRNA-seq) database for microRNA research

PubMed Central

Cheng, Wei-Chung; Chung, I-Fang; Huang, Tse-Shun; Chang, Shih-Ting; Sun, Hsing-Jen; Tsai, Cheng-Fong; Liang, Muh-Lii; Wong, Tai-Tong; Wang, Hsei-Wei

2013-01-01

MicroRNAs (miRNAs) are small RNAs ∼22 nt in length that are involved in the regulation of a variety of physiological and pathological processes. Advances in high-throughput small RNA sequencing (smRNA-seq), one of the next-generation sequencing applications, have reshaped the miRNA research landscape. In this study, we established an integrative database, the YM500 (http://ngs.ym.edu.tw/ym500/), containing analysis pipelines and analysis results for 609 human and mice smRNA-seq results, including public data from the Gene Expression Omnibus (GEO) and some private sources. YM500 collects analysis results for miRNA quantification, for isomiR identification (incl. RNA editing), for arm switching discovery, and, more importantly, for novel miRNA predictions. Wetlab validation on >100 miRNAs confirmed high correlation between miRNA profiling and RT-qPCR results (R = 0.84). This database allows researchers to search these four different types of analysis results via our interactive web interface. YM500 allows researchers to define the criteria of isomiRs, and also integrates the information of dbSNP to help researchers distinguish isomiRs from SNPs. A user-friendly interface is provided to integrate miRNA-related information and existing evidence from hundreds of sequencing datasets. The identified novel miRNAs and isomiRs hold the potential for both basic research and biotech applications. PMID:23203880
Identification of fungi in shotgun metagenomics datasets

PubMed Central

Donovan, Paul D.; Gonzalez, Gabriel; Higgins, Desmond G.

2018-01-01

Metagenomics uses nucleic acid sequencing to characterize species diversity in different niches such as environmental biomes or the human microbiome. Most studies have used 16S rRNA amplicon sequencing to identify bacteria. However, the decreasing cost of sequencing has resulted in a gradual shift away from amplicon analyses and towards shotgun metagenomic sequencing. Shotgun metagenomic data can be used to identify a wide range of species, but have rarely been applied to fungal identification. Here, we develop a sequence classification pipeline, FindFungi, and use it to identify fungal sequences in public metagenome datasets. We focus primarily on animal metagenomes, especially those from pig and mouse microbiomes. We identified fungi in 39 of 70 datasets comprising 71 fungal species. At least 11 pathogenic species with zoonotic potential were identified, including Candida tropicalis. We identified Pseudogymnoascus species from 13 Antarctic soil samples initially analyzed for the presence of bacteria capable of degrading diesel oil. We also show that Candida tropicalis and Candida loboi are likely the same species. In addition, we identify several examples where contaminating DNA was erroneously included in fungal genome assemblies. PMID:29444186
The Physcomitrella patens gene atlas project: large-scale RNA-seq based expression data.

PubMed

Perroud, Pierre-François; Haas, Fabian B; Hiss, Manuel; Ullrich, Kristian K; Alboresi, Alessandro; Amirebrahimi, Mojgan; Barry, Kerrie; Bassi, Roberto; Bonhomme, Sandrine; Chen, Haodong; Coates, Juliet C; Fujita, Tomomichi; Guyon-Debast, Anouchka; Lang, Daniel; Lin, Junyan; Lipzen, Anna; Nogué, Fabien; Oliver, Melvin J; Ponce de León, Inés; Quatrano, Ralph S; Rameau, Catherine; Reiss, Bernd; Reski, Ralf; Ricca, Mariana; Saidi, Younousse; Sun, Ning; Szövényi, Péter; Sreedasyam, Avinash; Grimwood, Jane; Stacey, Gary; Schmutz, Jeremy; Rensing, Stefan A

2018-07-01

High-throughput RNA sequencing (RNA-seq) has recently become the method of choice to define and analyze transcriptomes. For the model moss Physcomitrella patens, although this method has been used to help analyze specific perturbations, no overall reference dataset has yet been established. In the framework of the Gene Atlas project, the Joint Genome Institute selected P. patens as a flagship genome, opening the way to generate the first comprehensive transcriptome dataset for this moss. The first round of sequencing described here is composed of 99 independent libraries spanning 34 different developmental stages and conditions. Upon dataset quality control and processing through read mapping, 28 509 of the 34 361 v3.3 gene models (83%) were detected to be expressed across the samples. Differentially expressed genes (DEGs) were calculated across the dataset to permit perturbation comparisons between conditions. The analysis of the three most distinct and abundant P. patens growth stages - protonema, gametophore and sporophyte - allowed us to define both general transcriptional patterns and stage-specific transcripts. As an example of variation of physico-chemical growth conditions, we detail here the impact of ammonium supplementation under standard growth conditions on the protonemal transcriptome. Finally, the cooperative nature of this project allowed us to analyze inter-laboratory variation, as 13 different laboratories around the world provided samples. We compare differences in the replication of experiments in a single laboratory and between different laboratories. © 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.
Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma

PubMed Central

Wrzeszczynski, Kazimierz O.; Frank, Mayu O.; Koyama, Takahiko; Rhrissorrakrai, Kahn; Robine, Nicolas; Utro, Filippo; Emde, Anne-Katrin; Chen, Bo-Juen; Arora, Kanika; Shah, Minita; Vacic, Vladimir; Norel, Raquel; Bilal, Erhan; Bergmann, Ewa A.; Moore Vogel, Julia L.; Bruce, Jeffrey N.; Lassman, Andrew B.; Canoll, Peter; Grommes, Christian; Harvey, Steve; Parida, Laxmi; Michelini, Vanessa V.; Zody, Michael C.; Jobanputra, Vaidehi; Royyuru, Ajay K.

2017-01-01

Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. ClinicalTrials.gov identifier: NCT02725684. PMID:28740869

The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4).

PubMed

Huntemann, Marcel; Ivanova, Natalia N; Mavromatis, Konstantinos; Tripp, H James; Paez-Espino, David; Palaniappan, Krishnaveni; Szeto, Ernest; Pillay, Manoj; Chen, I-Min A; Pati, Amrita; Nielsen, Torben; Markowitz, Victor M; Kyrpides, Nikos C

2015-01-01

The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.
PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens.

PubMed

Spahn, Philipp N; Bath, Tyler; Weiss, Ryan J; Kim, Jihoon; Esko, Jeffrey D; Lewis, Nathan E; Harismendy, Olivier

2017-11-20

Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets.
MetaMetaDB: a database and analytic system for investigating microbial habitability.

PubMed

Yang, Ching-chia; Iwasaki, Wataru

2014-01-01

MetaMetaDB (http://mmdb.aori.u-tokyo.ac.jp/) is a database and analytic system for investigating microbial habitability, i.e., how a prokaryotic group can inhabit different environments. The interaction between prokaryotes and the environment is a key issue in microbiology because distinct prokaryotic communities maintain distinct ecosystems. Because 16S ribosomal RNA (rRNA) sequences play pivotal roles in identifying prokaryotic species, a system that comprehensively links diverse environments to 16S rRNA sequences of the inhabitant prokaryotes is necessary for the systematic understanding of the microbial habitability. However, existing databases are biased to culturable prokaryotes and exhibit limitations in the comprehensiveness of the data because most prokaryotes are unculturable. Recently, metagenomic and 16S rRNA amplicon sequencing approaches have generated abundant 16S rRNA sequence data that encompass unculturable prokaryotes across diverse environments; however, these data are usually buried in large databases and are difficult to access. In this study, we developed MetaMetaDB (Meta-Metagenomic DataBase), which comprehensively and compactly covers 16S rRNA sequences retrieved from public datasets. Using MetaMetaDB, users can quickly generate hypotheses regarding the types of environments a prokaryotic group may be adapted to. We anticipate that MetaMetaDB will improve our understanding of the diversity and evolution of prokaryotes.
PARRoT- a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms.

PubMed

Gan, Ruei-Chi; Chen, Ting-Wen; Wu, Timothy H; Huang, Po-Jung; Lee, Chi-Ching; Yeh, Yuan-Ming; Chiu, Cheng-Hsun; Huang, Hsien-Da; Tang, Petrus

2016-12-22

Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw .
Single-cell Transcriptome Study as Big Data

PubMed Central

Yu, Pingjian; Lin, Wei

2016-01-01

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. PMID:26876720
Statistical modeling of isoform splicing dynamics from RNA-seq time series data.

PubMed

Huang, Yuanhua; Sanguinetti, Guido

2016-10-01

Isoform quantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification. Here, we present DICEseq, the first isoform quantification method tailored to correlated RNA-seq experiments. DICEseq explicitly models the correlations between different RNA-seq experiments to aid the quantification of isoforms across experiments. Numerical experiments on simulated datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. On real datasets, our results show that DICEseq provides substantially more reproducible and robust quantifications, increasing the correlation of estimates from replicate datasets by up to 10% on genes with low or moderate expression levels (bottom third of all genes). Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Our results have strong implications for the design of RNA-seq experiments, and offer a novel tool for improved analysis of such datasets. Python code is freely available at http://diceseq.sf.net G.Sanguinetti@ed.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Evaluation of experimental design and computational parameter choices affecting analyses of ChIP-seq and RNA-seq data in undomesticated poplar trees.

Treesearch

Lijun Liu; V. Missirian; Matthew S. Zinkgraf; Andrew Groover; V. Filkov

2014-01-01

Background: One of the great advantages of next generation sequencing is the ability to generate large genomic datasets for virtually all species, including non-model organisms. It should be possible, in turn, to apply advanced computational approaches to these datasets to develop models of biological processes. In a practical sense, working with non-model organisms...
The Landscape of long non-coding RNA classification

PubMed Central

St Laurent, Georges; Wahlestedt, Claes; Kapranov, Philipp

2015-01-01

Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long non-coding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the non-coding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual un-ambiguous classification framework results in a number of challenges in the annotation and interpretation of non-coding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. PMID:25869999
YM500v2: a small RNA sequencing (smRNA-seq) database for human cancer miRNome research.

PubMed

Cheng, Wei-Chung; Chung, I-Fang; Tsai, Cheng-Fong; Huang, Tse-Shun; Chen, Chen-Yang; Wang, Shao-Chuan; Chang, Ting-Yu; Sun, Hsing-Jen; Chao, Jeffrey Yung-Chuan; Cheng, Cheng-Chung; Wu, Cheng-Wen; Wang, Hsei-Wei

2015-01-01

We previously presented YM500, which is an integrated database for miRNA quantification, isomiR identification, arm switching discovery and novel miRNA prediction from 468 human smRNA-seq datasets. Here in this updated YM500v2 database (http://ngs.ym.edu.tw/ym500/), we focus on the cancer miRNome to make the database more disease-orientated. New miRNA-related algorithms developed after YM500 were included in YM500v2, and, more significantly, more than 8000 cancer-related smRNA-seq datasets (including those of primary tumors, paired normal tissues, PBMC, recurrent tumors, and metastatic tumors) were incorporated into YM500v2. Novel miRNAs (miRNAs not included in the miRBase R21) were not only predicted by three independent algorithms but also cleaned by a new in silico filtration strategy and validated by wetlab data such as Cross-Linked ImmunoPrecipitation sequencing (CLIP-seq) to reduce the false-positive rate. A new function 'Meta-analysis' is additionally provided for allowing users to identify real-time differentially expressed miRNAs and arm-switching events according to customer-defined sample groups and dozens of clinical criteria tidying up by proficient clinicians. Cancer miRNAs identified hold the potential for both basic research and biotech applications. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
Discovering Deeply Divergent RNA Viruses in Existing Metatranscriptome Data with Machine Learning

NASA Astrophysics Data System (ADS)

Rivers, A. R.

2016-02-01

Most sampling of RNA viruses and phages has been directed toward a narrow range of hosts and environments. Several marine metagenomic studies have examined the RNA viral fraction in aquatic samples and found a number of picornaviruses and uncharacterized sequences. The lack of homology to known protein families has limited the discovery of new RNA viruses. We developed a computational method for identifying RNA viruses that relies on information in the codon transition probabilities of viral sequences to train a classifier. This approach does not rely on homology, but it has higher information content than other reference-free methods such as tetranucleotide frequency. Training and validation with RefSeq data gave true positive and true negative rates of 99.6% and 99.5% on the highly imbalanced validation sets (0.2% viruses) that, like the metatranscriptomes themselves, contain mostly non-viral sequences. To further test the method, a validation dataset of putative RNA virus genomes were identified in metatransciptomes by the presence of RNA dependent RNA polymerase, an essential gene for RNA viruses. The classifier successfully identified 99.4% of those contigs as viral. This approach is currently being extended to screen all metatranscriptome data sequenced at the DOE Joint Genome Institute, presently 4.5 Gb of assembled data from 504 public projects representing a wide range of marine, aquatic and terrestrial environments.
CANEapp: a user-friendly application for automated next generation transcriptomic data analysis.

PubMed

Velmeshev, Dmitry; Lally, Patrick; Magistri, Marco; Faghihi, Mohammad Ali

2016-01-13

Next generation sequencing (NGS) technologies are indispensable for molecular biology research, but data analysis represents the bottleneck in their application. Users need to be familiar with computer terminal commands, the Linux environment, and various software tools and scripts. Analysis workflows have to be optimized and experimentally validated to extract biologically meaningful data. Moreover, as larger datasets are being generated, their analysis requires use of high-performance servers. To address these needs, we developed CANEapp (application for Comprehensive automated Analysis of Next-generation sequencing Experiments), a unique suite that combines a Graphical User Interface (GUI) and an automated server-side analysis pipeline that is platform-independent, making it suitable for any server architecture. The GUI runs on a PC or Mac and seamlessly connects to the server to provide full GUI control of RNA-sequencing (RNA-seq) project analysis. The server-side analysis pipeline contains a framework that is implemented on a Linux server through completely automated installation of software components and reference files. Analysis with CANEapp is also fully automated and performs differential gene expression analysis and novel noncoding RNA discovery through alternative workflows (Cuffdiff and R packages edgeR and DESeq2). We compared CANEapp to other similar tools, and it significantly improves on previous developments. We experimentally validated CANEapp's performance by applying it to data derived from different experimental paradigms and confirming the results with quantitative real-time PCR (qRT-PCR). CANEapp adapts to any server architecture by effectively using available resources and thus handles large amounts of data efficiently. CANEapp performance has been experimentally validated on various biological datasets. CANEapp is available free of charge at http://psychiatry.med.miami.edu/research/laboratory-of-translational-rna-genomics/CANE-app . We believe that CANEapp will serve both biologists with no computational experience and bioinformaticians as a simple, timesaving but accurate and powerful tool to analyze large RNA-seq datasets and will provide foundations for future development of integrated and automated high-throughput genomics data analysis tools. Due to its inherently standardized pipeline and combination of automated analysis and platform-independence, CANEapp is an ideal for large-scale collaborative RNA-seq projects between different institutions and research groups.
Transposable element-associated microRNA hairpins produce 21-nt sRNAs integrated into typical microRNA pathways in rice

PubMed Central

Ou-Yang, Fangqian; Luo, Qing-Jun; Zhang, Yue; Richardson, Casey R.; Jiang, Yingwen; Rock, Christopher D.

2013-01-01

microRNAs (miRNAs) are a class of small RNAs (sRNAs) of ~21 nucleotides (nt) in length processed from foldback hairpins by DICER-LIKE1 (DCL1) or DCL4. They regulate the expression of target mRNAs by base pairing through RNA-Induced Silencing Complex (RISC). In the RISC, ARGONAUTE1 (AGO1) is the key protein that cleaves miRNA targets at position ten of a miRNA:target duplex. The authenticity of many annotated rice miRNA hairpins is under debate because of their homology to repeat sequences. Some of them, like miR1884b, have been removed from the current release of miRBase based on incomplete information. In this study, we investigated the association of transposable element (TE)-derived miRNAs with typical miRNA pathways (DCL1/4- and AGO1-dependent) using publicly available deep sequencing datasets. Seven miRNA hairpins with 13 unique sRNAs were specifically enriched in AGO1 immunoprecipitation samples and relatively reduced in DCL1/4 knockdown genotypes. Interestingly, these species are ~21-nt long, instead of 24-nt as annotated in miRBase and the literature. Their expression profiles meet current criteria for functional annotation of miRNAs. In addition, diagnostic cleavage tags were found in degradome datasets for predicted target mRNAs. Most of these miRNA hairpins share significant homology with miniature inverted-repeat transposable elements (MITEs), one type of abundant DNA transposons in rice. Finally, the root-specific production of a 24 nt miRNA-like sRNA was confirmed by RNA blot for a novel EST that maps to the 3'-UTR of a candidate pseudogene showing extensive sequence homology to miR1884b hairpin. Our data are consistent with the hypothesis that TEs can serve as a driving force for the evolution of some MIRNAs, where co-opting of DICER-LIKE1/4 processing and integration into AGO1 could exapt transcribed TE-associated hairpins into typical miRNA pathways. PMID:23420033
IMG/M: integrated genome and metagenome comparative data analysis system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; ...

2016-10-13

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support formore » examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.« less
IMG/M: integrated genome and metagenome comparative data analysis system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support formore » examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.« less
IMG/M: integrated genome and metagenome comparative data analysis system

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Palaniappan, Krishna; Szeto, Ernest; Pillay, Manoj; Ratner, Anna; Huang, Jinghua; Andersen, Evan; Huntemann, Marcel; Varghese, Neha; Hadjithomas, Michalis; Tennessen, Kristin; Nielsen, Torben; Ivanova, Natalia N.; Kyrpides, Nikos C.

2017-01-01

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support for examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review (ER) companion system (IMG/M ER: https://img.jgi.doe.gov/mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system. PMID:27738135
A normalization strategy for comparing tag count data

PubMed Central

2012-01-01

Background High-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data. Results We describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset. Conclusion Our results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data. PMID:22475125
DrImpute: imputing dropout events in single cell RNA sequencing data.

PubMed

Gong, Wuming; Kwak, Il-Youp; Pota, Pruthvi; Koyano-Nakagawa, Naoko; Garry, Daniel J

2018-06-08

The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. We develop DrImpute to impute dropout events in scRNA-seq data. We show that DrImpute has significantly better performance on the separation of the dropout zeros from true zeros than existing imputation algorithms. We also demonstrate that DrImpute can significantly improve the performance of existing tools for clustering, visualization and lineage reconstruction of nine published scRNA-seq datasets. DrImpute can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute is implemented in R and is available at https://github.com/gongx030/DrImpute .
The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)

DOE PAGES

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos; ...

2015-10-26

The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. In conclusion, structural annotation is followed by assignment of protein product names and functions.
The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos

The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. In conclusion, structural annotation is followed by assignment of protein product names and functions.
Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites.

PubMed

Li, Yang Eric; Xiao, Mu; Shi, Binbin; Yang, Yu-Cheng T; Wang, Dong; Wang, Fei; Marcia, Marco; Lu, Zhi John

2017-09-08

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.

A phylogenetic framework for root lesion nematodes of the genus Pratylenchus (Nematoda): Evidence from 18S and D2-D3 expansion segments of 28S ribosomal RNA genes and morphological characters.

PubMed

Subbotin, Sergei A; Ragsdale, Erik J; Mullens, Teresa; Roberts, Philip A; Mundo-Ocampo, Manuel; Baldwin, James G

2008-08-01

The root lesion nematodes of the genus Pratylenchus Filipjev, 1936 are migratory endoparasites of plant roots, considered among the most widespread and important nematode parasites in a variety of crops. We obtained gene sequences from the D2 and D3 expansion segments of 28S rRNA partial and 18S rRNA from 31 populations belonging to 11 valid and two unidentified species of root lesion nematodes and five outgroup taxa. These datasets were analyzed using maximum parsimony and Bayesian inference. The alignments were generated using the secondary structure models for these molecules and analyzed with Bayesian inference under the standard models and the complex model, considering helices under the doublet model and loops and bulges under the general time reversible model. The phylogenetic informativeness of morphological characters is tested by reconstruction of their histories on rRNA based trees using parallel parsimony and Bayesian approaches. Phylogenetic and sequence analyses of the 28S D2-D3 dataset with 145 accessions for 28 species and 18S dataset with 68 accessions for 15 species confirmed among large numbers of geographical diverse isolates that most classical morphospecies are monophyletic. Phylogenetic analyses revealed at least six distinct major clades of examined Pratylenchus species and these clades are generally congruent with those defined by characters derived from lip patterns, numbers of lip annules, and spermatheca shape. Morphological results suggest the need for sophisticated character discovery and analysis for morphology based phylogenetics in nematodes.
Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.
SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes.

PubMed

Pruesse, Elmar; Peplies, Jörg; Glöckner, Frank Oliver

2012-07-15

In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements. In this study, we present the SILVA Incremental Aligner (SINA) used to align the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA uses a combination of k-mer searching and partial order alignment (POA) to maintain very high alignment accuracy while satisfying high throughput performance demands. SINA was evaluated in comparison with the commonly used high throughput MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising 38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy than PyNAST and mothur in all performed benchmarks. Alignment of up to 500 sequences using the latest SILVA SSU/LSU Ref datasets as reference MSA is offered at http://www.arb-silva.de/aligner. This page also links to Linux binaries, user manual and tutorial. SINA is made available under a personal use license.
MetaMetaDB: A Database and Analytic System for Investigating Microbial Habitability

PubMed Central

Yang, Ching-chia; Iwasaki, Wataru

2014-01-01

MetaMetaDB (http://mmdb.aori.u-tokyo.ac.jp/) is a database and analytic system for investigating microbial habitability, i.e., how a prokaryotic group can inhabit different environments. The interaction between prokaryotes and the environment is a key issue in microbiology because distinct prokaryotic communities maintain distinct ecosystems. Because 16S ribosomal RNA (rRNA) sequences play pivotal roles in identifying prokaryotic species, a system that comprehensively links diverse environments to 16S rRNA sequences of the inhabitant prokaryotes is necessary for the systematic understanding of the microbial habitability. However, existing databases are biased to culturable prokaryotes and exhibit limitations in the comprehensiveness of the data because most prokaryotes are unculturable. Recently, metagenomic and 16S rRNA amplicon sequencing approaches have generated abundant 16S rRNA sequence data that encompass unculturable prokaryotes across diverse environments; however, these data are usually buried in large databases and are difficult to access. In this study, we developed MetaMetaDB (Meta-Metagenomic DataBase), which comprehensively and compactly covers 16S rRNA sequences retrieved from public datasets. Using MetaMetaDB, users can quickly generate hypotheses regarding the types of environments a prokaryotic group may be adapted to. We anticipate that MetaMetaDB will improve our understanding of the diversity and evolution of prokaryotes. PMID:24475242
Independent studies using deep sequencing resolve the same set of core bacterial species dominating gut communities of honey bees.

PubMed

Sabree, Zakee L; Hansen, Allison K; Moran, Nancy A

2012-01-01

Starting in 2003, numerous studies using culture-independent methodologies to characterize the gut microbiota of honey bees have retrieved a consistent and distinctive set of eight bacterial species, based on near identity of the 16S rRNA gene sequences. A recent study [Mattila HR, Rios D, Walker-Sperling VE, Roeselers G, Newton ILG (2012) Characterization of the active microbiotas associated with honey bees reveals healthier and broader communities when colonies are genetically diverse. PLoS ONE 7(3): e32962], using pyrosequencing of the V1-V2 hypervariable region of the 16S rRNA gene, reported finding entirely novel bacterial species in honey bee guts, and used taxonomic assignments from these reads to predict metabolic activities based on known metabolisms of cultivable species. To better understand this discrepancy, we analyzed the Mattila et al. pyrotag dataset. In contrast to the conclusions of Mattila et al., we found that the large majority of pyrotag sequences belonged to clusters for which representative sequences were identical to sequences from previously identified core species of the bee microbiota. On average, they represent 95% of the bacteria in each worker bee in the Mattila et al. dataset, a slightly lower value than that found in other studies. Some colonies contain small proportions of other bacteria, mostly species of Enterobacteriaceae. Reanalysis of the Mattila et al. dataset also did not support a relationship between abundances of Bifidobacterium and of putative pathogens or a significant difference in gut communities between colonies from queens that were singly or multiply mated. Additionally, consistent with previous studies, the dataset supports the occurrence of considerable strain variation within core species, even within single colonies. The roles of these bacteria within bees, or the implications of the strain variation, are not yet clear.
Genome-Wide Characterization of RNA Editing in Chicken Embryos Reveals Common Features among Vertebrates.

PubMed

Frésard, Laure; Leroux, Sophie; Roux, Pierre-François; Klopp, Christophe; Fabre, Stéphane; Esquerré, Diane; Dehais, Patrice; Djari, Anis; Gourichon, David; Lagarrigue, Sandrine; Pitel, Frédérique

2015-01-01

RNA editing results in a post-transcriptional nucleotide change in the RNA sequence that creates an alternative nucleotide not present in the DNA sequence. This leads to a diversification of transcription products with potential functional consequences. Two nucleotide substitutions are mainly described in animals, from adenosine to inosine (A-to-I) and from cytidine to uridine (C-to-U). This phenomenon is described in more details in mammals, notably since the availability of next generation sequencing technologies allowing whole genome screening of RNA-DNA differences. The number of studies recording RNA editing in other vertebrates like chicken is still limited. We chose to use high throughput sequencing technologies to search for RNA editing in chicken, and to extend the knowledge of its conservation among vertebrates. We performed sequencing of RNA and DNA from 8 embryos. Being aware of common pitfalls inherent to sequence analyses that lead to false positive discovery, we stringently filtered our datasets and found fewer than 40 reliable candidates. Conservation of particular sites of RNA editing was attested by the presence of 3 edited sites previously detected in mammals. We then characterized editing levels for selected candidates in several tissues and at different time points, from 4.5 days of embryonic development to adults, and observed a clear tissue-specificity and a gradual increase of editing level with time. By characterizing the RNA editing landscape in chicken, our results highlight the extent of evolutionary conservation of this phenomenon within vertebrates, attest to its tissue and stage specificity and provide support of the absence of non A-to-I events from the chicken transcriptome.
Genome-Wide Characterization of RNA Editing in Chicken Embryos Reveals Common Features among Vertebrates

PubMed Central

Frésard, Laure; Leroux, Sophie; Roux, Pierre-François; Klopp, Christophe; Fabre, Stéphane; Esquerré, Diane; Dehais, Patrice; Djari, Anis; Gourichon, David

2015-01-01

RNA editing results in a post-transcriptional nucleotide change in the RNA sequence that creates an alternative nucleotide not present in the DNA sequence. This leads to a diversification of transcription products with potential functional consequences. Two nucleotide substitutions are mainly described in animals, from adenosine to inosine (A-to-I) and from cytidine to uridine (C-to-U). This phenomenon is described in more details in mammals, notably since the availability of next generation sequencing technologies allowing whole genome screening of RNA-DNA differences. The number of studies recording RNA editing in other vertebrates like chicken is still limited. We chose to use high throughput sequencing technologies to search for RNA editing in chicken, and to extend the knowledge of its conservation among vertebrates. We performed sequencing of RNA and DNA from 8 embryos. Being aware of common pitfalls inherent to sequence analyses that lead to false positive discovery, we stringently filtered our datasets and found fewer than 40 reliable candidates. Conservation of particular sites of RNA editing was attested by the presence of 3 edited sites previously detected in mammals. We then characterized editing levels for selected candidates in several tissues and at different time points, from 4.5 days of embryonic development to adults, and observed a clear tissue-specificity and a gradual increase of editing level with time. By characterizing the RNA editing landscape in chicken, our results highlight the extent of evolutionary conservation of this phenomenon within vertebrates, attest to its tissue and stage specificity and provide support of the absence of non A-to-I events from the chicken transcriptome. PMID:26024316
Identifying active foraminifera in the Sea of Japan using metatranscriptomic approach

NASA Astrophysics Data System (ADS)

Lejzerowicz, Franck; Voltsky, Ivan; Pawlowski, Jan

2013-02-01

Metagenetics represents an efficient and rapid tool to describe environmental diversity patterns of microbial eukaryotes based on ribosomal DNA sequences. However, the results of metagenetic studies are often biased by the presence of extracellular DNA molecules that are persistent in the environment, especially in deep-sea sediment. As an alternative, short-lived RNA molecules constitute a good proxy for the detection of active species. Here, we used a metatranscriptomic approach based on RNA-derived (cDNA) sequences to study the diversity of the deep-sea benthic foraminifera and compared it to the metagenetic approach. We analyzed 257 ribosomal DNA and cDNA sequences obtained from seven sediments samples collected in the Sea of Japan at depths ranging from 486 to 3665 m. The DNA and RNA-based approaches gave a similar view of the taxonomic composition of foraminiferal assemblage, but differed in some important points. First, the cDNA dataset was dominated by sequences of rotaliids and robertiniids, suggesting that these calcareous species, some of which have been observed in Rose Bengal stained samples, are the most active component of foraminiferal community. Second, the richness of monothalamous (single-chambered) foraminifera was particularly high in DNA extracts from the deepest samples, confirming that this group of foraminifera is abundant but not necessarily very active in the deep-sea sediments. Finally, the high divergence of undetermined sequences in cDNA dataset indicate the limits of our database and lack of knowledge about some active but possibly rare species. Our study demonstrates the capability of the metatranscriptomic approach to detect active foraminiferal species and prompt its use in future high-throughput sequencing-based environmental surveys.
The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis.

PubMed

Mohorianu, Irina; Stocks, Matthew Benedict; Applegate, Christopher Steven; Folkes, Leighton; Moulton, Vincent

2017-01-01

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models.

PubMed

Ding, Jiarui; Condon, Anne; Shah, Sohrab P

2018-05-21

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbor structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.
RISE: a database of RNA interactome from sequencing experiments

PubMed Central

Gong, Jing; Shao, Di; Xu, Kui

2018-01-01

Abstract We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest. PMID:29040625
CMSA: a heterogeneous CPU/GPU computing system for multiple similar RNA/DNA sequence alignment.

PubMed

Chen, Xi; Wang, Chen; Tang, Shanjiang; Yu, Ce; Zou, Quan

2017-06-24

The multiple sequence alignment (MSA) is a classic and powerful technique for sequence analysis in bioinformatics. With the rapid growth of biological datasets, MSA parallelization becomes necessary to keep its running time in an acceptable level. Although there are a lot of work on MSA problems, their approaches are either insufficient or contain some implicit assumptions that limit the generality of usage. First, the information of users' sequences, including the sizes of datasets and the lengths of sequences, can be of arbitrary values and are generally unknown before submitted, which are unfortunately ignored by previous work. Second, the center star strategy is suited for aligning similar sequences. But its first stage, center sequence selection, is highly time-consuming and requires further optimization. Moreover, given the heterogeneous CPU/GPU platform, prior studies consider the MSA parallelization on GPU devices only, making the CPUs idle during the computation. Co-run computation, however, can maximize the utilization of the computing resources by enabling the workload computation on both CPU and GPU simultaneously. This paper presents CMSA, a robust and efficient MSA system for large-scale datasets on the heterogeneous CPU/GPU platform. It performs and optimizes multiple sequence alignment automatically for users' submitted sequences without any assumptions. CMSA adopts the co-run computation model so that both CPU and GPU devices are fully utilized. Moreover, CMSA proposes an improved center star strategy that reduces the time complexity of its center sequence selection process from O(mn 2 ) to O(mn). The experimental results show that CMSA achieves an up to 11× speedup and outperforms the state-of-the-art software. CMSA focuses on the multiple similar RNA/DNA sequence alignment and proposes a novel bitmap based algorithm to improve the center star strategy. We can conclude that harvesting the high performance of modern GPU is a promising approach to accelerate multiple sequence alignment. Besides, adopting the co-run computation model can maximize the entire system utilization significantly. The source code is available at https://github.com/wangvsa/CMSA .
PmiRExAt: plant miRNA expression atlas database and web applications

PubMed Central

Gurjar, Anoop Kishor Singh; Panwar, Abhijeet Singh; Gupta, Rajinder; Mantri, Shrikant S.

2016-01-01

High-throughput small RNA (sRNA) sequencing technology enables an entirely new perspective for plant microRNA (miRNA) research and has immense potential to unravel regulatory networks. Novel insights gained through data mining in publically available rich resource of sRNA data will help in designing biotechnology-based approaches for crop improvement to enhance plant yield and nutritional value. Bioinformatics resources enabling meta-analysis of miRNA expression across multiple plant species are still evolving. Here, we report PmiRExAt, a new online database resource that caters plant miRNA expression atlas. The web-based repository comprises of miRNA expression profile and query tool for 1859 wheat, 2330 rice and 283 maize miRNA. The database interface offers open and easy access to miRNA expression profile and helps in identifying tissue preferential, differential and constitutively expressing miRNAs. A feature enabling expression study of conserved miRNA across multiple species is also implemented. Custom expression analysis feature enables expression analysis of novel miRNA in total 117 datasets. New sRNA dataset can also be uploaded for analysing miRNA expression profiles for 73 plant species. PmiRExAt application program interface, a simple object access protocol web service allows other programmers to remotely invoke the methods written for doing programmatic search operations on PmiRExAt database. Database URL: http://pmirexat.nabi.res.in. PMID:27081157
Ohmic resistance in a multi-anode MxCs

EPA Pesticide Factsheets

A-3txf_sequence summary.xksx: Abundance of contigs or unique sequences for each biofilm samples from anodes in the MEC reactorHodon Waterloo final_fasta_working.docx: Raw sequences with their identification numbersRNA S1_MEC.docx: Representative sequences with their ID number and taxonomyThis dataset is associated with the following publication:Santodomingo, J., H. Ryu, B. Dhar, and H. Lee. Ohmic resistance affects microbial community and electrochemical kinetics in a multi-anode microbial electrochemical cell. JOURNAL OF POWER SOURCES. Elsevier Science Ltd, New York, NY, USA, 331: 315-321, (2016).
Improve the prediction of RNA-binding residues using structural neighbours.

PubMed

Li, Quan; Cao, Zanxia; Liu, Haiyan

2010-03-01

The interactions between RNA-binding proteins (RBPs) with RNA play key roles in managing some of the cell's basic functions. The identification and prediction of RNA binding sites is important for understanding the RNA-binding mechanism. Computational approaches are being developed to predict RNA-binding residues based on the sequence- or structure-derived features. To achieve higher prediction accuracy, improvements on current prediction methods are necessary. We identified that the structural neighbors of RNA-binding and non-RNA-binding residues have different amino acid compositions. Combining this structure-derived feature with evolutionary (PSSM) and other structural information (secondary structure and solvent accessibility) significantly improves the predictions over existing methods. Using a multiple linear regression approach and 6-fold cross validation, our best model can achieve an overall correct rate of 87.8% and MCC of 0.47, with a specificity of 93.4%, correctly predict 52.4% of the RNA-binding residues for a dataset containing 107 non-homologous RNA-binding proteins. Compared with existing methods, including the amino acid compositions of structure neighbors lead to clearly improvement. A web server was developed for predicting RNA binding residues in a protein sequence (or structure),which is available at http://mcgill.3322.org/RNA/.
Small RNA profiling of low biomass samples: identification and removal of contaminants.

PubMed

Heintz-Buschart, Anna; Yusuf, Dilmurat; Kaysen, Anne; Etheridge, Alton; Fritz, Joëlle V; May, Patrick; de Beaufort, Carine; Upadhyaya, Bimal B; Ghosal, Anubrata; Galas, David J; Wilmes, Paul

2018-05-14

Sequencing-based analyses of low-biomass samples are known to be prone to misinterpretation due to the potential presence of contaminating molecules derived from laboratory reagents and environments. DNA contamination has been previously reported, yet contamination with RNA is usually considered to be very unlikely due to its inherent instability. Small RNAs (sRNAs) identified in tissues and bodily fluids, such as blood plasma, have implications for physiology and pathology, and therefore the potential to act as disease biomarkers. Thus, the possibility for RNA contaminants demands careful evaluation. Herein, we report on the presence of small RNA (sRNA) contaminants in widely used microRNA extraction kits and propose an approach for their depletion. We sequenced sRNAs extracted from human plasma samples and detected important levels of non-human (exogenous) sequences whose source could be traced to the microRNA extraction columns through a careful qPCR-based analysis of several laboratory reagents. Furthermore, we also detected the presence of artefactual sequences related to these contaminants in a range of published datasets, thereby arguing in particular for a re-evaluation of reports suggesting the presence of exogenous RNAs of microbial and dietary origin in blood plasma. To avoid artefacts in future experiments, we also devise several protocols for the removal of contaminant RNAs, define minimal amounts of starting material for artefact-free analyses, and confirm the reduction of contaminant levels for identification of bona fide sequences using 'ultra-clean' extraction kits. This is the first report on the presence of RNA molecules as contaminants in RNA extraction kits. The described protocols should be applied in the future to avoid confounding sRNA studies.
Divergent homologs of the predicted small RNA BpCand697 in Burkholderia spp.

NASA Astrophysics Data System (ADS)

Damiri, Nadzirah; Mohd-Padil, Hirzahida; Firdaus-Raih, Mohd

2015-09-01

The small RNA (sRNA) gene candidate, BpCand697 was previously reported to be unique to Burkholderia spp. and is encoded at 3' non-coding region of a putative AraC family transcription regulator gene. This study demonstrates the conservation of BpCand697 sequence across 32 Burkholderia spp. including B. pseudomallei, B. mallei, B. thailandensis and Burkholderia sp. by integrating both sequence homology and secondary structural analyses of BpCand697 within the dataset. The divergent sequence of BpCand697 was also used as a discriminatory power in clustering the dataset according to the potential virulence of Burkholderia spp., showing that B. thailandensis was clearly secluded from the virulent cluster of B. pseudomallei and B. mallei. Finally, the differential co-transcript expression of BpCand697 and its flanking gene, bpsl2391 was detected in Burkholderia pseudomallei D286 after grown under two different culture conditions using nutrient-rich and minimal media. It is hypothesized that the differential expression of BpCand697-bpsl2391 co-transcript between the two standard prepared media might correlate with nutrient availability in the culture media, suggesting that the physical co-localization of BpCand697 in B. pseudomallei D286 might be directly or indirectly involved with the transcript regulation of bpsl2391 under the selected in vitro culture conditions.
A compendium of multi-omic sequence information from the Saanich Inlet water column

DOE PAGES

Hawley, Alyse K.; Torres-Beltran, Monica; Zaikova, Elena; ...

2017-10-31

Microbial communities play vital roles in earth’s geochemical cycles. Within marine oxygen minimum zones (OMZs) gradients of oxygen, nitrate and sulfide create redox gradients that drive biogeochemical cycling of carbon, nitrogen and sulphur. Climate-change induced expansion and intensification of OMZs and associated biogeochemical activities has significant implications for green house gas production i.e. nitrous oxide and methane. Next generation sequencing technologies have enabled observations of changes in microbial community structure and expression of RNA and protein along these redox gradients within OMZs. Here, we present a multi-omic time series dataset from Saanich Inlet spanning six years, including high spatial resolutionmore » small subunit ribosomal RNA tags, metagenomes, metatranscriptomes, and metaproteomes. As a result, this compendium provides paired multi-omic datasets over multiple time points providing a basis for exploring shifts in microbial community interactions and regulation of metabolic activities both along redox gradients and over time with implications for global climate models.« less
A compendium of multi-omic sequence information from the Saanich Inlet water column

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hawley, Alyse K.; Torres-Beltran, Monica; Zaikova, Elena

Microbial communities play vital roles in earth’s geochemical cycles. Within marine oxygen minimum zones (OMZs) gradients of oxygen, nitrate and sulfide create redox gradients that drive biogeochemical cycling of carbon, nitrogen and sulphur. Climate-change induced expansion and intensification of OMZs and associated biogeochemical activities has significant implications for green house gas production i.e. nitrous oxide and methane. Next generation sequencing technologies have enabled observations of changes in microbial community structure and expression of RNA and protein along these redox gradients within OMZs. Here, we present a multi-omic time series dataset from Saanich Inlet spanning six years, including high spatial resolutionmore » small subunit ribosomal RNA tags, metagenomes, metatranscriptomes, and metaproteomes. As a result, this compendium provides paired multi-omic datasets over multiple time points providing a basis for exploring shifts in microbial community interactions and regulation of metabolic activities both along redox gradients and over time with implications for global climate models.« less
Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

PubMed

Angiuoli, Samuel V; White, James R; Matalka, Malcolm; White, Owen; Fricke, W Florian

2011-01-01

The widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing

PubMed Central

Angiuoli, Samuel V.; White, James R.; Matalka, Malcolm; White, Owen; Fricke, W. Florian

2011-01-01

Background The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. Results We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Conclusions Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers. PMID:22028928
TargetM6A: Identifying N6-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

PubMed

Li, Guang-Qing; Liu, Zi; Shen, Hong-Bin; Yu, Dong-Jun

2016-10-01

As one of the most ubiquitous post-transcriptional modifications of RNA, N 6 -methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly important for both basic biomedical research and practical drug development. In this study, we designed a computational-based method, called TargetM6A, to rapidly and accurately target [Formula: see text] sites solely from the primary RNA sequences. Two new features, i.e., position-specific nucleotide/dinucleotide propensities (PSNP/PSDP), are introduced and combined with the traditional nucleotide composition (NC) feature to formulate RNA sequences. The extracted features are further optimized to obtain a much more compact and discriminative feature subset by applying an incremental feature selection (IFS) procedure. Based on the optimized feature subset, we trained TargetM6A on the training dataset with a support vector machine (SVM) as the prediction engine. We compared the proposed TargetM6A method with existing methods for predicting [Formula: see text] sites by performing stringent jackknife tests and independent validation tests on benchmark datasets. The experimental results show that the proposed TargetM6A method outperformed the existing methods for predicting [Formula: see text] sites and remarkably improved the prediction performances, with MCC = 0.526 and AUC = 0.818. We also provided a user-friendly web server for TargetM6A, which is publicly accessible for academic use at http://csbio.njust.edu.cn/bioinf/TargetM6A.
Genome-wide assessment of differential translations with ribosome profiling data.

PubMed

Xiao, Zhengtao; Zou, Qin; Liu, Yu; Yang, Xuerui

2016-04-04

The closely regulated process of mRNA translation is crucial for precise control of protein abundance and quality. Ribosome profiling, a combination of ribosome foot-printing and RNA deep sequencing, has been used in a large variety of studies to quantify genome-wide mRNA translation. Here, we developed Xtail, an analysis pipeline tailored for ribosome profiling data that comprehensively and accurately identifies differentially translated genes in pairwise comparisons. Applied on simulated and real datasets, Xtail exhibits high sensitivity with minimal false-positive rates, outperforming existing methods in the accuracy of quantifying differential translations. With published ribosome profiling datasets, Xtail does not only reveal differentially translated genes that make biological sense, but also uncovers new events of differential translation in human cancer cells on mTOR signalling perturbation and in human primary macrophages on interferon gamma (IFN-γ) treatment. This demonstrates the value of Xtail in providing novel insights into the molecular mechanisms that involve translational dysregulations.
DRME: Count-based differential RNA methylation analysis at small sample size scenario.

PubMed

Liu, Lian; Zhang, Shao-Wu; Gao, Fan; Zhang, Yixin; Huang, Yufei; Chen, Runsheng; Meng, Jia

2016-04-15

Differential methylation, which concerns difference in the degree of epigenetic regulation via methylation between two conditions, has been formulated as a beta or beta-binomial distribution to address the within-group biological variability in sequencing data. However, a beta or beta-binomial model is usually difficult to infer at small sample size scenario with discrete reads count in sequencing data. On the other hand, as an emerging research field, RNA methylation has drawn more and more attention recently, and the differential analysis of RNA methylation is significantly different from that of DNA methylation due to the impact of transcriptional regulation. We developed DRME to better address the differential RNA methylation problem. The proposed model can effectively describe within-group biological variability at small sample size scenario and handles the impact of transcriptional regulation on RNA methylation. We tested the newly developed DRME algorithm on simulated and 4 MeRIP-Seq case-control studies and compared it with Fisher's exact test. It is in principle widely applicable to several other RNA-related data types as well, including RNA Bisulfite sequencing and PAR-CLIP. The code together with an MeRIP-Seq dataset is available online (https://github.com/lzcyzm/DRME) for evaluation and reproduction of the figures shown in this article. Copyright © 2016 Elsevier Inc. All rights reserved.
Bayesian Correlation Analysis for Sequence Count Data

PubMed Central

Lau, Nelson; Perkins, Theodore J.

2016-01-01

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities’ measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low—especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities’ signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset. PMID:27701449
viRome: an R package for the visualization and analysis of viral small RNA sequence datasets.

PubMed

Watson, Mick; Schnettler, Esther; Kohl, Alain

2013-08-01

RNA interference (RNAi) is known to play an important part in defence against viruses in a range of species. Second-generation sequencing technologies allow us to assay these systems and the small RNAs that play a key role with unprecedented depth. However, scientists need access to tools that can condense, analyse and display the resulting data. Here, we present viRome, a package for R that takes aligned sequence data and produces a range of essential plots and reports. viRome is released under the BSD license as a package for R available for both Windows and Linux http://virome.sf.net. Additional information and a tutorial is available on the ARK-Genomics website: http://www.ark-genomics.org/bioinformatics/virome. mick.watson@roslin.ed.ac.uk.
QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.

PubMed

Zhao, Shanrong; Xi, Li; Quan, Jie; Xi, Hualin; Zhang, Ying; von Schack, David; Vincent, Michael; Zhang, Baohong

2016-01-08

RNA sequencing (RNA-seq), a next-generation sequencing technique for transcriptome profiling, is being increasingly used, in part driven by the decreasing cost of sequencing. Nevertheless, the analysis of the massive amounts of data generated by large-scale RNA-seq remains a challenge. Multiple algorithms pertinent to basic analyses have been developed, and there is an increasing need to automate the use of these tools so as to obtain results in an efficient and user friendly manner. Increased automation and improved visualization of the results will help make the results and findings of the analyses readily available to experimental scientists. By combing the best open source tools developed for RNA-seq data analyses and the most advanced web 2.0 technologies, we have implemented QuickRNASeq, a pipeline for large-scale RNA-seq data analyses and visualization. The QuickRNASeq workflow consists of three main steps. In Step #1, each individual sample is processed, including mapping RNA-seq reads to a reference genome, counting the numbers of mapped reads, quality control of the aligned reads, and SNP (single nucleotide polymorphism) calling. Step #1 is computationally intensive, and can be processed in parallel. In Step #2, the results from individual samples are merged, and an integrated and interactive project report is generated. All analyses results in the report are accessible via a single HTML entry webpage. Step #3 is the data interpretation and presentation step. The rich visualization features implemented here allow end users to interactively explore the results of RNA-seq data analyses, and to gain more insights into RNA-seq datasets. In addition, we used a real world dataset to demonstrate the simplicity and efficiency of QuickRNASeq in RNA-seq data analyses and interactive visualizations. The seamless integration of automated capabilites with interactive visualizations in QuickRNASeq is not available in other published RNA-seq pipelines. The high degree of automation and interactivity in QuickRNASeq leads to a substantial reduction in the time and effort required prior to further downstream analyses and interpretation of the analyses findings. QuickRNASeq advances primary RNA-seq data analyses to the next level of automation, and is mature for public release and adoption.
Predicting protein-binding RNA nucleotides with consideration of binding partners.

PubMed

Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

2015-06-01

In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures. To the best of our knowledge, this is the first sequence-based prediction of protein-binding nucleotides in RNA which considers the binding partner of RNA. The new model will provide valuable information for designing biochemical experiments to find putative protein-binding sites in RNA with unknown structure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Unifying cancer and normal RNA sequencing data from different sources

PubMed Central

Wang, Qingguo; Armenia, Joshua; Zhang, Chao; Penson, Alexander V.; Reznik, Ed; Zhang, Liguo; Minet, Thais; Ochoa, Angelica; Gross, Benjamin E.; Iacobuzio-Donahue, Christine A.; Betel, Doron; Taylor, Barry S.; Gao, Jianjiong; Schultz, Nikolaus

2018-01-01

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare. PMID:29664468
Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

PubMed

McCarthy, Davis J; Campbell, Kieran R; Lun, Aaron T L; Wills, Quin F

2017-04-15

Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . davis@ebi.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.
Zipper plot: visualizing transcriptional activity of genomic regions.

PubMed

Avila Cobos, Francisco; Anckaert, Jasper; Volders, Pieter-Jan; Everaert, Celine; Rombaut, Dries; Vandesompele, Jo; De Preter, Katleen; Mestdagh, Pieter

2017-05-02

Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs. To tackle this problem we have developed the Zipper plot, a novel visualization and analysis method that enables users to simultaneously interrogate thousands of human putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These include publicly available CAGE-sequencing, ChIP-sequencing and DNase-sequencing datasets. Our method only requires three tab-separated fields (chromosome, genomic coordinate of the TSS and strand) as input and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot. Using the Zipper plot, we found evidence of transcription for a set of well-characterized lncRNAs and observed that fewer mono-exonic lncRNAs have CAGE peaks overlapping with their TSSs compared to multi-exonic lncRNAs. Using publicly available RNA-seq data, we found more than one hundred cases where junction reads connected protein-coding gene exons with a downstream mono-exonic lncRNA, revealing the need for a careful evaluation of lncRNA 5'-boundaries. Our method is implemented using the statistical programming language R and is freely available as a webtool.
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs.

PubMed

Parekh, Swati; Ziegenhain, Christoph; Vieth, Beate; Enard, Wolfgang; Hellmann, Ines

2018-06-01

Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution. zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data.
Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset

PubMed Central

Lipps, David; Devineni, Sree

2016-01-01

MiRNAs are short non-coding RNAs of about 22 nucleotides, which play critical roles in gene expression regulation. The biogenesis of miRNAs is largely determined by the sequence and structural features of their parental RNA molecules. Based on these features, multiple computational tools have been developed to predict if RNA transcripts contain miRNAs or not. Although being very successful, these predictors started to face multiple challenges in recent years. Many predictors were optimized using datasets of hundreds of miRNA samples. The sizes of these datasets are much smaller than the number of known miRNAs. Consequently, the prediction accuracy of these predictors in large dataset becomes unknown and needs to be re-tested. In addition, many predictors were optimized for either high sensitivity or high specificity. These optimization strategies may bring in serious limitations in applications. Moreover, to meet continuously raised expectations on these computational tools, improving the prediction accuracy becomes extremely important. In this study, a meta-predictor mirMeta was developed by integrating a set of non-linear transformations with meta-strategy. More specifically, the outputs of five individual predictors were first preprocessed using non-linear transformations, and then fed into an artificial neural network to make the meta-prediction. The prediction accuracy of meta-predictor was validated using both multi-fold cross-validation and independent dataset. The final accuracy of meta-predictor in newly-designed large dataset is improved by 7% to 93%. The meta-predictor is also proved to be less dependent on datasets, as well as has refined balance between sensitivity and specificity. This study has two folds of importance: First, it shows that the combination of non-linear transformations and artificial neural networks improves the prediction accuracy of individual predictors. Second, a new miRNA predictor with significantly improved prediction accuracy is developed for the community for identifying novel miRNAs and the complete set of miRNAs. Source code is available at: https://github.com/xueLab/mirMeta PMID:28002428
DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruggles, Kelly V.; Tang, Zuojian; Wang, Xuya

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations and splice variants identified in cancer cells are translated. Herein we therefore describe a proteogenomic data integration tool (QUILTS) and illustrate its application to whole genome, transcriptome and global MS peptide sequence datasets generated from a pair of luminal and basal-like breast cancer patient derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS process replicates. Despite over thirty sample replicates, only about 10% of all SNV (somatic andmore » germline) were detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNV without a detectable mRNA transcript were also observed demonstrating the transcriptome coverage was also incomplete (~80%). In contrast to germ-line variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than the luminal tumor raising the possibility of differential translation or protein degradation effects. In conclusion, the QUILTS program integrates DNA, RNA and peptide sequencing to assess the degree to which somatic mutations are translated and therefore biologically active. By identifying gaps in sequence coverage QUILTS benchmarks current technology and assesses progress towards whole cancer proteome and transcriptome analysis.« less
An efficient annotation and gene-expression derivation tool for Illumina Solexa datasets.

PubMed

Hosseini, Parsa; Tremblay, Arianne; Matthews, Benjamin F; Alkharouf, Nadim W

2010-07-02

The data produced by an Illumina flow cell with all eight lanes occupied, produces well over a terabyte worth of images with gigabytes of reads following sequence alignment. The ability to translate such reads into meaningful annotation is therefore of great concern and importance. Very easily, one can get flooded with such a great volume of textual, unannotated data irrespective of read quality or size. CASAVA, a optional analysis tool for Illumina sequencing experiments, enables the ability to understand INDEL detection, SNP information, and allele calling. To not only extract from such analysis, a measure of gene expression in the form of tag-counts, but furthermore to annotate such reads is therefore of significant value. We developed TASE (Tag counting and Analysis of Solexa Experiments), a rapid tag-counting and annotation software tool specifically designed for Illumina CASAVA sequencing datasets. Developed in Java and deployed using jTDS JDBC driver and a SQL Server backend, TASE provides an extremely fast means of calculating gene expression through tag-counts while annotating sequenced reads with the gene's presumed function, from any given CASAVA-build. Such a build is generated for both DNA and RNA sequencing. Analysis is broken into two distinct components: DNA sequence or read concatenation, followed by tag-counting and annotation. The end result produces output containing the homology-based functional annotation and respective gene expression measure signifying how many times sequenced reads were found within the genomic ranges of functional annotations. TASE is a powerful tool to facilitate the process of annotating a given Illumina Solexa sequencing dataset. Our results indicate that both homology-based annotation and tag-count analysis are achieved in very efficient times, providing researchers to delve deep in a given CASAVA-build and maximize information extraction from a sequencing dataset. TASE is specially designed to translate sequence data in a CASAVA-build into functional annotations while producing corresponding gene expression measurements. Achieving such analysis is executed in an ultrafast and highly efficient manner, whether the analysis be a single-read or paired-end sequencing experiment. TASE is a user-friendly and freely available application, allowing rapid analysis and annotation of any given Illumina Solexa sequencing dataset with ease.
PRADA: pipeline for RNA sequencing data analysis.

PubMed

Torres-García, Wandaliz; Zheng, Siyuan; Sivachenko, Andrey; Vegesna, Rahulsimham; Wang, Qianghu; Yao, Rong; Berger, Michael F; Weinstein, John N; Getz, Gad; Verhaak, Roel G W

2014-08-01

Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. http://sourceforge.net/projects/prada/ gadgetz@broadinstitute.org or rverhaak@mdanderson.org Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
A transcriptional blueprint for a spiral-cleaving embryo.

PubMed

Chou, Hsien-Chao; Pruitt, Margaret M; Bastin, Benjamin R; Schneider, Stephan Q

2016-08-05

The spiral cleavage mode of early development is utilized in over one-third of all animal phyla and generates embryonic cells of different size, position, and fate through a conserved set of stereotypic and invariant asymmetric cell divisions. Despite the widespread use of spiral cleavage, regulatory and molecular features for any spiral-cleaving embryo are largely uncharted. To address this gap we use RNA-sequencing on the spiralian model Platynereis dumerilii to capture and quantify the first complete genome-wide transcriptional landscape of early spiral cleavage. RNA-sequencing datasets from seven stages in early Platynereis development, from the zygote to the protrochophore, are described here including the de novo assembly and annotation of ~17,200 Platynereis genes. Depth and quality of the RNA-sequencing datasets allow the identification of the temporal onset and level of transcription for each annotated gene, even if the expression is restricted to a single cell. Over 4000 transcripts are maternally contributed and cleared by the end of the early spiral cleavage phase. Small early waves of zygotic expression are followed by major waves of thousands of genes, demarcating the maternal to zygotic transition shortly after the completion of spiral cleavages in this annelid species. Our comprehensive stage-specific transcriptional analysis of early embryonic stages in Platynereis elucidates the regulatory genome during early spiral embryogenesis and defines the maternal to zygotic transition in Platynereis embryos. This transcriptome assembly provides the first systems-level view of the transcriptional and regulatory landscape for a spiral-cleaving embryo.
SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

PubMed

Wang, Tianyu; Nabavi, Sheida

2018-04-24

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a data imputation approach, a logistic regression model and a nonparametric method based on the Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model and data imputation are used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated datasets and real datasets to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity. Copyright © 2018 Elsevier Inc. All rights reserved.
TarPmiR: a new approach for microRNA target site prediction.

PubMed

Ding, Jun; Li, Xiaoman; Hu, Haiyan

2016-09-15

The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ CONTACTS: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
Next-generation sequencing of translocation renal cell carcinoma reveals novel RNA splicing partners and frequent mutations of chromatin-remodeling genes.

PubMed

Malouf, Gabriel G; Su, Xiaoping; Yao, Hui; Gao, Jianjun; Xiong, Liangwen; He, Qiuming; Compérat, Eva; Couturier, Jérôme; Molinié, Vincent; Escudier, Bernard; Camparo, Philippe; Doss, Denaha J; Thompson, Erika J; Khayat, David; Wood, Christopher G; Yu, Willie; Teh, Bin T; Weinstein, John; Tannir, Nizar M

2014-08-01

MITF/TFE translocation renal cell carcinoma (TRCC) is a rare subtype of kidney cancer. Its incidence and the genome-wide characterization of its genetic origin have not been fully elucidated. We performed RNA and exome sequencing on an exploratory set of TRCC (n = 7), and validated our findings using The Cancer Genome Atlas (TCGA) clear-cell RCC (ccRCC) dataset (n = 460). Using the TCGA dataset, we identified seven TRCC (1.5%) cases and determined their genomic profile. We discovered three novel partners of MITF/TFE (LUC7L3, KHSRP, and KHDRBS2) that are involved in RNA splicing. TRCC displayed a unique gene expression signature as compared with other RCC types, and showed activation of MITF, the transforming growth factor β1 and the PI3K complex targets. Genes differentially spliced between TRCC and other RCC types were enriched for MITF and ID2 targets. Exome sequencing of TRCC revealed a distinct mutational spectrum as compared with ccRCC, with frequent mutations in chromatin-remodeling genes (six of eight cases, three of which were from the TCGA). In two cases, we identified mutations in INO80D, an ATP-dependent chromatin-remodeling gene, previously shown to control the amplitude of the S phase. Knockdown of INO80D decreased cell proliferation in a novel cell line bearing LUC7L3-TFE3 translocation. This genome-wide study defines the incidence of TRCC within a ccRCC-directed project and expands the genomic spectrum of TRCC by identifying novel MITF/TFE partners involved in RNA splicing and frequent mutations in chromatin-remodeling genes. ©2014 American Association for Cancer Research.

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

PubMed

Sabooh, M Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H F

2018-05-01

This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m 5 C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m 5 C sites in RNA precisely. The laboratory techniques and procedures are available to identify m 5 C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m 5 C sites from non- m 5 C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism. Copyright © 2018 Elsevier Ltd. All rights reserved.
Gene expression distribution deconvolution in single-cell RNA sequencing.

PubMed

Wang, Jingshu; Huang, Mo; Torre, Eduardo; Dueck, Hannah; Shaffer, Sydney; Murray, John; Raj, Arjun; Li, Mingyao; Zhang, Nancy R

2018-06-26

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND's noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Copyright © 2018 the Author(s). Published by PNAS.
Spatially resolved RNA-sequencing of the embryonic heart identifies a role for Wnt/β-catenin signaling in autonomic control of heart rate

PubMed Central

Burkhard, Silja Barbara

2018-01-01

Development of specialized cells and structures in the heart is regulated by spatially -restricted molecular pathways. Disruptions in these pathways can cause severe congenital cardiac malformations or functional defects. To better understand these pathways and how they regulate cardiac development we used tomo-seq, combining high-throughput RNA-sequencing with tissue-sectioning, to establish a genome-wide expression dataset with high spatial resolution for the developing zebrafish heart. Analysis of the dataset revealed over 1100 genes differentially expressed in sub-compartments. Pacemaker cells in the sinoatrial region induce heart contractions, but little is known about the mechanisms underlying their development. Using our transcriptome map, we identified spatially restricted Wnt/β-catenin signaling activity in pacemaker cells, which was controlled by Islet-1 activity. Moreover, Wnt/β-catenin signaling controls heart rate by regulating pacemaker cellular response to parasympathetic stimuli. Thus, this high-resolution transcriptome map incorporating all cell types in the embryonic heart can expose spatially restricted molecular pathways critical for specific cardiac functions. PMID:29400650
The Transcriptome Analysis and Comparison Explorer--T-ACE: a platform-independent, graphical tool to process large RNAseq datasets of non-model organisms.

PubMed

Philipp, E E R; Kraemer, L; Mountfort, D; Schilhabel, M; Schreiber, S; Rosenstiel, P

2012-03-15

Next generation sequencing (NGS) technologies allow a rapid and cost-effective compilation of large RNA sequence datasets in model and non-model organisms. However, the storage and analysis of transcriptome information from different NGS platforms is still a significant bottleneck, leading to a delay in data dissemination and subsequent biological understanding. Especially database interfaces with transcriptome analysis modules going beyond mere read counts are missing. Here, we present the Transcriptome Analysis and Comparison Explorer (T-ACE), a tool designed for the organization and analysis of large sequence datasets, and especially suited for transcriptome projects of non-model organisms with little or no a priori sequence information. T-ACE offers a TCL-based interface, which accesses a PostgreSQL database via a php-script. Within T-ACE, information belonging to single sequences or contigs, such as annotation or read coverage, is linked to the respective sequence and immediately accessible. Sequences and assigned information can be searched via keyword- or BLAST-search. Additionally, T-ACE provides within and between transcriptome analysis modules on the level of expression, GO terms, KEGG pathways and protein domains. Results are visualized and can be easily exported for external analysis. We developed T-ACE for laboratory environments, which have only a limited amount of bioinformatics support, and for collaborative projects in which different partners work on the same dataset from different locations or platforms (Windows/Linux/MacOS). For laboratories with some experience in bioinformatics and programming, the low complexity of the database structure and open-source code provides a framework that can be customized according to the different needs of the user and transcriptome project.
Parallel analysis of RNA ends enhances global investigation of microRNAs and target RNAs of Brachypodium distachyon

PubMed Central

2013-01-01

Background The wild grass Brachypodium distachyon has emerged as a model system for temperate grasses and biofuel plants. However, the global analysis of miRNAs, molecules known to be key for eukaryotic gene regulation, has been limited in B. distachyon to studies examining a few samples or that rely on computational predictions. Similarly an in-depth global analysis of miRNA-mediated target cleavage using parallel analysis of RNA ends (PARE) data is lacking in B. distachyon. Results B. distachyon small RNAs were cloned and deeply sequenced from 17 libraries that represent different tissues and stresses. Using a computational pipeline, we identified 116 miRNAs including not only conserved miRNAs that have not been reported in B. distachyon, but also non-conserved miRNAs that were not found in other plants. To investigate miRNA-mediated cleavage function, four PARE libraries were constructed from key tissues and sequenced to a total depth of approximately 70 million sequences. The roughly 5 million distinct genome-matched sequences that resulted represent an extensive dataset for analyzing small RNA-guided cleavage events. Analysis of the PARE and miRNA data provided experimental evidence for miRNA-mediated cleavage of 264 sites in predicted miRNA targets. In addition, PARE analysis revealed that differentially expressed miRNAs in the same family guide specific target RNA cleavage in a correspondingly tissue-preferential manner. Conclusions B. distachyon miRNAs and target RNAs were experimentally identified and analyzed. Knowledge gained from this study should provide insights into the roles of miRNAs and the regulation of their targets in B. distachyon and related plants. PMID:24367943
Next-generation sequencing coupled with a cell-free display technology for high-throughput production of reliable interactome data

PubMed Central

Fujimori, Shigeo; Hirai, Naoya; Ohashi, Hiroyuki; Masuoka, Kazuyo; Nishikimi, Akihiko; Fukui, Yoshinori; Washio, Takanori; Oshikubo, Tomohiro; Yamashita, Tatsuhiro; Miyamoto-Sato, Etsuko

2012-01-01

Next-generation sequencing (NGS) has been applied to various kinds of omics studies, resulting in many biological and medical discoveries. However, high-throughput protein-protein interactome datasets derived from detection by sequencing are scarce, because protein-protein interaction analysis requires many cell manipulations to examine the interactions. The low reliability of the high-throughput data is also a problem. Here, we describe a cell-free display technology combined with NGS that can improve both the coverage and reliability of interactome datasets. The completely cell-free method gives a high-throughput and a large detection space, testing the interactions without using clones. The quantitative information provided by NGS reduces the number of false positives. The method is suitable for the in vitro detection of proteins that interact not only with the bait protein, but also with DNA, RNA and chemical compounds. Thus, it could become a universal approach for exploring the large space of protein sequences and interactome networks. PMID:23056904
MINTmap: fast and exhaustive profiling of nuclear and mitochondrial tRNA fragments from short RNA-seq data

PubMed Central

Loher, Phillipe; Telonis, Aristeidis G.; Rigoutsos, Isidore

2017-01-01

Transfer RNA fragments (tRFs) are an established class of constitutive regulatory molecules that arise from precursor and mature tRNAs. RNA deep sequencing (RNA-seq) has greatly facilitated the study of tRFs. However, the repeat nature of the tRNA templates and the idiosyncrasies of tRNA sequences necessitate the development and use of methodologies that differ markedly from those used to analyze RNA-seq data when studying microRNAs (miRNAs) or messenger RNAs (mRNAs). Here we present MINTmap (for MItochondrial and Nuclear TRF mapping), a method and a software package that was developed specifically for the quick, deterministic and exhaustive identification of tRFs in short RNA-seq datasets. In addition to identifying them, MINTmap is able to unambiguously calculate and report both raw and normalized abundances for the discovered tRFs. Furthermore, to ensure specificity, MINTmap identifies the subset of discovered tRFs that could be originating outside of tRNA space and flags them as candidate false positives. Our comparative analysis shows that MINTmap exhibits superior sensitivity and specificity to other available methods while also being exceptionally fast. The MINTmap codes are available through https://github.com/TJU-CMC-Org/MINTmap/ under an open source GNU GPL v3.0 license. PMID:28220888
FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

PubMed

Xiao, Chuan-Le; Mai, Zhi-Biao; Lian, Xin-Lei; Zhong, Jia-Yong; Jin, Jing-Jie; He, Qing-Yu; Zhang, Gong

2014-01-01

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.
Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs.

PubMed

Lazzari, Barbara; Caprera, Andrea; Cestaro, Alessandro; Merelli, Ivan; Del Corvo, Marcello; Fontana, Paolo; Milanesi, Luciano; Velasco, Riccardo; Stella, Alessandra

2009-06-29

Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated.
The first whole transcriptomic exploration of pre-oviposited early chicken embryos using single and bulked embryonic RNA-sequencing.

PubMed

Hwang, Young Sun; Seo, Minseok; Choi, Hee Jung; Kim, Sang Kyung; Kim, Heebal; Han, Jae Yong

2018-04-01

The chicken is a valuable model organism, especially in evolutionary and embryology research because its embryonic development occurs in the egg. However, despite its scientific importance, no transcriptome data have been generated for deciphering the early developmental stages of the chicken because of practical and technical constraints in accessing pre-oviposited embryos. Here, we determine the entire transcriptome of pre-oviposited avian embryos, including oocyte, zygote, and intrauterine embryos from Eyal-giladi and Kochav stage I (EGK.I) to EGK.X collected using a noninvasive approach for the first time. We also compare RNA-sequencing data obtained using a bulked embryo sequencing and single embryo/cell sequencing technique. The raw sequencing data were preprocessed with two genome builds, Galgal4 and Galgal5, and the expression of 17,108 and 26,102 genes was quantified in the respective builds. There were some differences between the two techniques, as well as between the two genome builds, and these were affected by the emergence of long intergenic noncoding RNA annotations. The first transcriptome datasets of pre-oviposited early chicken embryos based on bulked and single embryo sequencing techniques will serve as a valuable resource for investigating early avian embryogenesis, for comparative studies among vertebrates, and for novel gene annotation in the chicken genome.
Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification.

PubMed

Ziesemer, Kirsten A; Mann, Allison E; Sankaranarayanan, Krithivasan; Schroeder, Hannes; Ozga, Andrew T; Brandt, Bernd W; Zaura, Egija; Waters-Rist, Andrea; Hoogland, Menno; Salazar-García, Domingo C; Aldenderfer, Mark; Speller, Camilla; Hendy, Jessica; Weston, Darlene A; MacDonald, Sandy J; Thomas, Gavin H; Collins, Matthew J; Lewis, Cecil M; Hofman, Corinne; Warinner, Christina

2015-11-13

To date, characterization of ancient oral (dental calculus) and gut (coprolite) microbiota has been primarily accomplished through a metataxonomic approach involving targeted amplification of one or more variable regions in the 16S rRNA gene. Specifically, the V3 region (E. coli 341-534) of this gene has been suggested as an excellent candidate for ancient DNA amplification and microbial community reconstruction. However, in practice this metataxonomic approach often produces highly skewed taxonomic frequency data. In this study, we use non-targeted (shotgun metagenomics) sequencing methods to better understand skewed microbial profiles observed in four ancient dental calculus specimens previously analyzed by amplicon sequencing. Through comparisons of microbial taxonomic counts from paired amplicon (V3 U341F/534R) and shotgun sequencing datasets, we demonstrate that extensive length polymorphisms in the V3 region are a consistent and major cause of differential amplification leading to taxonomic bias in ancient microbiome reconstructions based on amplicon sequencing. We conclude that systematic amplification bias confounds attempts to accurately reconstruct microbiome taxonomic profiles from 16S rRNA V3 amplicon data generated using universal primers. Because in silico analysis indicates that alternative 16S rRNA hypervariable regions will present similar challenges, we advocate for the use of a shotgun metagenomics approach in ancient microbiome reconstructions.
Intrinsic challenges in ancient microbiome reconstruction using 16S rRNA gene amplification

PubMed Central

Ziesemer, Kirsten A.; Mann, Allison E.; Sankaranarayanan, Krithivasan; Schroeder, Hannes; Ozga, Andrew T.; Brandt, Bernd W.; Zaura, Egija; Waters-Rist, Andrea; Hoogland, Menno; Salazar-García, Domingo C.; Aldenderfer, Mark; Speller, Camilla; Hendy, Jessica; Weston, Darlene A.; MacDonald, Sandy J.; Thomas, Gavin H.; Collins, Matthew J.; Lewis, Cecil M.; Hofman, Corinne; Warinner, Christina

2015-01-01

To date, characterization of ancient oral (dental calculus) and gut (coprolite) microbiota has been primarily accomplished through a metataxonomic approach involving targeted amplification of one or more variable regions in the 16S rRNA gene. Specifically, the V3 region (E. coli 341–534) of this gene has been suggested as an excellent candidate for ancient DNA amplification and microbial community reconstruction. However, in practice this metataxonomic approach often produces highly skewed taxonomic frequency data. In this study, we use non-targeted (shotgun metagenomics) sequencing methods to better understand skewed microbial profiles observed in four ancient dental calculus specimens previously analyzed by amplicon sequencing. Through comparisons of microbial taxonomic counts from paired amplicon (V3 U341F/534R) and shotgun sequencing datasets, we demonstrate that extensive length polymorphisms in the V3 region are a consistent and major cause of differential amplification leading to taxonomic bias in ancient microbiome reconstructions based on amplicon sequencing. We conclude that systematic amplification bias confounds attempts to accurately reconstruct microbiome taxonomic profiles from 16S rRNA V3 amplicon data generated using universal primers. Because in silico analysis indicates that alternative 16S rRNA hypervariable regions will present similar challenges, we advocate for the use of a shotgun metagenomics approach in ancient microbiome reconstructions. PMID:26563586
The transcriptome of nitrofen-induced pulmonary hypoplasia in the rat model of congenital diaphragmatic hernia.

PubMed

Mahood, Thomas H; Johar, Dina R; Iwasiow, Barbara M; Xu, Wayne; Keijzer, Richard

2016-05-01

We currently do not know how the herbicide nitrofen induces lung hypoplasia and congenital diaphragmatic hernia in rats. Our aim was to compare the differentially expressed transcriptome of nitrofen-induced hypoplastic lungs to control lungs in embryonic day 13 rat embryos before the development of embryonic diaphragmatic defects. Using next-generation sequencing technology, we identified the expression profile of microRNA (miRNA) and mRNA genes. Once the dataset was validated by both RT-qPCR and digital-PCR, we conducted gene ontology, miRNA target analysis, and orthologous miRNA sequence matching for the deregulated miRNAs in silico. Our study identified 186 known mRNA and 100 miRNAs which were differentially expressed in nitrofen-induced hypoplastic lungs. Sixty-four rat miRNAs homologous to known human miRNAs were identified. A subset of these genes may promote lung hypoplasia in rat and/or human, and we discuss their associations. Potential miRNA pathways relevant to nitrofen-induced lung hypoplasia include PI3K, TGF-β, and cell cycle kinases. Nitrofen-induced hypoplastic lungs have an abnormal transcriptome that may lead to impaired development.
High-throughput detection of RNA processing in bacteria.

PubMed

Gill, Erin E; Chan, Luisa S; Winsor, Geoffrey L; Dobson, Neil; Lo, Raymond; Ho Sui, Shannan J; Dhillon, Bhavjinder K; Taylor, Patrick K; Shrestha, Raunak; Spencer, Cory; Hancock, Robert E W; Unrau, Peter J; Brinkman, Fiona S L

2018-03-27

Understanding the RNA processing of an organism's transcriptome is an essential but challenging step in understanding its biology. Here we investigate with unprecedented detail the transcriptome of Pseudomonas aeruginosa PAO1, a medically important and innately multi-drug resistant bacterium. We systematically mapped RNA cleavage and dephosphorylation sites that result in 5'-monophosphate terminated RNA (pRNA) using monophosphate RNA-Seq (pRNA-Seq). Transcriptional start sites (TSS) were also mapped using differential RNA-Seq (dRNA-Seq) and both datasets were compared to conventional RNA-Seq performed in a variety of growth conditions. The pRNA-Seq library revealed known tRNA, rRNA and transfer-messenger RNA (tmRNA) processing sites, together with previously uncharacterized RNA cleavage events that were found disproportionately near the 5' ends of transcripts associated with basic bacterial functions such as oxidative phosphorylation and purine metabolism. The majority (97%) of the processed mRNAs were cleaved at precise codon positions within defined sequence motifs indicative of distinct endonucleolytic activities. The most abundant of these motifs corresponded closely to an E. coli RNase E site previously established in vitro. Using the dRNA-Seq library, we performed an operon analysis and predicted 3159 potential TSS. A correlation analysis uncovered 105 antiparallel pairs of TSS that were separated by 18 bp from each other and were centered on single palindromic TAT(A/T)ATA motifs (likely - 10 promoter elements), suggesting that, consistent with previous in vitro experimentation, these sites can initiate transcription bi-directionally and may thus provide a novel form of transcriptional regulation. TSS and RNA-Seq analysis allowed us to confirm expression of small non-coding RNAs (ncRNAs), many of which are differentially expressed in swarming and biofilm formation conditions. This study uses pRNA-Seq, a method that provides a genome-wide survey of RNA processing, to study the bacterium Pseudomonas aeruginosa and discover extensive transcript processing not previously appreciated. We have also gained novel insight into RNA maturation and turnover as well as a potential novel form of transcription regulation. NOTE: All sequence data has been submitted to the NCBI sequence read archive. Accession numbers are as follows: [NCBI sequence read archive: SRX156386, SRX157659, SRX157660, SRX157661, SRX157683 and SRX158075]. The sequence data is viewable using Jbrowse on www.pseudomonas.com .
Multiple hot-deck imputation for network inference from RNA sequencing data.

PubMed

Imbert, Alyssa; Valsesia, Armand; Le Gall, Caroline; Armenise, Claudia; Lefebvre, Gregory; Gourraud, Pierre-Antoine; Viguerie, Nathalie; Villa-Vialaneix, Nathalie

2018-05-15

Network inference provides a global view of the relations existing between gene expression in a given transcriptomic experiment (often only for a restricted list of chosen genes). However, it is still a challenging problem: even if the cost of sequencing techniques has decreased over the last years, the number of samples in a given experiment is still (very) small compared to the number of genes. We propose a method to increase the reliability of the inference when RNA-seq expression data have been measured together with an auxiliary dataset that can provide external information on gene expression similarity between samples. Our statistical approach, hd-MI, is based on imputation for samples without available RNA-seq data that are considered as missing data but are observed on the secondary dataset. hd-MI can improve the reliability of the inference for missing rates up to 30% and provides more stable networks with a smaller number of false positive edges. On a biological point of view, hd-MI was also found relevant to infer networks from RNA-seq data acquired in adipose tissue during a nutritional intervention in obese individuals. In these networks, novel links between genes were highlighted, as well as an improved comparability between the two steps of the nutritional intervention. Software and sample data are available as an R package, RNAseqNet, that can be downloaded from the Comprehensive R Archive Network (CRAN). alyssa.imbert@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.
Comparative and Joint Analysis of Two Metagenomic Datasets from a Biogas Fermenter Obtained by 454-Pyrosequencing

PubMed Central

Jaenicke, Sebastian; Ander, Christina; Bekel, Thomas; Bisdorf, Regina; Dröge, Marcus; Gartemann, Karl-Heinz; Jünemann, Sebastian; Kaiser, Olaf; Krause, Lutz; Tille, Felix; Zakrzewski, Martha; Pühler, Alfred

2011-01-01

Biogas production from renewable resources is attracting increased attention as an alternative energy source due to the limited availability of traditional fossil fuels. Many countries are promoting the use of alternative energy sources for sustainable energy production. In this study, a metagenome from a production-scale biogas fermenter was analysed employing Roche's GS FLX Titanium technology and compared to a previous dataset obtained from the same community DNA sample that was sequenced on the GS FLX platform. Taxonomic profiling based on 16S rRNA-specific sequences and an Environmental Gene Tag (EGT) analysis employing CARMA demonstrated that both approaches benefit from the longer read lengths obtained on the Titanium platform. Results confirmed Clostridia as the most prevalent taxonomic class, whereas species of the order Methanomicrobiales are dominant among methanogenic Archaea. However, the analyses also identified additional taxa that were missed by the previous study, including members of the genera Streptococcus, Acetivibrio, Garciella, Tissierella, and Gelria, which might also play a role in the fermentation process leading to the formation of methane. Taking advantage of the CARMA feature to correlate taxonomic information of sequences with their assigned functions, it appeared that Firmicutes, followed by Bacteroidetes and Proteobacteria, dominate within the functional context of polysaccharide degradation whereas Methanomicrobiales represent the most abundant taxonomic group responsible for methane production. Clostridia is the most important class involved in the reductive CoA pathway (Wood-Ljungdahl pathway) that is characteristic for acetogenesis. Based on binning of 16S rRNA-specific sequences allocated to the dominant genus Methanoculleus, it could be shown that this genus is represented by several different species. Phylogenetic analysis of these sequences placed them in close proximity to the hydrogenotrophic methanogen Methanoculleus bourgensis. While rarefaction analyses still indicate incomplete coverage, examination of the GS FLX Titanium dataset resulted in the identification of additional genera and functional elements, providing a far more complete coverage of the community involved in anaerobic fermentative pathways leading to methane formation. PMID:21297863
Genome-wide assessment of differential translations with ribosome profiling data

PubMed Central

Xiao, Zhengtao; Zou, Qin; Liu, Yu; Yang, Xuerui

2016-01-01

The closely regulated process of mRNA translation is crucial for precise control of protein abundance and quality. Ribosome profiling, a combination of ribosome foot-printing and RNA deep sequencing, has been used in a large variety of studies to quantify genome-wide mRNA translation. Here, we developed Xtail, an analysis pipeline tailored for ribosome profiling data that comprehensively and accurately identifies differentially translated genes in pairwise comparisons. Applied on simulated and real datasets, Xtail exhibits high sensitivity with minimal false-positive rates, outperforming existing methods in the accuracy of quantifying differential translations. With published ribosome profiling datasets, Xtail does not only reveal differentially translated genes that make biological sense, but also uncovers new events of differential translation in human cancer cells on mTOR signalling perturbation and in human primary macrophages on interferon gamma (IFN-γ) treatment. This demonstrates the value of Xtail in providing novel insights into the molecular mechanisms that involve translational dysregulations. PMID:27041671
miTRATA: a web-based tool for microRNA Truncation and Tailing Analysis.

PubMed

Patel, Parth; Ramachandruni, S Deepthi; Kakrana, Atul; Nakano, Mayumi; Meyers, Blake C

2016-02-01

We describe miTRATA, the first web-based tool for microRNA Truncation and Tailing Analysis--the analysis of 3' modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA is implemented in Python (version 3) and employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online. miTRATA's strengths lie in (i) its biologist-focused web interface, (ii) improved scalability via parallel processing and (iii) its uniqueness as a webtool to perform microRNA truncation and tailing analysis. miTRATA is developed in Python and PHP. It is available as a web-based application from https://wasabi.dbi.udel.edu/∼apps/ta/. meyers@dbi.udel.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins.

PubMed

Walia, Rasna R; Xue, Li C; Wilkins, Katherine; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2014-01-01

Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.
El Verde Ridge, El Verde Valley, and Rio Icacos root phosphatase and bacterial community composition (December 2015)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cabugao, Kristine; Timm, Collin; Carrell, Alyssa

Raw data of resin P values, root phosphatase, bacterial community 16S rRNA gene sequences, and bacterial isolate phosphatase and P solubilization in Rio Icacos, El Verde Ridge and El Verde Valley. Contact cabugaokm@ornl.gov if you need to use this dataset for additional information.

In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome.

PubMed

Chowdhury, Shomeek; Zhang, Jian; Kurgan, Lukasz

2018-05-28

Deciphering a complete landscape of protein-RNA interactions in the human proteome remains an elusive challenge. We computationally elucidate RNA binding proteins (RBPs) using an approach that complements previous efforts. We employ two modern complementary sequence-based methods that provide accurate predictions from the structured and the intrinsically disordered sequences, even in the absence of sequence similarity to the known RBPs. We generate and analyze putative RNA binding residues on the whole proteome scale. Using a conservative setting that ensures low, 5% false positive rate, we identify 1511 putative RBPs that include 281 known RBPs and 166 RBPs that were previously predicted. We empirically demonstrate that these overlaps are statistically significant. We also validate the putative RBPs based on two major hallmarks of their RNA binding residues: high levels of evolutionary conservation and enrichment in charged amino acids. Moreover, we show that the novel RBPs are significantly under-annotated functionally which coincides with the fact that they were not yet found to interact with RNAs. We provide two examples of our novel putative RBPs for which there is recent evidence of their interactions with RNAs. The dataset of novel putative RBPs and RNA binding residues for the future hypothesis generation is provided in the Supporting Information. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Computational Identification and Functional Predictions of Long Noncoding RNA in Zea mays

PubMed Central

Boerner, Susan; McGinnis, Karen M.

2012-01-01

Background Computational analysis of cDNA sequences from multiple organisms suggests that a large portion of transcribed DNA does not code for a functional protein. In mammals, noncoding transcription is abundant, and often results in functional RNA molecules that do not appear to encode proteins. Many long noncoding RNAs (lncRNAs) appear to have epigenetic regulatory function in humans, including HOTAIR and XIST. While epigenetic gene regulation is clearly an essential mechanism in plants, relatively little is known about the presence or function of lncRNAs in plants. Methodology/Principal Findings To explore the connection between lncRNA and epigenetic regulation of gene expression in plants, a computational pipeline using the programming language Python has been developed and applied to maize full length cDNA sequences to identify, classify, and localize potential lncRNAs. The pipeline was used in parallel with an SVM tool for identifying ncRNAs to identify the maximal number of ncRNAs in the dataset. Although the available library of sequences was small and potentially biased toward protein coding transcripts, 15% of the sequences were predicted to be noncoding. Approximately 60% of these sequences appear to act as precursors for small RNA molecules and may function to regulate gene expression via a small RNA dependent mechanism. ncRNAs were predicted to originate from both genic and intergenic loci. Of the lncRNAs that originated from genic loci, ∼20% were antisense to the host gene loci. Conclusions/Significance Consistent with similar studies in other organisms, noncoding transcription appears to be widespread in the maize genome. Computational predictions indicate that maize lncRNAs may function to regulate expression of other genes through multiple RNA mediated mechanisms. PMID:22916204
MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.

PubMed

Xu, Xilin; Wu, Aiping; Zhang, Xinlei; Su, Mingming; Jiang, Taijiao; Yuan, Zhe-Ming

2016-01-01

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).
Intracellular diversity of the V4 and V9 regions of the 18S rRNA in marine protists (radiolarians) assessed by high-throughput sequencing.

PubMed

Decelle, Johan; Romac, Sarah; Sasaki, Eriko; Not, Fabrice; Mahé, Frédéric

2014-01-01

Metabarcoding is a powerful tool for exploring microbial diversity in the environment, but its accurate interpretation is impeded by diverse technical (e.g. PCR and sequencing errors) and biological biases (e.g. intra-individual polymorphism) that remain poorly understood. To help interpret environmental metabarcoding datasets, we investigated the intracellular diversity of the V4 and V9 regions of the 18S rRNA gene from Acantharia and Nassellaria (radiolarians) using 454 pyrosequencing. Individual cells of radiolarians were isolated, and PCRs were performed with generalist primers to amplify the V4 and V9 regions. Different denoising procedures were employed to filter the pyrosequenced raw amplicons (Acacia, AmpliconNoise, Linkage method). For each of the six isolated cells, an average of 541 V4 and 562 V9 amplicons assigned to radiolarians were obtained, from which one numerically dominant sequence and several minor variants were found. At the 97% identity, a diversity metrics commonly used in environmental surveys, up to 5 distinct OTUs were detected in a single cell. However, most amplicons grouped within a single OTU whereas other OTUs contained very few amplicons. Different analytical methods provided evidence that most minor variants forming different OTUs correspond to PCR and sequencing artifacts. Duplicate PCR and sequencing from the same DNA extract of a single cell had only 9 to 16% of unique amplicons in common, and alignment visualization of V4 and V9 amplicons showed that most minor variants contained substitutions in highly-conserved regions. We conclude that intracellular variability of the 18S rRNA in radiolarians is very limited despite its multi-copy nature and the existence of multiple nuclei in these protists. Our study recommends some technical guidelines to conservatively discard artificial amplicons from metabarcoding datasets, and thus properly assess the diversity and richness of protists in the environment.
An efficient annotation and gene-expression derivation tool for Illumina Solexa datasets

PubMed Central

2010-01-01

Background The data produced by an Illumina flow cell with all eight lanes occupied, produces well over a terabyte worth of images with gigabytes of reads following sequence alignment. The ability to translate such reads into meaningful annotation is therefore of great concern and importance. Very easily, one can get flooded with such a great volume of textual, unannotated data irrespective of read quality or size. CASAVA, a optional analysis tool for Illumina sequencing experiments, enables the ability to understand INDEL detection, SNP information, and allele calling. To not only extract from such analysis, a measure of gene expression in the form of tag-counts, but furthermore to annotate such reads is therefore of significant value. Findings We developed TASE (Tag counting and Analysis of Solexa Experiments), a rapid tag-counting and annotation software tool specifically designed for Illumina CASAVA sequencing datasets. Developed in Java and deployed using jTDS JDBC driver and a SQL Server backend, TASE provides an extremely fast means of calculating gene expression through tag-counts while annotating sequenced reads with the gene's presumed function, from any given CASAVA-build. Such a build is generated for both DNA and RNA sequencing. Analysis is broken into two distinct components: DNA sequence or read concatenation, followed by tag-counting and annotation. The end result produces output containing the homology-based functional annotation and respective gene expression measure signifying how many times sequenced reads were found within the genomic ranges of functional annotations. Conclusions TASE is a powerful tool to facilitate the process of annotating a given Illumina Solexa sequencing dataset. Our results indicate that both homology-based annotation and tag-count analysis are achieved in very efficient times, providing researchers to delve deep in a given CASAVA-build and maximize information extraction from a sequencing dataset. TASE is specially designed to translate sequence data in a CASAVA-build into functional annotations while producing corresponding gene expression measurements. Achieving such analysis is executed in an ultrafast and highly efficient manner, whether the analysis be a single-read or paired-end sequencing experiment. TASE is a user-friendly and freely available application, allowing rapid analysis and annotation of any given Illumina Solexa sequencing dataset with ease. PMID:20598141
Metavisitor, a Suite of Galaxy Tools for Simple and Rapid Detection and Discovery of Viruses in Deep Sequence Data

PubMed Central

Vernick, Kenneth D.

2017-01-01

Metavisitor is a software package that allows biologists and clinicians without specialized bioinformatics expertise to detect and assemble viral genomes from deep sequence datasets. The package is composed of a set of modular bioinformatic tools and workflows that are implemented in the Galaxy framework. Using the graphical Galaxy workflow editor, users with minimal computational skills can use existing Metavisitor workflows or adapt them to suit specific needs by adding or modifying analysis modules. Metavisitor works with DNA, RNA or small RNA sequencing data over a range of read lengths and can use a combination of de novo and guided approaches to assemble genomes from sequencing reads. We show that the software has the potential for quick diagnosis as well as discovery of viruses from a vast array of organisms. Importantly, we provide here executable Metavisitor use cases, which increase the accessibility and transparency of the software, ultimately enabling biologists or clinicians to focus on biological or medical questions. PMID:28045932
RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers

PubMed Central

Tan, Jean-Marie; Payne, Elizabeth J.; Lin, Lynlee L.; Sinnya, Sudipta; Raphael, Anthony P.; Lambie, Duncan; Frazer, Ian H.; Dinger, Marcel E.; Soyer, H. Peter

2017-01-01

Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, we have utilised next generation RNA sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes that are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as qPCR RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer. PMID:28852586
Functional Advantages of Conserved Intrinsic Disorder in RNA-Binding Proteins.

PubMed

Varadi, Mihaly; Zsolyomi, Fruzsina; Guharoy, Mainak; Tompa, Peter

2015-01-01

Proteins form large macromolecular assemblies with RNA that govern essential molecular processes. RNA-binding proteins have often been associated with conformational flexibility, yet the extent and functional implications of their intrinsic disorder have never been fully assessed. Here, through large-scale analysis of comprehensive protein sequence and structure datasets we demonstrate the prevalence of intrinsic structural disorder in RNA-binding proteins and domains. We addressed their functionality through a quantitative description of the evolutionary conservation of disordered segments involved in binding, and investigated the structural implications of flexibility in terms of conformational stability and interface formation. We conclude that the functional role of intrinsically disordered protein segments in RNA-binding is two-fold: first, these regions establish extended, conserved electrostatic interfaces with RNAs via induced fit. Second, conformational flexibility enables them to target different RNA partners, providing multi-functionality, while also ensuring specificity. These findings emphasize the functional importance of intrinsically disordered regions in RNA-binding proteins.
Brettanomyces acidodurans sp. nov., a new acetic acid producing yeast species from olive oil.

PubMed

Péter, Gábor; Dlauchy, Dénes; Tóbiás, Andrea; Fülöp, László; Podgoršek, Martina; Čadež, Neža

2017-05-01

Two yeast strains representing a hitherto undescribed yeast species were isolated from olive oil and spoiled olive oil originating from Spain and Israel, respectively. Both strains are strong acetic acid producers, equipped with considerable tolerance to acetic acid. The cultures are not short-lived. Cellobiose is fermented as well as several other sugars. The sequences of their large subunit (LSU) rRNA gene D1/D2 domain are very divergent from the sequences available in the GenBank. They differ from the closest hit, Brettanomyces naardenensis by about 27%, mainly substitutions. Sequence analyses of the concatenated dataset from genes of the small subunit (SSU) rRNA, LSU rRNA and translation elongation factor-1α (EF-1α) placed the two strains as an early diverging member of the Brettanomyces/Dekkera clade with high bootstrap support. Sexual reproduction was not observed. The name Brettanomyces acidodurans sp. nov. (holotype: NCAIM Y.02178 T ; isotypes: CBS 14519 T = NRRL Y-63865 T = ZIM 2626 T , MycoBank no.: MB 819608) is proposed for this highly divergent new yeast species.
Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data.

PubMed

Althammer, Sonja; González-Vallinas, Juan; Ballaré, Cecilia; Beato, Miguel; Eyras, Eduardo

2011-12-15

High-throughput sequencing (HTS) has revolutionized gene regulation studies and is now fundamental for the detection of protein-DNA and protein-RNA binding, as well as for measuring RNA expression. With increasing variety and sequencing depth of HTS datasets, the need for more flexible and memory-efficient tools to analyse them is growing. We describe Pyicos, a powerful toolkit for the analysis of mapped reads from diverse HTS experiments: ChIP-Seq, either punctuated or broad signals, CLIP-Seq and RNA-Seq. We prove the effectiveness of Pyicos to select for significant signals and show that its accuracy is comparable and sometimes superior to that of methods specifically designed for each particular type of experiment. Pyicos facilitates the analysis of a variety of HTS datatypes through its flexibility and memory efficiency, providing a useful framework for data integration into models of regulatory genomics. Open-source software, with tutorials and protocol files, is available at http://regulatorygenomics.upf.edu/pyicos or as a Galaxy server at http://regulatorygenomics.upf.edu/galaxy eduardo.eyras@upf.edu Supplementary data are available at Bioinformatics online.
ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.

PubMed

Gonzalez, Sergio; Clavijo, Bernardo; Rivarola, Máximo; Moreno, Patricio; Fernandez, Paula; Dopazo, Joaquín; Paniego, Norma

2017-02-22

In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species. We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results. ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .
Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

PubMed Central

McArt, Darragh G.; Dunne, Philip D.; Blayney, Jaine K.; Salto-Tellez, Manuel; Van Schaeybroeck, Sandra; Hamilton, Peter W.; Zhang, Shu-Dong

2013-01-01

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. PMID:23840550
RAG-3D: A search tool for RNA 3D substructures

DOE PAGES

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; ...

2015-08-24

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less
RAG-3D: a search tool for RNA 3D substructures

PubMed Central

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; Schlick, Tamar

2015-01-01

To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding. PMID:26304547
RAG-3D: A search tool for RNA 3D substructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less
RNA-Seq of Bacillus licheniformis: active regulatory RNA features expressed within a productive fermentation.

PubMed

Wiegand, Sandra; Dietrich, Sascha; Hertel, Robert; Bongaerts, Johannes; Evers, Stefan; Volland, Sonja; Daniel, Rolf; Liesegang, Heiko

2013-10-01

The production of enzymes by an industrial strain requires a complex adaption of the bacterial metabolism to the conditions within the fermenter. Regulatory events within the process result in a dynamic change of the transcriptional activity of the genome. This complex network of genes is orchestrated by proteins as well as regulatory RNA elements. Here we present an RNA-Seq based study considering selected phases of an industry-oriented fermentation of Bacillus licheniformis. A detailed analysis of 20 strand-specific RNA-Seq datasets revealed a multitude of transcriptionally active genomic regions. 3314 RNA features encoded by such active loci have been identified and sorted into ten functional classes. The identified sequences include the expected RNA features like housekeeping sRNAs, metabolic riboswitches and RNA switches well known from studies on Bacillus subtilis as well as a multitude of completely new candidates for regulatory RNAs. An unexpectedly high number of 855 RNA features are encoded antisense to annotated protein and RNA genes, in addition to 461 independently transcribed small RNAs. These antisense transcripts contain molecules with a remarkable size range variation from 38 to 6348 base pairs in length. The genome of the type strain B. licheniformis DSM13 was completely reannotated using data obtained from RNA-Seq analyses and from public databases. The hereby generated data-sets represent a solid amount of knowledge on the dynamic transcriptional activities during the investigated fermentation stages. The identified regulatory elements enable research on the understanding and the optimization of crucial metabolic activities during a productive fermentation of Bacillus licheniformis strains.
MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data.

PubMed

Simon, L M; Karg, S; Westermann, A J; Engel, M; Elbehery, A H A; Hense, B; Heinig, M; Deng, L; Theis, F J

2018-06-12

With the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts. We developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from >17,000 samples from >400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource. Our results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease. Additionally, codes to process new datasets and perform statistical analyses are made available at https://github.com/theislab/MetaMap.
Identification of Two Novel Amalgaviruses in the Common Eelgrass (Zostera marina) and in Silico Analysis of the Amalgavirus +1 Programmed Ribosomal Frameshifting Sites.

PubMed

Park, Dongbin; Goh, Chul Jun; Kim, Hyein; Hahn, Yoonsoo

2018-04-01

The genome sequences of two novel monopartite RNA viruses were identified in a common eelgrass ( Zostera marina ) transcriptome dataset. Sequence comparison and phylogenetic analyses revealed that these two novel viruses belong to the genus Amalgavirus in the family Amalgaviridae . They were named Zostera marina amalgavirus 1 (ZmAV1) and Zostera marina amalgavirus 2 (ZmAV2). Genomes of both ZmAV1 and ZmAV2 contain two overlapping open reading frames (ORFs). ORF1 encodes a putative replication factory matrix-like protein, while ORF2 encodes a RNA-dependent RNA polymerase (RdRp) domain. The fusion protein (ORF1+2) of ORF1 and ORF2, which mediates RNA replication, was produced using the +1 programmed ribosomal frameshifting (PRF) mechanism. The +1 PRF motif sequence, UUU_CGN, which is highly conserved among known amalgaviruses, was also found in ZmAV1 and ZmAV2. Multiple sequence alignment of the ORF1+2 fusion proteins from 24 amalgaviruses revealed that +1 PRF occurred only at three different positions within the 13-amino acid-long segment, which was surrounded by highly conserved regions on both sides. This suggested that the +1 PRF may be constrained by the structure of fusion proteins. Genome sequences of ZmAV1 and ZmAV2, which are the first viruses to be identified in common eelgrass, will serve as useful resources for studying evolution and diversity of amalgaviruses.
Identification of Two Novel Amalgaviruses in the Common Eelgrass (Zostera marina) and in Silico Analysis of the Amalgavirus +1 Programmed Ribosomal Frameshifting Sites

PubMed Central

Park, Dongbin; Goh, Chul Jun; Kim, Hyein; Hahn, Yoonsoo

2018-01-01

The genome sequences of two novel monopartite RNA viruses were identified in a common eelgrass (Zostera marina) transcriptome dataset. Sequence comparison and phylogenetic analyses revealed that these two novel viruses belong to the genus Amalgavirus in the family Amalgaviridae. They were named Zostera marina amalgavirus 1 (ZmAV1) and Zostera marina amalgavirus 2 (ZmAV2). Genomes of both ZmAV1 and ZmAV2 contain two overlapping open reading frames (ORFs). ORF1 encodes a putative replication factory matrix-like protein, while ORF2 encodes a RNA-dependent RNA polymerase (RdRp) domain. The fusion protein (ORF1+2) of ORF1 and ORF2, which mediates RNA replication, was produced using the +1 programmed ribosomal frameshifting (PRF) mechanism. The +1 PRF motif sequence, UUU_CGN, which is highly conserved among known amalgaviruses, was also found in ZmAV1 and ZmAV2. Multiple sequence alignment of the ORF1+2 fusion proteins from 24 amalgaviruses revealed that +1 PRF occurred only at three different positions within the 13-amino acid-long segment, which was surrounded by highly conserved regions on both sides. This suggested that the +1 PRF may be constrained by the structure of fusion proteins. Genome sequences of ZmAV1 and ZmAV2, which are the first viruses to be identified in common eelgrass, will serve as useful resources for studying evolution and diversity of amalgaviruses. PMID:29628822
Identification of 15 candidate structured noncoding RNA motifs in fungi by comparative genomics.

PubMed

Li, Sanshu; Breaker, Ronald R

2017-10-13

With the development of rapid and inexpensive DNA sequencing, the genome sequences of more than 100 fungal species have been made available. This dataset provides an excellent resource for comparative genomics analyses, which can be used to discover genetic elements, including noncoding RNAs (ncRNAs). Bioinformatics tools similar to those used to uncover novel ncRNAs in bacteria, likewise, should be useful for searching fungal genomic sequences, and the relative ease of genetic experiments with some model fungal species could facilitate experimental validation studies. We have adapted a bioinformatics pipeline for discovering bacterial ncRNAs to systematically analyze many fungal genomes. This comparative genomics pipeline integrates information on conserved RNA sequence and structural features with alternative splicing information to reveal fungal RNA motifs that are candidate regulatory domains, or that might have other possible functions. A total of 15 prominent classes of structured ncRNA candidates were identified, including variant HDV self-cleaving ribozyme representatives, atypical snoRNA candidates, and possible structured antisense RNA motifs. Candidate regulatory motifs were also found associated with genes for ribosomal proteins, S-adenosylmethionine decarboxylase (SDC), amidase, and HexA protein involved in Woronin body formation. We experimentally confirm that the variant HDV ribozymes undergo rapid self-cleavage, and we demonstrate that the SDC RNA motif reduces the expression of SAM decarboxylase by translational repression. Furthermore, we provide evidence that several other motifs discovered in this study are likely to be functional ncRNA elements. Systematic screening of fungal genomes using a computational discovery pipeline has revealed the existence of a variety of novel structured ncRNAs. Genome contexts and similarities to known ncRNA motifs provide strong evidence for the biological and biochemical functions of some newly found ncRNA motifs. Although initial examinations of several motifs provide evidence for their likely functions, other motifs will require more in-depth analysis to reveal their functions.

Complete Mitochondrial Genome of Echinostoma hortense (Digenea: Echinostomatidae).

PubMed

Liu, Ze-Xuan; Zhang, Yan; Liu, Yu-Ting; Chang, Qiao-Cheng; Su, Xin; Fu, Xue; Yue, Dong-Mei; Gao, Yuan; Wang, Chun-Ren

2016-04-01

Echinostoma hortense (Digenea: Echinostomatidae) is one of the intestinal flukes with medical importance in humans. However, the mitochondrial (mt) genome of this fluke has not been known yet. The present study has determined the complete mt genome sequences of E. hortense and assessed the phylogenetic relationships with other digenean species for which the complete mt genome sequences are available in GenBank using concatenated amino acid sequences inferred from 12 protein-coding genes. The mt genome of E. hortense contained 12 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and 1 non-coding region. The length of the mt genome of E. hortense was 14,994 bp, which was somewhat smaller than those of other trematode species. Phylogenetic analyses based on concatenated nucleotide sequence datasets for all 12 protein-coding genes using maximum parsimony (MP) method showed that E. hortense and Hypoderaeum conoideum gathered together, and they were closer to each other than to Fasciolidae and other echinostomatid trematodes. The availability of the complete mt genome sequences of E. hortense provides important genetic markers for diagnostics, population genetics, and evolutionary studies of digeneans.
Complete Mitochondrial Genome of Echinostoma hortense (Digenea: Echinostomatidae)

PubMed Central

Liu, Ze-Xuan; Zhang, Yan; Liu, Yu-Ting; Chang, Qiao-Cheng; Su, Xin; Fu, Xue; Yue, Dong-Mei; Gao, Yuan; Wang, Chun-Ren

2016-01-01

Echinostoma hortense (Digenea: Echinostomatidae) is one of the intestinal flukes with medical importance in humans. However, the mitochondrial (mt) genome of this fluke has not been known yet. The present study has determined the complete mt genome sequences of E. hortense and assessed the phylogenetic relationships with other digenean species for which the complete mt genome sequences are available in GenBank using concatenated amino acid sequences inferred from 12 protein-coding genes. The mt genome of E. hortense contained 12 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and 1 non-coding region. The length of the mt genome of E. hortense was 14,994 bp, which was somewhat smaller than those of other trematode species. Phylogenetic analyses based on concatenated nucleotide sequence datasets for all 12 protein-coding genes using maximum parsimony (MP) method showed that E. hortense and Hypoderaeum conoideum gathered together, and they were closer to each other than to Fasciolidae and other echinostomatid trematodes. The availability of the complete mt genome sequences of E. hortense provides important genetic markers for diagnostics, population genetics, and evolutionary studies of digeneans. PMID:27180575
Characterization of the complete mitochondrial genome of Marshallagia marshalli and phylogenetic implications for the superfamily Trichostrongyloidea.

PubMed

Sun, Miao-Miao; Han, Liang; Zhang, Fu-Kai; Zhou, Dong-Hui; Wang, Shu-Qing; Ma, Jun; Zhu, Xing-Quan; Liu, Guo-Hua

2018-01-01

Marshallagia marshalli (Nematoda: Trichostrongylidae) infection can lead to serious parasitic gastroenteritis in sheep, goat, and wild ruminant, causing significant socioeconomic losses worldwide. Up to now, the study concerning the molecular biology of M. marshalli is limited. Herein, we sequenced the complete mitochondrial (mt) genome of M. marshalli and examined its phylogenetic relationship with selected members of the superfamily Trichostrongyloidea using Bayesian inference (BI) based on concatenated mt amino acid sequence datasets. The complete mt genome sequence of M. marshalli is 13,891 bp, including 12 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. All protein-coding genes are transcribed in the same direction. Phylogenetic analyses based on concatenated amino acid sequences of the 12 protein-coding genes supported the monophylies of the families Haemonchidae, Molineidae, and Dictyocaulidae with strong statistical support, but rejected the monophyly of the family Trichostrongylidae. The determination of the complete mt genome sequence of M. marshalli provides novel genetic markers for studying the systematics, population genetics, and molecular epidemiology of M. marshalli and its congeners.
Deep sequencing of small RNA repertoires in mice reveals metabolic disorders-associated hepatic miRNAs.

PubMed

Liang, Tingming; Liu, Chang; Ye, Zhenchao

2013-01-01

Obesity and associated metabolic disorders contribute importantly to the metabolic syndrome. On the other hand, microRNAs (miRNAs) are a class of small non-coding RNAs that repress target gene expression by inducing mRNA degradation and/or translation repression. Dysregulation of specific miRNAs in obesity may influence energy metabolism and cause insulin resistance, which leads to dyslipidemia, steatosis hepatis and type 2 diabetes. In the present study, we comprehensively analyzed and validated dysregulated miRNAs in ob/ob mouse liver, as well as miRNA groups based on miRNA gene cluster and gene family by using deep sequencing miRNA datasets. We found that over 13.8% of the total analyzed miRNAs were dysregulated, of which 37 miRNA species showed significantly differential expression. Further RT-qPCR analysis in some selected miRNAs validated the similar expression patterns observed in deep sequencing. Interestingly, we found that miRNA gene cluster and family always showed consistent dysregulation patterns in ob/ob mouse liver, although they had various enrichment levels. Functional enrichment analysis revealed the versatile physiological roles (over six signal pathways and five human diseases) of these miRNAs. Biological studies indicated that overexpression of miR-126 or inhibition of miR-24 in AML-12 cells attenuated free fatty acids-induced fat accumulation. Taken together, our data strongly suggest that obesity and metabolic disturbance are tightly associated with functional miRNAs. We also identified hepatic miRNA candidates serving as potential biomarkers for the diagnose of the metabolic syndrome.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Jun-Hao; Liu, Shun; Zheng, Ling-Ling

Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein–lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP–lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulatemore » gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP–lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.« less
Combined molecular and morphological phylogenetic analyses of the New Zealand wolf spider genus Anoteropsis (Araneae: Lycosidae).

PubMed

Vink, Cor J; Paterson, Adrian M

2003-09-01

Datasets from the mitochondrial gene regions NADH dehydrogenase subunit I (ND1) and cytochrome c oxidase subunit I (COI) of the 20 species in the New Zealand wolf spider (Lycosidae) genus Anoteropsis were generated. Sequence data were phylogenetically analysed using parsimony and maximum likelihood analyses. The phylogenies generated from the ND1 and COI sequence data and a previously generated morphological dataset were significantly congruent (p<0.001). Sequence data were combined with morphological data and phylogenetically analysed using parsimony. The ND1 region sequenced included part of tRNA(Leu(CUN)), which appears to have an unstable amino-acyl arm and no TpsiC arm in lycosids. Analyses supported the existence of five species groups within Anoteropsis and the monophyly of species represented by multiple samples. A radiation of Anoteropsis species within the last five million years is inferred from the ND1 and COI likelihood phylograms, habitat and geological data, which also indicates that Anoteropsis arrived in New Zealand some time after it separated from Gondwana.
Changes in the Plasticity of HIV-1 Nef RNA during the Evolution of the North American Epidemic

PubMed Central

Manzourolajdad, Amirhossein; Gonzalez, Mileidy; Spouge, John L.

2016-01-01

Because of a high mutation rate, HIV exists as a viral swarm of many sequence variants evolving under various selective pressures from the human immune system. Although the Nef gene codes for the most immunogenic of HIV accessory proteins, which alone makes it of great interest to HIV research, it also encodes an RNA structure, whose contribution to HIV virulence has been largely unexplored. Nef RNA helps HIV escape RNA interference (RNAi) through nucleotide changes and alternative folding. This study examines Historic and Modern Datasets of patient HIV-1 Nef sequences during the evolution of the North American epidemic for local changes in RNA plasticity. By definition, RNA plasticity refers to an RNA molecule’s ability to take alternative folds (i.e., alternative conformations). Our most important finding is that an evolutionarily conserved region of the HIV-1 Nef gene, which we denote by R2, recently underwent a statistically significant increase in its RNA plasticity. Thus, our results indicate that Modern Nef R2 typically accommodates an alternative fold more readily than Historic Nef R2. Moreover, the increase in RNA plasticity resides mostly in synonymous nucleotide changes, which cannot be a response to selective pressures on the Nef protein. R2 may therefore be of interest in the development of antiviral RNAi therapies. PMID:27685447
Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains.

PubMed

Liao, Weinan; Ren, Jie; Wang, Kun; Wang, Shun; Zeng, Feng; Wang, Ying; Sun, Fengzhu

2016-11-23

The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microbial communities. However, in FOMC, the number of parameters grows exponentially with the increase of the order of Markov Chain (MC). Under a fixed high order of MC, the parameters might not be accurately estimated owing to the limitation of sequencing depth. In our study, we investigate an alternative to FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data. The VLMC originally designed for long sequences was extended to apply to high-throughput sequencing reads and the strategies to estimate the corresponding parameters were developed. The flexible number of parameters in VLMC avoids estimating the vast number of parameters of high-order MC under limited sequencing depth. Different from the manual selection in FOMC, VLMC determines the MC order adaptively. Several beta diversity measures based on VLMC were applied to compare the bacterial RNA-Seq and metatranscriptomic datasets. Experiments show that VLMC outperforms FOMC to model the background sequences in transcriptomic and metatranscriptomic samples. A software pipeline is available at https://d2vlmc.codeplex.com.
Assessing the prevalence of mycoplasma contamination in cell culture via a survey of NCBI's RNA-seq archive

PubMed Central

Olarerin-George, Anthony O.; Hogenesch, John B.

2015-01-01

Mycoplasmas are notorious contaminants of cell culture and can have profound effects on host cell biology by depriving cells of nutrients and inducing global changes in gene expression. Over the last two decades, sentinel testing has revealed wide-ranging contamination rates in mammalian culture. To obtain an unbiased assessment from hundreds of labs, we analyzed sequence data from 9395 rodent and primate samples from 884 series in the NCBI Sequence Read Archive. We found 11% of these series were contaminated (defined as ≥100 reads/million mapping to mycoplasma in one or more samples). Ninety percent of mycoplasma-mapped reads aligned to ribosomal RNA. This was unexpected given 37% of contaminated series used poly(A)-selection for mRNA enrichment. Lastly, we examined the relationship between mycoplasma contamination and host gene expression in a single cell RNA-seq dataset and found 61 host genes (P < 0.001) were significantly associated with mycoplasma-mapped read counts. In all, this study suggests mycoplasma contamination is still prevalent today and poses substantial risk to research quality. PMID:25712092
Mining and Development of Novel SSR Markers Using Next Generation Sequencing (NGS) Data in Plants.

PubMed

Taheri, Sima; Lee Abdullah, Thohirah; Yusop, Mohd Rafii; Hanafi, Mohamed Musa; Sahebi, Mahbod; Azizi, Parisa; Shamshiri, Redmond Ramin

2018-02-13

Microsatellites, or simple sequence repeats (SSRs), are one of the most informative and multi-purpose genetic markers exploited in plant functional genomics. However, the discovery of SSRs and development using traditional methods are laborious, time-consuming, and costly. Recently, the availability of high-throughput sequencing technologies has enabled researchers to identify a substantial number of microsatellites at less cost and effort than traditional approaches. Illumina is a noteworthy transcriptome sequencing technology that is currently used in SSR marker development. Although 454 pyrosequencing datasets can be used for SSR development, this type of sequencing is no longer supported. This review aims to present an overview of the next generation sequencing, with a focus on the efficient use of de novo transcriptome sequencing (RNA-Seq) and related tools for mining and development of microsatellites in plants.
ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

PubMed Central

Gardeux, Vincent; David, Fabrice P. A.; Shajkofci, Adrian; Schwalie, Petra C.; Deplancke, Bart

2017-01-01

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. Results We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. Availability and implementation The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. Contact bart.deplancke@epfl.ch Supplementary information Supplementary data are available at Bioinformatics online. PMID:28541377
ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

PubMed

Gardeux, Vincent; David, Fabrice P A; Shajkofci, Adrian; Schwalie, Petra C; Deplancke, Bart

2017-10-01

Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. bart.deplancke@epfl.ch. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.
Quantitative RNA-seq analysis of the Campylobacter jejuni transcriptome

PubMed Central

Chaudhuri, Roy R.; Yu, Lu; Kanji, Alpa; Perkins, Timothy T.; Gardner, Paul P.; Choudhary, Jyoti; Maskell, Duncan J.

2011-01-01

Campylobacter jejuni is the most common bacterial cause of foodborne disease in the developed world. Its general physiology and biochemistry, as well as the mechanisms enabling it to colonize and cause disease in various hosts, are not well understood, and new approaches are required to understand its basic biology. High-throughput sequencing technologies provide unprecedented opportunities for functional genomic research. Recent studies have shown that direct Illumina sequencing of cDNA (RNA-seq) is a useful technique for the quantitative and qualitative examination of transcriptomes. In this study we report RNA-seq analyses of the transcriptomes of C. jejuni (NCTC11168) and its rpoN mutant. This has allowed the identification of hitherto unknown transcriptional units, and further defines the regulon that is dependent on rpoN for expression. The analysis of the NCTC11168 transcriptome was supplemented by additional proteomic analysis using liquid chromatography-MS. The transcriptomic and proteomic datasets represent an important resource for the Campylobacter research community. PMID:21816880
Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles.

PubMed

Piao, Yongjun; Piao, Minghao; Ryu, Keun Ho

2017-01-01

Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data. In this article, we propose a feature subset-based ensemble method in which each model is learned from a different projection of the original feature space to classify multiple cancers. In our method, the feature relevance and redundancy are considered to generate multiple feature subsets, the base classifiers are learned from each independent miRNA subset, and the average posterior probability is used to combine the base classifiers. To test the performance of our method, we used bead-based and sequence-based miRNA expression datasets and conducted 10-fold and leave-one-out cross validations. The experimental results show that the proposed method yields good results and has higher prediction accuracy than popular ensemble methods. The Java program and source code of the proposed method and the datasets in the experiments are freely available at https://sourceforge.net/projects/mirna-ensemble/. Copyright © 2016 Elsevier Ltd. All rights reserved.
Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins

PubMed Central

Turnbaugh, Peter J.; Quince, Christopher; Faith, Jeremiah J.; McHardy, Alice C.; Yatsunenko, Tanya; Niazi, Faheem; Affourtit, Jason; Egholm, Michael; Henrissat, Bernard; Knight, Rob; Gordon, Jeffrey I.

2010-01-01

We deeply sampled the organismal, genetic, and transcriptional diversity in fecal samples collected from a monozygotic (MZ) twin pair and compared the results to 1,095 communities from the gut and other body habitats of related and unrelated individuals. Using a new scheme for noise reduction in pyrosequencing data, we estimated the total diversity of species-level bacterial phylotypes in the 1.2-1.5 million bacterial 16S rRNA reads obtained from each deeply sampled cotwin to be ~800 (35.9%, 49.1% detected in both). A combined 1.1 million read 16S rRNA dataset representing 281 shallowly sequenced fecal samples from 54 twin pairs and their mothers contained an estimated 4,018 species-level phylotypes, with each sample having a unique species assemblage (53.4 ± 0.6% and 50.3 ± 0.5% overlap with the deeply sampled cotwins). Of the 134 phylotypes with a relative abundance of >0.1% in the combined dataset, only 37 appeared in >50% of the samples, with one phylotype in the Lachnospiraceae family present in 99%. Nongut communities had significantly reduced overlap with the deeply sequenced twins’ fecal microbiota (18.3 ± 0.3%, 15.3 ± 0.3%). The MZ cotwins’ fecal DNA was deeply sequenced (3.8-6.3 Gbp/sample) and assembled reads were assigned to 25 genus-level phylogenetic bins. Only 17% of the genes in these bins were shared between the cotwins. Bins exhibited differences in their degree of sequence variation, gene content including the repertoire of carbohydrate active enzymes present within and between twins (e.g., predicted cellulases, dockerins), and transcriptional activities. These results provide an expanded perspective about features that make each of us unique life forms and directions for future characterization of our gut ecosystems. PMID:20363958
Genome-wide identification of alternate bearing-associated microRNAs (miRNAs) in olive (Olea europaea L.)

PubMed Central

2013-01-01

Background Alternate bearing is a widespread phenomenon among crop plants, defined as the tendency of certain fruit trees to produce a high-yield crop one year ("on-year"), followed by a low-yield or even no crop the following year ("off-year"). Several factors may affect the balance between such developmental phase-transition processes. Among them are the microRNA (miRNA), being gene-expression regulators that have been found to be involved as key determinants in several physiological processes. Results Six olive (Olea europaea L. cv. Ayvalik variety) small RNA libraries were constructed from fruits (ripe and unripe) and leaves (”on year” and ”off year” leaves in July and in November, respectively) and sequenced by high-throughput Illumina sequencing. The RNA was retrotranscribed and sequenced using the high-throughput Illumina platform. Bioinformatics analyses of 93,526,915 reads identified 135 conserved miRNA, belonging to 22 miRNA families in the olive. In addition, 38 putative novel miRNAs were discovered in the datasets. Expression of olive tree miRNAs varied greatly among the six libraries, indicating the contribution of diverse miRNA in balancing between reproductive and vegetative phases. Predicted targets of miRNA were categorized into 108 process ontology groups with significance abundance. Among those, potential alternate bearing-associated processes were found, such as development, hormone-mediated signaling and organ morphogenesis. The KEGG analyses revealed that the miRNA-targeted genes are involved in seven main pathways, belonging to carbohydrate metabolism and hormone signal-transduction pathways. Conclusion A comprehensive study on olive miRNA related to alternate bearing was performed. Regulation of miRNA under different developmental phases and tissues indicated that control of nutrition and hormone, together with flowering processes had a noteworthy impact on the olive tree alternate bearing. Our results also provide significant data on the miRNA-fruit development interaction and advance perspectives in the miRNA profile of the olive tree. PMID:23320600
TAM: a method for enrichment and depletion analysis of a microRNA category in a list of microRNAs.

PubMed

Lu, Ming; Shi, Bing; Wang, Juan; Cao, Qun; Cui, Qinghua

2010-08-09

MicroRNAs (miRNAs) are a class of important gene regulators. The number of identified miRNAs has been increasing dramatically in recent years. An emerging major challenge is the interpretation of the genome-scale miRNA datasets, including those derived from microarray and deep-sequencing. It is interesting and important to know the common rules or patterns behind a list of miRNAs, (i.e. the deregulated miRNAs resulted from an experiment of miRNA microarray or deep-sequencing). For the above purpose, this study presents a method and develops a tool (TAM) for annotations of meaningful human miRNAs categories. We first integrated miRNAs into various meaningful categories according to prior knowledge, such as miRNA family, miRNA cluster, miRNA function, miRNA associated diseases, and tissue specificity. Using TAM, given lists of miRNAs can be rapidly annotated and summarized according to the integrated miRNA categorical data. Moreover, given a list of miRNAs, TAM can be used to predict novel related miRNAs. Finally, we confirmed the usefulness and reliability of TAM by applying it to deregulated miRNAs in acute myocardial infarction (AMI) from two independent experiments. TAM can efficiently identify meaningful categories for given miRNAs. In addition, TAM can be used to identify novel miRNA biomarkers. TAM tool, source codes, and miRNA category data are freely available at http://cmbi.bjmu.edu.cn/tam.
RnaSeqSampleSize: real data based sample size estimation for RNA sequencing.

PubMed

Zhao, Shilin; Li, Chung-I; Guo, Yan; Sheng, Quanhu; Shyr, Yu

2018-05-30

One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/ . RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.
Integrated genome browser: visual analytics platform for genomics.

PubMed

Freese, Nowlan H; Norris, David C; Loraine, Ann E

2016-07-15

Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB's ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. IGB is open source and is freely available from http://bioviz.org/igb aloraine@uncc.edu. © The Author 2016. Published by Oxford University Press.
PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse

PubMed Central

Reuter, Kerstin; Helms, Volkhard

2016-01-01

Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non–AUG codons located in the annotated 5’ UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in–frame and out–of–frame translational start sites within the 5’ UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near–cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of in silico mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice PreTIS visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. PreTIS is accessible at http://service.bioinformatik.uni-saarland.de/pretis. PMID:27768687

Metagenomic ventures into outer sequence space.

PubMed

Dutilh, Bas E

Sequencing DNA or RNA directly from the environment often results in many sequencing reads that have no homologs in the database. These are referred to as "unknowns," and reflect the vast unexplored microbial sequence space of our biosphere, also known as "biological dark matter." However, unknowns also exist because metagenomic datasets are not optimally mined. There is a pressure on researchers to publish and move on, and the unknown sequences are often left for what they are, and conclusions drawn based on reads with annotated homologs. This can cause abundant and widespread genomes to be overlooked, such as the recently discovered human gut bacteriophage crAssphage. The unknowns may be enriched for bacteriophage sequences, the most abundant and genetically diverse component of the biosphere and of sequence space. However, it remains an open question, what is the actual size of biological sequence space? The de novo assembly of shotgun metagenomes is the most powerful tool to address this question.
Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.

PubMed

Liu, Bin; Wu, Hao; Zhang, Deyuan; Wang, Xiaolong; Chou, Kuo-Chen

2017-02-21

To expedite the pace in conducting genome/proteome analysis, we have developed a Python package called Pse-Analysis. The powerful package can automatically complete the following five procedures: (1) sample feature extraction, (2) optimal parameter selection, (3) model training, (4) cross validation, and (5) evaluating prediction quality. All the work a user needs to do is to input a benchmark dataset along with the query biological sequences concerned. Based on the benchmark dataset, Pse-Analysis will automatically construct an ideal predictor, followed by yielding the predicted results for the submitted query samples. All the aforementioned tedious jobs can be automatically done by the computer. Moreover, the multiprocessing technique was adopted to enhance computational speed by about 6 folds. The Pse-Analysis Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/Pse-Analysis/, and can be directly run on Windows, Linux, and Unix.
IM-TORNADO: a tool for comparison of 16S reads from paired-end libraries.

PubMed

Jeraldo, Patricio; Kalari, Krishna; Chen, Xianfeng; Bhavsar, Jaysheel; Mangalam, Ashutosh; White, Bryan; Nelson, Heidi; Kocher, Jean-Pierre; Chia, Nicholas

2014-01-01

16S rDNA hypervariable tag sequencing has become the de facto method for accessing microbial diversity. Illumina paired-end sequencing, which produces two separate reads for each DNA fragment, has become the platform of choice for this application. However, when the two reads do not overlap, existing computational pipelines analyze data from read separately and underutilize the information contained in the paired-end reads. We created a workflow known as Illinois Mayo Taxon Organization from RNA Dataset Operations (IM-TORNADO) for processing non-overlapping reads while retaining maximal information content. Using synthetic mock datasets, we show that the use of both reads produced answers with greater correlation to those from full length 16S rDNA when looking at taxonomy, phylogeny, and beta-diversity. IM-TORNADO is freely available at http://sourceforge.net/projects/imtornado and produces BIOM format output for cross compatibility with other pipelines such as QIIME, mothur, and phyloseq.
Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data

PubMed Central

Althammer, Sonja; González-Vallinas, Juan; Ballaré, Cecilia; Beato, Miguel; Eyras, Eduardo

2011-01-01

Motivation: High-throughput sequencing (HTS) has revolutionized gene regulation studies and is now fundamental for the detection of protein–DNA and protein–RNA binding, as well as for measuring RNA expression. With increasing variety and sequencing depth of HTS datasets, the need for more flexible and memory-efficient tools to analyse them is growing. Results: We describe Pyicos, a powerful toolkit for the analysis of mapped reads from diverse HTS experiments: ChIP-Seq, either punctuated or broad signals, CLIP-Seq and RNA-Seq. We prove the effectiveness of Pyicos to select for significant signals and show that its accuracy is comparable and sometimes superior to that of methods specifically designed for each particular type of experiment. Pyicos facilitates the analysis of a variety of HTS datatypes through its flexibility and memory efficiency, providing a useful framework for data integration into models of regulatory genomics. Availability: Open-source software, with tutorials and protocol files, is available at http://regulatorygenomics.upf.edu/pyicos or as a Galaxy server at http://regulatorygenomics.upf.edu/galaxy Contact: eduardo.eyras@upf.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21994224
Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells.

PubMed

Xin, Yurong; Kim, Jinrang; Ni, Min; Wei, Yi; Okamoto, Haruka; Lee, Joseph; Adler, Christina; Cavino, Katie; Murphy, Andrew J; Yancopoulos, George D; Lin, Hsin Chieh; Gromada, Jesper

2016-03-22

This study provides an assessment of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. The system combines microfluidic technology and nanoliter-scale reactions. We sequenced 622 cells, allowing identification of 341 islet cells with high-quality gene expression profiles. The cells clustered into populations of α-cells (5%), β-cells (92%), δ-cells (1%), and pancreatic polypeptide cells (2%). We identified cell-type-specific transcription factors and pathways primarily involved in nutrient sensing and oxidation and cell signaling. Unexpectedly, 281 cells had to be removed from the analysis due to low viability, low sequencing quality, or contamination resulting in the detection of more than one islet hormone. Collectively, we provide a resource for identification of high-quality gene expression datasets to help expand insights into genes and pathways characterizing islet cell types. We reveal limitations in the C1 Fluidigm cell capture process resulting in contaminated cells with altered gene expression patterns. This calls for caution when interpreting single-cell transcriptomics data using the C1 Fluidigm system.
Impact of target mRNA structure on siRNA silencing efficiency: A large-scale study.

PubMed

Gredell, Joseph A; Berger, Angela K; Walton, S Patrick

2008-07-01

The selection of active siRNAs is generally based on identifying siRNAs with certain sequence and structural properties. However, the efficiency of RNA interference has also been shown to depend on the structure of the target mRNA, primarily through studies using exogenous transcripts with well-defined secondary structures in the vicinity of the target sequence. While these studies provide a means for examining the impact of target sequence and structure independently, the predicted secondary structures for these transcripts are often not reflective of structures that form in full-length, native mRNAs where interactions can occur between relatively remote segments of the mRNAs. Here, using a combination of experimental results and analysis of a large dataset, we demonstrate that the accessibility of certain local target structures on the mRNA is an important determinant in the gene silencing ability of siRNAs. siRNAs targeting the enhanced green fluorescent protein were chosen using a minimal siRNA selection algorithm followed by classification based on the predicted minimum free energy structures of the target transcripts. Transfection into HeLa and HepG2 cells revealed that siRNAs targeting regions of the mRNA predicted to have unpaired 5'- and 3'-ends resulted in greater gene silencing than regions predicted to have other types of secondary structure. These results were confirmed by analysis of gene silencing data from previously published siRNAs, which showed that mRNA target regions unpaired at either the 5'-end or 3'-end were silenced, on average, approximately 10% more strongly than target regions unpaired in the center or primarily paired throughout. We found this effect to be independent of the structure of the siRNA guide strand. Taken together, these results suggest minimal requirements for nucleation of hybridization between the siRNA guide strand and mRNA and that both mRNA and guide strand structure should be considered when choosing candidate siRNAs. (c) 2008 Wiley Periodicals, Inc.
Impact of target mRNA structure on siRNA silencing efficiency: a large-scale study

PubMed Central

Gredell, Joseph A.; Berger, Angela K.; Walton, S. Patrick

2009-01-01

The selection of active siRNAs is generally based on identifying siRNAs with certain sequence and structural properties. However, the efficiency of RNA interference has also been shown to depend on the structure of the target mRNA, primarily through studies using exogenous transcripts with well-defined secondary structures in the vicinity of the target sequence. While these studies provide a means for examining the impact of target sequence and structure independently, the predicted secondary structures for these transcripts are often not reflective of structures that form in full-length, native mRNAs where interactions can occur between relatively remote segments of the mRNAs. Here, using a combination of experimental results and analysis of a large dataset, we demonstrate that the accessibility of certain local target structures on the mRNA is an important determinant in the gene silencing ability of siRNAs. siRNAs targeting the enhanced green fluorescent protein were chosen using a minimal siRNA selection algorithm followed by classification based on the predicted minimum free energy structures of the target transcripts. Transfection into HeLa and HepG2 cells revealed that siRNAs targeting regions of the mRNA predicted to have unpaired 5’- and 3’-ends resulted in greater gene silencing than regions predicted to have other types of secondary structure. These results were confirmed by analysis of gene silencing data from previously published siRNAs, which showed that mRNA target regions unpaired at either the 5’-end or 3’-end were silenced, on average, ~10% more strongly than target regions unpaired in the center or primarily paired throughout. We found this effect to be independent of the structure of the siRNA guide strand. Taken together, these results suggest minimal requirements for nucleation of hybridization between the siRNA guide strand and mRNA and that both mRNA and guide strand structure should be considered when choosing candidate siRNAs. PMID:18306428
GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

PubMed Central

Di, Yanming; Schafer, Daniel W.; Wilhelm, Larry J.; Fox, Samuel E.; Sullivan, Christopher M.; Curzon, Aron D.; Carrington, James C.; Mockler, Todd C.; Chang, Jeff H.

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts. PMID:21998647
Wanted dead or alive? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications

PubMed Central

Zaiko, Anastasija; Fletcher, Lauren M.; Laroche, Olivier; Wood, Susanna A.

2017-01-01

High-throughput sequencing metabarcoding studies in marine biosecurity have largely focused on targeting environmental DNA (eDNA). DNA can persist extracellularly in the environment, making discrimination of living organisms difficult. In this study, bilge water samples (i.e., water accumulating on-board a vessel during transit) were collected from 15 small recreational and commercial vessels. eDNA and eRNA molecules were co-extracted and the V4 region of the 18S ribosomal RNA gene targeted for metabarcoding. In total, 62.7% of the Operational Taxonomic Units (OTUs) were identified at least once in the corresponding eDNA and eRNA reads, with 19.5% unique to eDNA and 17.7% to eRNA. There were substantial differences in diversity between molecular compartments; 57% of sequences from eDNA-only OTUs belonged to fungi, likely originating from legacy DNA. In contrast, there was a higher percentage of metazoan (50.2%) and ciliate (31.7%) sequences in the eRNA-only OTUs. Our data suggest that the presence of eRNA-only OTUs could be due to increased cellular activities of some rare taxa that were not identified in the eDNA datasets, unusually high numbers of rRNA transcripts in ciliates, and/or artefacts produced during the reverse transcriptase, PCR and sequencing steps. The proportions of eDNA/eRNA shared and unshared OTUs were highly heterogeneous within individual bilge water samples. Multiple factors including boat type and the activities performed on-board, such as washing of scientific equipment, may play a major role in contributing to this variability. For some marine biosecurity applications analysis, eDNA-only data may be sufficient, however there are an increasing number of instances where distinguishing the living portion of a community is essential. For these circumstances, we suggest only including OTUs that are present in both eDNA and eRNA data. OTUs found only in the eRNA data need to be interpreted with caution until further research provides conclusive evidence for their origin. PMID:29095959
Wanted dead or alive? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications.

PubMed

Pochon, Xavier; Zaiko, Anastasija; Fletcher, Lauren M; Laroche, Olivier; Wood, Susanna A

2017-01-01

High-throughput sequencing metabarcoding studies in marine biosecurity have largely focused on targeting environmental DNA (eDNA). DNA can persist extracellularly in the environment, making discrimination of living organisms difficult. In this study, bilge water samples (i.e., water accumulating on-board a vessel during transit) were collected from 15 small recreational and commercial vessels. eDNA and eRNA molecules were co-extracted and the V4 region of the 18S ribosomal RNA gene targeted for metabarcoding. In total, 62.7% of the Operational Taxonomic Units (OTUs) were identified at least once in the corresponding eDNA and eRNA reads, with 19.5% unique to eDNA and 17.7% to eRNA. There were substantial differences in diversity between molecular compartments; 57% of sequences from eDNA-only OTUs belonged to fungi, likely originating from legacy DNA. In contrast, there was a higher percentage of metazoan (50.2%) and ciliate (31.7%) sequences in the eRNA-only OTUs. Our data suggest that the presence of eRNA-only OTUs could be due to increased cellular activities of some rare taxa that were not identified in the eDNA datasets, unusually high numbers of rRNA transcripts in ciliates, and/or artefacts produced during the reverse transcriptase, PCR and sequencing steps. The proportions of eDNA/eRNA shared and unshared OTUs were highly heterogeneous within individual bilge water samples. Multiple factors including boat type and the activities performed on-board, such as washing of scientific equipment, may play a major role in contributing to this variability. For some marine biosecurity applications analysis, eDNA-only data may be sufficient, however there are an increasing number of instances where distinguishing the living portion of a community is essential. For these circumstances, we suggest only including OTUs that are present in both eDNA and eRNA data. OTUs found only in the eRNA data need to be interpreted with caution until further research provides conclusive evidence for their origin.
Comprehensive analysis of coding-lncRNA gene co-expression network uncovers conserved functional lncRNAs in zebrafish.

PubMed

Chen, Wen; Zhang, Xuan; Li, Jing; Huang, Shulan; Xiang, Shuanglin; Hu, Xiang; Liu, Changning

2018-05-09

Zebrafish is a full-developed model system for studying development processes and human disease. Recent studies of deep sequencing had discovered a large number of long non-coding RNAs (lncRNAs) in zebrafish. However, only few of them had been functionally characterized. Therefore, how to take advantage of the mature zebrafish system to deeply investigate the lncRNAs' function and conservation is really intriguing. We systematically collected and analyzed a series of zebrafish RNA-seq data, then combined them with resources from known database and literatures. As a result, we obtained by far the most complete dataset of zebrafish lncRNAs, containing 13,604 lncRNA genes (21,128 transcripts) in total. Based on that, a co-expression network upon zebrafish coding and lncRNA genes was constructed and analyzed, and used to predict the Gene Ontology (GO) and the KEGG annotation of lncRNA. Meanwhile, we made a conservation analysis on zebrafish lncRNA, identifying 1828 conserved zebrafish lncRNA genes (1890 transcripts) that have their putative mammalian orthologs. We also found that zebrafish lncRNAs play important roles in regulation of the development and function of nervous system; these conserved lncRNAs present a significant sequential and functional conservation, with their mammalian counterparts. By integrative data analysis and construction of coding-lncRNA gene co-expression network, we gained the most comprehensive dataset of zebrafish lncRNAs up to present, as well as their systematic annotations and comprehensive analyses on function and conservation. Our study provides a reliable zebrafish-based platform to deeply explore lncRNA function and mechanism, as well as the lncRNA commonality between zebrafish and human.
Web-Beagle: a web server for the alignment of RNA secondary structures.

PubMed

Mattei, Eugenio; Pietrosanto, Marco; Ferrè, Fabrizio; Helmer-Citterich, Manuela

2015-07-01

Web-Beagle (http://beagle.bio.uniroma2.it) is a web server for the pairwise global or local alignment of RNA secondary structures. The server exploits a new encoding for RNA secondary structure and a substitution matrix of RNA structural elements to perform RNA structural alignments. The web server allows the user to compute up to 10 000 alignments in a single run, taking as input sets of RNA sequences and structures or primary sequences alone. In the latter case, the server computes the secondary structure prediction for the RNAs on-the-fly using RNAfold (free energy minimization). The user can also compare a set of input RNAs to one of five pre-compiled RNA datasets including lncRNAs and 3' UTRs. All types of comparison produce in output the pairwise alignments along with structural similarity and statistical significance measures for each resulting alignment. A graphical color-coded representation of the alignments allows the user to easily identify structural similarities between RNAs. Web-Beagle can be used for finding structurally related regions in two or more RNAs, for the identification of homologous regions or for functional annotation. Benchmark tests show that Web-Beagle has lower computational complexity, running time and better performances than other available methods. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
RNA-seq based transcriptomic map reveals new insights into mouse salivary gland development and maturation.

PubMed

Gluck, Christian; Min, Sangwon; Oyelakin, Akinsola; Smalley, Kirsten; Sinha, Satrajit; Romano, Rose-Anne

2016-11-16

Mouse models have served a valuable role in deciphering various facets of Salivary Gland (SG) biology, from normal developmental programs to diseased states. To facilitate such studies, gene expression profiling maps have been generated for various stages of SG organogenesis. However these prior studies fall short of capturing the transcriptional complexity due to the limited scope of gene-centric microarray-based technology. Compared to microarray, RNA-sequencing (RNA-seq) offers unbiased detection of novel transcripts, broader dynamic range and high specificity and sensitivity for detection of genes, transcripts, and differential gene expression. Although RNA-seq data, particularly under the auspices of the ENCODE project, have covered a large number of biological specimens, studies on the SG have been lacking. To better appreciate the wide spectrum of gene expression profiles, we isolated RNA from mouse submandibular salivary glands at different embryonic and adult stages. In parallel, we processed RNA-seq data for 24 organs and tissues obtained from the mouse ENCODE consortium and calculated the average gene expression values. To identify molecular players and pathways likely to be relevant for SG biology, we performed functional gene enrichment analysis, network construction and hierarchal clustering of the RNA-seq datasets obtained from different stages of SG development and maturation, and other mouse organs and tissues. Our bioinformatics-based data analysis not only reaffirmed known modulators of SG morphogenesis but revealed novel transcription factors and signaling pathways unique to mouse SG biology and function. Finally we demonstrated that the unique SG gene signature obtained from our mouse studies is also well conserved and can demarcate features of the human SG transcriptome that is different from other tissues. Our RNA-seq based Atlas has revealed a high-resolution cartographic view of the dynamic transcriptomic landscape of the mouse SG at various stages. These RNA-seq datasets will complement pre-existing microarray based datasets, including the Salivary Gland Molecular Anatomy Project by offering a broader systems-biology based perspective rather than the classical gene-centric view. Ultimately such resources will be valuable in providing a useful toolkit to better understand how the diverse cell population of the SG are organized and controlled during development and differentiation.
Common and distinguishing features of the bacterial and fungal communities in biological soil crusts and shrub root zone soils

USGS Publications Warehouse

Steven, Blaire; Gallegos-Graves, La Verne; Yeager, Chris; Belnap, Jayne; Kuske, Cheryl R.

2013-01-01

Soil microbial communities in dryland ecosystems play important roles as root associates of the widely spaced plants and as the dominant members of biological soil crusts (biocrusts) colonizing the plant interspaces. We employed rRNA gene sequencing (bacterial 16S/fungal large subunit) and shotgun metagenomic sequencing to compare the microbial communities inhabiting the root zones of the dominant shrub, Larrea tridentata (creosote bush), and the interspace biocrusts in a Mojave desert shrubland within the Nevada Free Air CO2 Enrichment (FACE) experiment. Most of the numerically abundant bacteria and fungi were present in both the biocrusts and root zones, although the proportional abundance of those members differed significantly between habitats. Biocrust bacteria were predominantly Cyanobacteria while root zones harbored significantly more Actinobacteria and Proteobacteria. Pezizomycetes fungi dominated the biocrusts while Dothideomycetes were highest in root zones. Functional gene abundances in metagenome sequence datasets reflected the taxonomic differences noted in the 16S rRNA datasets. For example, functional categories related to photosynthesis, circadian clock proteins, and heterocyst-associated genes were enriched in the biocrusts, where populations of Cyanobacteria were larger. Genes related to potassium metabolism were also more abundant in the biocrusts, suggesting differences in nutrient cycling between biocrusts and root zones. Finally, ten years of elevated atmospheric CO2 did not result in large shifts in taxonomic composition of the bacterial or fungal communities or the functional gene inventories in the shotgun metagenomes.
Unmasking Upstream Gene Expression Regulators with miRNA-corrected mRNA Data

PubMed Central

Bollmann, Stephanie; Bu, Dengpan; Wang, Jiaqi; Bionaz, Massimo

2015-01-01

Expressed micro-RNA (miRNA) affects messenger RNA (mRNA) abundance, hindering the accuracy of upstream regulator analysis. Our objective was to provide an algorithm to correct such bias. Large mRNA and miRNA analyses were performed on RNA extracted from bovine liver and mammary tissue. Using four levels of target scores from TargetScan (all miRNA:mRNA target gene pairs or only the top 25%, 50%, or 75%). Using four levels of target scores from TargetScan (all miRNA:mRNA target gene pairs or only the top 25%, 50%, or 75%) and four levels of the magnitude of miRNA effect (ME) on mRNA expression (30%, 50%, 75%, and 83% mRNA reduction), we generated 17 different datasets (including the original dataset). For each dataset, we performed upstream regulator analysis using two bioinformatics tools. We detected an increased effect on the upstream regulator analysis with larger miRNA:mRNA pair bins and higher ME. The miRNA correction allowed identification of several upstream regulators not present in the analysis of the original dataset. Thus, the proposed algorithm improved the prediction of upstream regulators. PMID:27279737
metaseq: a Python package for integrative genome-wide analysis reveals relationships between chromatin insulators and associated nuclear mRNA.

PubMed

Dale, Ryan K; Matzat, Leah H; Lei, Elissa P

2014-08-01

Here we introduce metaseq, a software library written in Python, which enables loading multiple genomic data formats into standard Python data structures and allows flexible, customized manipulation and visualization of data from high-throughput sequencing studies. We demonstrate its practical use by analyzing multiple datasets related to chromatin insulators, which are DNA-protein complexes proposed to organize the genome into distinct transcriptional domains. Recent studies in Drosophila and mammals have implicated RNA in the regulation of chromatin insulator activities. Moreover, the Drosophila RNA-binding protein Shep has been shown to antagonize gypsy insulator activity in a tissue-specific manner, but the precise role of RNA in this process remains unclear. Better understanding of chromatin insulator regulation requires integration of multiple datasets, including those from chromatin-binding, RNA-binding, and gene expression experiments. We use metaseq to integrate RIP- and ChIP-seq data for Shep and the core gypsy insulator protein Su(Hw) in two different cell types, along with publicly available ChIP-chip and RNA-seq data. Based on the metaseq-enabled analysis presented here, we propose a model where Shep associates with chromatin cotranscriptionally, then is recruited to insulator complexes in trans where it plays a negative role in insulator activity. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.
Analysis and Visualization of ChIP-Seq and RNA-Seq Sequence Alignments Using ngs.plot.

PubMed

Loh, Yong-Hwee Eddie; Shen, Li

2016-01-01

The continual maturation and increasing applications of next-generation sequencing technology in scientific research have yielded ever-increasing amounts of data that need to be effectively and efficiently analyzed and innovatively mined for new biological insights. We have developed ngs.plot-a quick and easy-to-use bioinformatics tool that performs visualizations of the spatial relationships between sequencing alignment enrichment and specific genomic features or regions. More importantly, ngs.plot is customizable beyond the use of standard genomic feature databases to allow the analysis and visualization of user-specified regions of interest generated by the user's own hypotheses. In this protocol, we demonstrate and explain the use of ngs.plot using command line executions, as well as a web-based workflow on the Galaxy framework. We replicate the underlying commands used in the analysis of a true biological dataset that we had reported and published earlier and demonstrate how ngs.plot can easily generate publication-ready figures. With ngs.plot, users would be able to efficiently and innovatively mine their own datasets without having to be involved in the technical aspects of sequence coverage calculations and genomic databases.
Grape RNA-Seq analysis pipeline environment

PubMed Central

Knowles, David G.; Röder, Maik; Merkel, Angelika; Guigó, Roderic

2013-01-01

Motivation: The avalanche of data arriving since the development of NGS technologies have prompted the need for developing fast, accurate and easily automated bioinformatic tools capable of dealing with massive datasets. Among the most productive applications of NGS technologies is the sequencing of cellular RNA, known as RNA-Seq. Although RNA-Seq provides similar or superior dynamic range than microarrays at similar or lower cost, the lack of standard and user-friendly pipelines is a bottleneck preventing RNA-Seq from becoming the standard for transcriptome analysis. Results: In this work we present a pipeline for processing and analyzing RNA-Seq data, that we have named Grape (Grape RNA-Seq Analysis Pipeline Environment). Grape supports raw sequencing reads produced by a variety of technologies, either in FASTA or FASTQ format, or as prealigned reads in SAM/BAM format. A minimal Grape configuration consists of the file location of the raw sequencing reads, the genome of the species and the corresponding gene and transcript annotation. Grape first runs a set of quality control steps, and then aligns the reads to the genome, a step that is omitted for prealigned read formats. Grape next estimates gene and transcript expression levels, calculates exon inclusion levels and identifies novel transcripts. Grape can be run on a single computer or in parallel on a computer cluster. It is distributed with specific mapping and quantification tools, but given its modular design, any tool supporting popular data interchange formats can be integrated. Availability: Grape can be obtained from the Bioinformatics and Genomics website at: http://big.crg.cat/services/grape. Contact: david.gonzalez@crg.eu or roderic.guigo@crg.eu PMID:23329413
Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality.

PubMed

Jiang, Yue; Xiong, Xuejian; Danska, Jayne; Parkinson, John

2016-01-12

Metatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function. Here, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76% of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions. The application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of reference genomes can impact comprehensive annotation of metatranscriptomes. Consequently, beyond the application of standardized pipelines, additional caution must be taken when interpreting their output and performing downstream, microbiome-specific, analyses. The pipeline used in these analyses along with a tutorial has been made freely available for download from our project website: http://www.compsysbio.org/microbiome .
In-silico and in-vivo analyses of EST databases unveil conserved miRNAs from Carthamus tinctorius and Cynara cardunculus

PubMed Central

2012-01-01

Background MicroRNAs (miRNAs) are small RNAs (21-24 bp) providing an RNA-based system of gene regulation highly conserved in plants and animals. In plants, miRNAs control mRNA degradation or restrain translation, affecting development and responses to stresses. Plant miRNAs show imperfect but extensive complementarity to mRNA targets, making their computational prediction possible, useful when data mining is applied on different species. In this study we used a comparative approach to identify both miRNAs and their targets, in artichoke and safflower. Results Two complete expressed sequence tags (ESTs) datasets from artichoke (3.6·104 entries) and safflower (4.2·104), were analysed with a bioinformatic pipeline and in vitro experiments, identifying 17 potential miRNAs. For each EST, using RNAhybrid program and 953 non redundant miRNA mature sequences, available in mirBase as reference, we searched matching putative targets. 8730 out of 42011 ESTs from safflower and 7145 of 36323 ESTs from artichoke showed at least one predicted miRNA target. BLAST analysis showed that 75% of all ESTs shared at least a common homologous region (E-value < 10-4) and about 50% of these displayed 400 bp or longer aligned sequences as conserved homologous/orthologous (COS) regions. 960 and 890 ESTs of safflower and artichoke organized in COS shared 79 different miRNA targets, considered functionally conserved, and statistically significant when compared with random sequences (signal to noise ratio > 2 and specificity ≥ 0.85). Four highly significant miRNAs selected from in silico data were experimentally validated in globe artichoke leaves. Conclusions Mature miRNAs and targets were predicted within EST sequences of safflower and artichoke. Most of the miRNA targets appeared highly/moderately conserved, highlighting an important and conserved function. In this study we introduce a stringent parameter for the comparative sequence analysis, represented by the identification of the same target in the COS region. After statistical analysis 79 targets, found on the COS regions and belonging to 60 miRNA families, have a signal to noise ratio > 2, with ≥ 0.85 specificity. The putative miRNAs identified belong to 55 dicotyledon plants and to 24 families only in monocotyledon. PMID:22536958

Molecular phylogeny of Systellognatha (Plecoptera: Arctoperlaria) inferred from mitochondrial genome sequences.

PubMed

Chen, Zhi-Teng; Zhao, Meng-Yuan; Xu, Cheng; Du, Yu-Zhou

2018-05-01

The infraorder Systellognatha is the most species-rich clade in the insect order Plecoptera and includes six families in two superfamilies: Pteronarcyoidea (Pteronarcyidae, Peltoperlidae, and Styloperlidae) and Perloidea (Perlidae, Perlodidae, and Chloroperlidae). To resolve the debatable phylogeny of Systellognatha, we carried out the first mitochondrial phylogenetic analysis covering all the six families, including three newly sequenced mitogenomes from two families (Perlodidae and Peltoperlidae) and 15 published mitogenomes. The three newly reported mitogenomes share conserved mitogenomic features with other sequenced stoneflies. For phylogenetic analyses, we assembled five datasets with two inference methods to assess their influence on topology and nodal support within Systellognatha. The results indicated that inclusion of the third codon positions of PCGs, exclusion of rRNA genes, the use of nucleotide datasets and Bayesian inference could improve the phylogenetic reconstruction of Systellognatha. The monophyly of Perloidea was supported in the mitochondrial phylogeny, but Pteronarcyoidea was recovered as paraphyletic and remained controversial. In this mitochondrial phylogenetic study, the relationships within Systellognatha were recovered as (((Perlidae + (Perlodidae + Chloroperlidae)) + (Pteronarcyidae + Styloperlidae)) + Peltoperlidae). Copyright © 2018 Elsevier B.V. All rights reserved.
DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.

PubMed

Wang, Zhuo; Jin, Shuilin; Liu, Guiyou; Zhang, Xiurui; Wang, Nan; Wu, Deliang; Hu, Yang; Zhang, Chiping; Jiang, Qinghua; Xu, Li; Wang, Yadong

2017-05-23

The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis. We present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types. The DTWscore successfully classify cells of different types with the most highly variable genes from time-series scRNA-seq data. The study was confined to methods that are implemented and available within the R framework. Sample datasets and R packages are available at https://github.com/xiaoxiaoxier/DTWscore .
A powerful and flexible approach to the analysis of RNA sequence count data.

PubMed

Zhou, Yi-Hui; Xia, Kai; Wright, Fred A

2011-10-01

A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean-variance relationships provides a flexible testing regimen that 'borrows' information across genes, while easily incorporating design effects and additional covariates. We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean-variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq yzhou@bios.unc.edu; fwright@bios.unc.edu Supplementary data are available at Bioinformatics online.
RED: A Java-MySQL Software for Identifying and Visualizing RNA Editing Sites Using Rule-Based and Statistical Filters.

PubMed

Sun, Yongmei; Li, Xing; Wu, Di; Pan, Qi; Ji, Yuefeng; Ren, Hong; Ding, Keyue

2016-01-01

RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, there remains challenges to be addressed in distinguishing true RNA editing sites from its counterparts on genome and technical artifacts. In addition, there lacks a software framework to identify and visualize potential RNA editing sites. Here, we presented a software - 'RED' (RNA Editing sites Detector) - for the identification of RNA editing sites by integrating multiple rule-based and statistical filters. The potential RNA editing sites can be visualized at the genome and the site levels by graphical user interface (GUI). To improve performance, we used MySQL database management system (DBMS) for high-throughput data storage and query. We demonstrated the validity and utility of RED by identifying the presence and absence of C→U RNA-editing sites experimentally validated, in comparison with REDItools, a command line tool to perform high-throughput investigation of RNA editing. In an analysis of a sample data-set with 28 experimentally validated C→U RNA editing sites, RED had sensitivity and specificity of 0.64 and 0.5. In comparison, REDItools had a better sensitivity (0.75) but similar specificity (0.5). RED is an easy-to-use, platform-independent Java-based software, and can be applied to RNA-seq data without or with DNA sequencing data. The package is freely available under the GPLv3 license at http://github.com/REDetector/RED or https://sourceforge.net/projects/redetector.
RED: A Java-MySQL Software for Identifying and Visualizing RNA Editing Sites Using Rule-Based and Statistical Filters

PubMed Central

Sun, Yongmei; Li, Xing; Wu, Di; Pan, Qi; Ji, Yuefeng; Ren, Hong; Ding, Keyue

2016-01-01

RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, there remains challenges to be addressed in distinguishing true RNA editing sites from its counterparts on genome and technical artifacts. In addition, there lacks a software framework to identify and visualize potential RNA editing sites. Here, we presented a software − ‘RED’ (RNA Editing sites Detector) − for the identification of RNA editing sites by integrating multiple rule-based and statistical filters. The potential RNA editing sites can be visualized at the genome and the site levels by graphical user interface (GUI). To improve performance, we used MySQL database management system (DBMS) for high-throughput data storage and query. We demonstrated the validity and utility of RED by identifying the presence and absence of C→U RNA-editing sites experimentally validated, in comparison with REDItools, a command line tool to perform high-throughput investigation of RNA editing. In an analysis of a sample data-set with 28 experimentally validated C→U RNA editing sites, RED had sensitivity and specificity of 0.64 and 0.5. In comparison, REDItools had a better sensitivity (0.75) but similar specificity (0.5). RED is an easy-to-use, platform-independent Java-based software, and can be applied to RNA-seq data without or with DNA sequencing data. The package is freely available under the GPLv3 license at http://github.com/REDetector/RED or https://sourceforge.net/projects/redetector. PMID:26930599
OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes.

PubMed

Chetal, Kashish; Janga, Sarath Chandra

2015-01-01

Background. In prokaryotic organisms, a substantial fraction of adjacent genes are organized into operons-codirectionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Although several available operon databases provide information with varying levels of reliability, very few resources provide experimentally supported results. Therefore, we believe that the biological community could benefit from having a new operon prediction database with operons predicted using next-generation RNA-seq datasets. Description. We present operomeDB, a database which provides an ensemble of all the predicted operons for bacterial genomes using available RNA-sequencing datasets across a wide range of experimental conditions. Although several studies have recently confirmed that prokaryotic operon structure is dynamic with significant alterations across environmental and experimental conditions, there are no comprehensive databases for studying such variations across prokaryotic transcriptomes. Currently our database contains nine bacterial organisms and 168 transcriptomes for which we predicted operons. User interface is simple and easy to use, in terms of visualization, downloading, and querying of data. In addition, because of its ability to load custom datasets, users can also compare their datasets with publicly available transcriptomic data of an organism. Conclusion. OperomeDB as a database should not only aid experimental groups working on transcriptome analysis of specific organisms but also enable studies related to computational and comparative operomics.
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database.

PubMed

Zappia, Luke; Phipson, Belinda; Oshlack, Alicia

2018-06-25

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time.
APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins.

PubMed

Sharan, Malvika; Förstner, Konrad U; Eulalio, Ana; Vogel, Jörg

2017-06-20

RNA-binding proteins (RBPs) have been established as core components of several post-transcriptional gene regulation mechanisms. Experimental techniques such as cross-linking and co-immunoprecipitation have enabled the identification of RBPs, RNA-binding domains (RBDs) and their regulatory roles in the eukaryotic species such as human and yeast in large-scale. In contrast, our knowledge of the number and potential diversity of RBPs in bacteria is poorer due to the technical challenges associated with the existing global screening approaches. We introduce APRICOT, a computational pipeline for the sequence-based identification and characterization of proteins using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences using position-specific scoring matrices and Hidden Markov Models of the functional domains and statistically scores them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them by several biological properties. Here we demonstrate the application and adaptability of the pipeline on large-scale protein sets, including the bacterial proteome of Escherichia coli. APRICOT showed better performance on various datasets compared to other existing tools for the sequence-based prediction of RBPs by achieving an average sensitivity and specificity of 0.90 and 0.91 respectively. The command-line tool and its documentation are available at https://pypi.python.org/pypi/bio-apricot. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
ISRNA: an integrative online toolkit for short reads from high-throughput sequencing data.

PubMed

Luo, Guan-Zheng; Yang, Wei; Ma, Ying-Ke; Wang, Xiu-Jie

2014-02-01

Integrative Short Reads NAvigator (ISRNA) is an online toolkit for analyzing high-throughput small RNA sequencing data. Besides the high-speed genome mapping function, ISRNA provides statistics for genomic location, length distribution and nucleotide composition bias analysis of sequence reads. Number of reads mapped to known microRNAs and other classes of short non-coding RNAs, coverage of short reads on genes, expression abundance of sequence reads as well as some other analysis functions are also supported. The versatile search functions enable users to select sequence reads according to their sub-sequences, expression abundance, genomic location, relationship to genes, etc. A specialized genome browser is integrated to visualize the genomic distribution of short reads. ISRNA also supports management and comparison among multiple datasets. ISRNA is implemented in Java/C++/Perl/MySQL and can be freely accessed at http://omicslab.genetics.ac.cn/ISRNA/.
Low Maternal Microbiota Sharing across Gut, Breast Milk and Vagina, as Revealed by 16S rRNA Gene and Reduced Metagenomic Sequencing.

PubMed

Avershina, Ekaterina; Angell, Inga Leena; Simpson, Melanie; Storrø, Ola; Øien, Torbjørn; Johnsen, Roar; Rudi, Knut

2018-05-01

The maternal microbiota plays an important role in infant gut colonization. In this work we have investigated which bacterial species are shared across the breast milk, vaginal and stool microbiotas of 109 women shortly before and after giving birth using 16S rRNA gene sequencing and a novel reduced metagenomic sequencing (RMS) approach in a subgroup of 16 women. All the species predicted by the 16S rRNA gene sequencing were also detected by RMS analysis and there was good correspondence between their relative abundances estimated by both approaches. Both approaches also demonstrate a low level of maternal microbiota sharing across the population and RMS analysis identified only two species common to most women and in all sample types ( Bifidobacterium longum and Enterococcus faecalis ). Breast milk was the only sample type that had significantly higher intra- than inter- individual similarity towards both vaginal and stool samples. We also searched our RMS dataset against an in silico generated reference database derived from bacterial isolates in the Human Microbiome Project. The use of this reference-based search enabled further separation of Bifidobacterium longum into Bifidobacterium longum ssp. longum and Bifidobacterium longum ssp. infantis . We also detected the Lactobacillus rhamnosus GG strain, which was used as a probiotic supplement by some women, demonstrating the potential of RMS approach for deeper taxonomic delineation and estimation.
Low Maternal Microbiota Sharing across Gut, Breast Milk and Vagina, as Revealed by 16S rRNA Gene and Reduced Metagenomic Sequencing

PubMed Central

Angell, Inga Leena; Storrø, Ola; Øien, Torbjørn; Johnsen, Roar; Rudi, Knut

2018-01-01

The maternal microbiota plays an important role in infant gut colonization. In this work we have investigated which bacterial species are shared across the breast milk, vaginal and stool microbiotas of 109 women shortly before and after giving birth using 16S rRNA gene sequencing and a novel reduced metagenomic sequencing (RMS) approach in a subgroup of 16 women. All the species predicted by the 16S rRNA gene sequencing were also detected by RMS analysis and there was good correspondence between their relative abundances estimated by both approaches. Both approaches also demonstrate a low level of maternal microbiota sharing across the population and RMS analysis identified only two species common to most women and in all sample types (Bifidobacterium longum and Enterococcus faecalis). Breast milk was the only sample type that had significantly higher intra- than inter- individual similarity towards both vaginal and stool samples. We also searched our RMS dataset against an in silico generated reference database derived from bacterial isolates in the Human Microbiome Project. The use of this reference-based search enabled further separation of Bifidobacterium longum into Bifidobacterium longum ssp. longum and Bifidobacterium longum ssp. infantis. We also detected the Lactobacillus rhamnosus GG strain, which was used as a probiotic supplement by some women, demonstrating the potential of RMS approach for deeper taxonomic delineation and estimation. PMID:29724017
Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

NASA Astrophysics Data System (ADS)

Xing, Pengwei; Su, Ran; Guo, Fei; Wei, Leyi

2017-04-01

N6-methyladenosine (m6A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m6A sites within sequences since high-resolution mapping of m6A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m6A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/. It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m6A site functions.
Sequence editing by Apolipoprotein B RNA-editing catalytic component-B and epidemiological surveillance of transmitted HIV-1 drug resistance

PubMed Central

Gifford, Robert J.; Rhee, Soo-Yon; Eriksson, Nicolas; Liu, Tommy F.; Kiuchi, Mark; Das, Amar K.; Shafer, Robert W.

2008-01-01

Design Promiscuous guanine (G) to adenine (A) substitutions catalysed by apolipoprotein B RNA-editing catalytic component (APOBEC) enzymes are observed in a proportion of HIV-1 sequences in vivo and can introduce artifacts into some genetic analyses. The potential impact of undetected lethal editing on genotypic estimation of transmitted drug resistance was assessed. Methods Classifiers of lethal, APOBEC-mediated editing were developed by analysis of lentiviral pol gene sequence variation and evaluated using control sets of HIV-1 sequences. The potential impact of sequence editing on genotypic estimation of drug resistance was assessed in sets of sequences obtained from 77 studies of 25 or more therapy-naive individuals, using mixture modelling approaches to determine the maximum likelihood classification of sequences as lethally edited as opposed to viable. Results Analysis of 6437 protease and reverse transcriptase sequences from therapy-naive individuals using a novel classifier of lethal, APOBEC3G-mediated sequence editing, the polypeptide-like 3G (APOBEC3G)-mediated defectives (A3GD) index’, detected lethal editing in association with spurious ‘transmitted drug resistance’ in nearly 3% of proviral sequences obtained from whole blood and 0.2% of samples obtained from plasma. Conclusion Screening for lethally edited sequences in datasets containing a proportion of proviral DNA, such as those likely to be obtained for epidemiological surveillance of transmitted drug resistance in the developing world, can eliminate rare but potentially significant errors in genotypic estimation of transmitted drug resistance. PMID:18356601
Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

PubMed Central

2010-01-01

Background Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. Results An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. Conclusions The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism. PMID:20380733
Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study.

PubMed

Liu, Qi; Xu, Qian; Zheng, Vincent W; Xue, Hong; Cao, Zhiwei; Yang, Qiang

2010-04-10

Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.
Sequencing, Annotation and Analysis of the Syrian Hamster (Mesocricetus auratus) Transcriptome

PubMed Central

Tchitchek, Nicolas; Safronetz, David; Rasmussen, Angela L.; Martens, Craig; Virtaneva, Kimmo; Porcella, Stephen F.; Feldmann, Heinz

2014-01-01

Background The Syrian hamster (golden hamster, Mesocricetus auratus) is gaining importance as a new experimental animal model for multiple pathogens, including emerging zoonotic diseases such as Ebola. Nevertheless there are currently no publicly available transcriptome reference sequences or genome for this species. Results A cDNA library derived from mRNA and snRNA isolated and pooled from the brains, lungs, spleens, kidneys, livers, and hearts of three adult female Syrian hamsters was sequenced. Sequence reads were assembled into 62,482 contigs and 111,796 reads remained unassembled (singletons). This combined contig/singleton dataset, designated as the Syrian hamster transcriptome, represents a total of 60,117,204 nucleotides. Our Mesocricetus auratus Syrian hamster transcriptome mapped to 11,648 mouse transcripts representing 9,562 distinct genes, and mapped to a similar number of transcripts and genes in the rat. We identified 214 quasi-complete transcripts based on mouse annotations. Canonical pathways involved in a broad spectrum of fundamental biological processes were significantly represented in the library. The Syrian hamster transcriptome was aligned to the current release of the Chinese hamster ovary (CHO) cell transcriptome and genome to improve the genomic annotation of this species. Finally, our Syrian hamster transcriptome was aligned against 14 other rodents, primate and laurasiatheria species to gain insights about the genetic relatedness and placement of this species. Conclusions This Syrian hamster transcriptome dataset significantly improves our knowledge of the Syrian hamster's transcriptome, especially towards its future use in infectious disease research. Moreover, this library is an important resource for the wider scientific community to help improve genome annotation of the Syrian hamster and other closely related species. Furthermore, these data provide the basis for development of expression microarrays that can be used in functional genomics studies. PMID:25398096
De novo assembled expressed gene catalog of a fast-growing Eucalyptus tree produced by Illumina mRNA-Seq

PubMed Central

2010-01-01

Background De novo assembly of transcript sequences produced by short-read DNA sequencing technologies offers a rapid approach to obtain expressed gene catalogs for non-model organisms. A draft genome sequence will be produced in 2010 for a Eucalyptus tree species (E. grandis) representing the most important hardwood fibre crop in the world. Genome annotation of this valuable woody plant and genetic dissection of its superior growth and productivity will be greatly facilitated by the availability of a comprehensive collection of expressed gene sequences from multiple tissues and organs. Results We present an extensive expressed gene catalog for a commercially grown E. grandis × E. urophylla hybrid clone constructed using only Illumina mRNA-Seq technology and de novo assembly. A total of 18,894 transcript-derived contigs, a large proportion of which represent full-length protein coding genes were assembled and annotated. Analysis of assembly quality, length and diversity show that this dataset represent the most comprehensive expressed gene catalog for any Eucalyptus tree. mRNA-Seq analysis furthermore allowed digital expression profiling of all of the assembled transcripts across diverse xylogenic and non-xylogenic tissues, which is invaluable for ascribing putative gene functions. Conclusions De novo assembly of Illumina mRNA-Seq reads is an efficient approach for transcriptome sequencing and profiling in Eucalyptus and other non-model organisms. The transcriptome resource (Eucspresso, http://eucspresso.bi.up.ac.za/) generated by this study will be of value for genomic analysis of woody biomass production in Eucalyptus and for comparative genomic analysis of growth and development in woody and herbaceous plants. PMID:21122097
Phylogenetic position of the enigmatic clawless eutardigrade genus Apodibius Dastych, 1983 (Tardigrada), based on 18S and 28S rRNA sequence data from its type species A. confusus.

PubMed

Dabert, Miroslawa; Dastych, Hieronymus; Hohberg, Karin; Dabert, Jacek

2014-01-01

The systematics of Eutardigrada, the largest lineage among the three classes of the phylum Tardigrada, is based mainly on the morphology of the leg claws and of the buccal apparatus. However, three members of the rarely recorded and poorly known limno-terrestrial eutardigrade genus Apodibius have no claws on their strongly reduced legs, a unique character among all tardigrades. This absence of all claws makes the systematic position of Apodibius one of the most enigmatic among the whole class. Until now all known associates of the genus Apodibius have been located in the incertae sedis species group or, quite recently, included into the Necopinatidae family. In the present study, phylogenetic analyses of 18S and 28S rRNA sequence data from 31 tardigrade species representing four parachelan superfamilies (Isohypsibioidea, Hypsibioidea, Macrobiotoidea, Eohypsibioidea), the apochelan Milnesium tardigradum, and the type species of the genus Apodibius, A. confusus, indicated close relationship of the Apodibius with tardigrade species recently included in the superfamily Isohypsibioidea. This result was well-supported and consistent across all markers (separate 18S rRNA, 28S rRNA, and combined 18S rRNA+28S rRNA datasets) and methods (MP, ML) applied. Copyright © 2013 Elsevier Inc. All rights reserved.
Profiling RNA editing in human tissues: towards the inosinome Atlas

PubMed Central

Picardi, Ernesto; Manzari, Caterina; Mastropasqua, Francesca; Aiello, Italia; D’Erchia, Anna Maria; Pesole, Graziano

2015-01-01

Adenine to Inosine RNA editing is a widespread co- and post-transcriptional mechanism mediated by ADAR enzymes acting on double stranded RNA. It has a plethora of biological effects, appears to be particularly pervasive in humans with respect to other mammals, and is implicated in a number of diverse human pathologies. Here we present the first human inosinome atlas comprising 3,041,422 A-to-I events identified in six tissues from three healthy individuals. Matched directional total-RNA-Seq and whole genome sequence datasets were generated and analysed within a dedicated computational framework, also capable of detecting hyper-edited reads. Inosinome profiles are tissue specific and edited gene sets consistently show enrichment of genes involved in neurological disorders and cancer. Overall frequency of editing also varies, but is strongly correlated with ADAR expression levels. The inosinome database is available at: http://srv00.ibbe.cnr.it/editing/. PMID:26449202
Phylogenetic screening of a bacterial, metagenomic library using homing endonuclease restriction and marker insertion

PubMed Central

Yung, Pui Yi; Burke, Catherine; Lewis, Matt; Egan, Suhelen; Kjelleberg, Staffan; Thomas, Torsten

2009-01-01

Metagenomics provides access to the uncultured majority of the microbial world. The approaches employed in this field have, however, had limited success in linking functional genes to the taxonomic or phylogenetic origin of the organism they belong to. Here we present an efficient strategy to recover environmental DNA fragments that contain phylogenetic marker genes from metagenomic libraries. Our method involves the cleavage of 23S ribsosmal RNA (rRNA) genes within pooled library clones by the homing endonuclease I-CeuI followed by the insertion and selection of an antibiotic resistance cassette. This approach was applied to screen a library of 6500 fosmid clones derived from the microbial community associated with the sponge Cymbastela concentrica. Several fosmid clones were recovered after the screen and detailed phylogenetic and taxonomic assignment based on the rRNA gene showed that they belong to previously unknown organisms. In addition, compositional features of these fosmid clones were used to classify and taxonomically assign a dataset of environmental shotgun sequences. Our approach represents a valuable tool for the analysis of rapidly increasing, environmental DNA sequencing information. PMID:19767618

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.
Reticulamoeba Is a Long-Branched Granofilosean (Cercozoa) That Is Missing from Sequence Databases

PubMed Central

Bass, David; Yabuki, Akinori; Santini, Sébastien; Romac, Sarah; Berney, Cédric

2012-01-01

We sequenced the 18S ribosomal RNA gene of seven isolates of the enigmatic marine amoeboflagellate Reticulamoeba Grell, which resolved into four genetically distinct Reticulamoeba lineages, two of which correspond to R. gemmipara Grell and R. minor Grell, another with a relatively large cell body forming lacunae, and another that has similarities to both R. minor and R. gemmipara but with a greater propensity to form cell clusters. These lineages together form a long-branched clade that branches within the cercozoan class Granofilosea (phylum Cercozoa), showing phylogenetic affinities with the genus Mesofila. The basic morphology of Reticulamoeba is a roundish or ovoid cell with a more or less irregular outline. Long and branched reticulopodia radiate from the cell. The reticulopodia bear granules that are bidirectionally motile. There is also a biflagellate dispersal stage. Reticulamoeba is frequently observed in coastal marine environmental samples. PCR primers specific to the Reticulamoeba clade confirm that it is a frequent member of benthic marine microbial communities, and is also found in brackish water sediments and freshwater biofilm. However, so far it has not been found in large molecular datasets such as the nucleotide database in NCBI GenBank, metagenomic datasets in Camera, and the marine microbial eukaryote sampling and sequencing consortium BioMarKs, although closely related lineages can be found in some of these datasets using a highly targeted approach. Therefore, although such datasets are very powerful tools in microbial ecology, they may, for several methodological reasons, fail to detect ecologically and evolutionary key lineages. PMID:23226495
Isolation and genetic characterization of Aurantimonas and Methylobacterium strains from stems of hypernodulated soybeans.

PubMed

Anda, Mizue; Ikeda, Seishi; Eda, Shima; Okubo, Takashi; Sato, Shusei; Tabata, Satoshi; Mitsui, Hisayuki; Minamisawa, Kiwamu

2011-01-01

The aims of this study were to isolate Aurantimonas and Methylobacterium strains that responded to soybean nodulation phenotypes and nitrogen fertilization rates in a previous culture-independent analysis (Ikeda et al. ISME J. 4:315-326, 2010). Two strategies were adopted for isolation from enriched bacterial cells prepared from stems of field-grown, hypernodulated soybeans: PCR-assisted isolation for Aurantimonas and selective cultivation for Methylobacterium. Thirteen of 768 isolates cultivated on Nutrient Agar medium were identified as Aurantimonas by colony PCR specific for Aurantimonas and 16S rRNA gene sequencing. Meanwhile, among 187 isolates on methanol-containing agar media, 126 were identified by 16S rRNA gene sequences as Methylobacterium. A clustering analysis (>99% identity) of the 16S rRNA gene sequences for the combined datasets of the present and previous studies revealed 4 and 8 operational taxonomic units (OTUs) for Aurantimonas and Methylobacterium, respectively, and showed the successful isolation of target bacteria for these two groups. ERIC- and BOX-PCR showed the genomic uniformity of the target isolates. In addition, phylogenetic analyses of Aurantimonas revealed a phyllosphere-specific cluster in the genus. The isolates obtained in the present study will be useful for revealing unknown legume-microbe interactions in relation to the autoregulation of nodulation.
RNA-seq analysis of Rubus idaeus cv. Nova: transcriptome sequencing and de novo assembly for subsequent functional genomics approaches.

PubMed

Hyun, Tae Kyung; Lee, Sarah; Kumar, Dhinesh; Rim, Yeonggil; Kumar, Ritesh; Lee, Sang Yeol; Lee, Choong Hwan; Kim, Jae-Yean

2014-10-01

Using Illumina sequencing technology, we have generated the large-scale transcriptome sequencing data containing abundant information on genes involved in the metabolic pathways in R. idaeus cv. Nova fruits. Rubus idaeus (Red raspberry) is one of the important economical crops that possess numerous nutrients, micronutrients and phytochemicals with essential health benefits to human. The molecular mechanism underlying the ripening process and phytochemical biosynthesis in red raspberry is attributed to the changes in gene expression, but very limited transcriptomic and genomic information in public databases is available. To address this issue, we generated more than 51 million sequencing reads from R. idaeus cv. Nova fruit using Illumina RNA-Seq technology. After de novo assembly, we obtained 42,604 unigenes with an average length of 812 bp. At the protein level, Nova fruit transcriptome showed 77 and 68 % sequence similarities with Rubus coreanus and Fragaria versa, respectively, indicating the evolutionary relationship between them. In addition, 69 % of assembled unigenes were annotated using public databases including NCBI non-redundant, Cluster of Orthologous Groups and Gene ontology database, suggesting that our transcriptome dataset provides a valuable resource for investigating metabolic processes in red raspberry. To analyze the relationship between several novel transcripts and the amounts of metabolites such as γ-aminobutyric acid and anthocyanins, real-time PCR and target metabolite analysis were performed on two different ripening stages of Nova. This is the first attempt using Illumina sequencing platform for RNA sequencing and de novo assembly of Nova fruit without reference genome. Our data provide the most comprehensive transcriptome resource available for Rubus fruits, and will be useful for understanding the ripening process and for breeding R. idaeus cultivars with improved fruit quality.
CloVR-ITS: Automated internal transcribed spacer amplicon sequence analysis pipeline for the characterization of fungal microbiota

PubMed Central

2013-01-01

Background Besides the development of comprehensive tools for high-throughput 16S ribosomal RNA amplicon sequence analysis, there exists a growing need for protocols emphasizing alternative phylogenetic markers such as those representing eukaryotic organisms. Results Here we introduce CloVR-ITS, an automated pipeline for comparative analysis of internal transcribed spacer (ITS) pyrosequences amplified from metagenomic DNA isolates and representing fungal species. This pipeline performs a variety of steps similar to those commonly used for 16S rRNA amplicon sequence analysis, including preprocessing for quality, chimera detection, clustering of sequences into operational taxonomic units (OTUs), taxonomic assignment (at class, order, family, genus, and species levels) and statistical analysis of sample groups of interest based on user-provided information. Using ITS amplicon pyrosequencing data from a previous human gastric fluid study, we demonstrate the utility of CloVR-ITS for fungal microbiota analysis and provide runtime and cost examples, including analysis of extremely large datasets on the cloud. We show that the largest fractions of reads from the stomach fluid samples were assigned to Dothideomycetes, Saccharomycetes, Agaricomycetes and Sordariomycetes but that all samples were dominated by sequences that could not be taxonomically classified. Representatives of the Candida genus were identified in all samples, most notably C. quercitrusa, while sequence reads assigned to the Aspergillus genus were only identified in a subset of samples. CloVR-ITS is made available as a pre-installed, automated, and portable software pipeline for cloud-friendly execution as part of the CloVR virtual machine package (http://clovr.org). Conclusion The CloVR-ITS pipeline provides fungal microbiota analysis that can be complementary to bacterial 16S rRNA and total metagenome sequence analysis allowing for more comprehensive studies of environmental and host-associated microbial communities. PMID:24451270
ReadXplorer—visualization and analysis of mapped sequences

PubMed Central

Hilker, Rolf; Stadermann, Kai Bernd; Doppmeier, Daniel; Kalinowski, Jörn; Stoye, Jens; Straube, Jasmin; Winnebald, Jörn; Goesmann, Alexander

2014-01-01

Motivation: Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. Results: ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion–insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. Availability and implementation: ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual. Contact: rhilker@mikrobio.med.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24790157
Complete Sequence and Analysis of Coconut Palm (Cocos nucifera) Mitochondrial Genome.

PubMed

Aljohi, Hasan Awad; Liu, Wanfei; Lin, Qiang; Zhao, Yuhui; Zeng, Jingyao; Alamer, Ali; Alanazi, Ibrahim O; Alawad, Abdullah O; Al-Sadi, Abdullah M; Hu, Songnian; Yu, Jun

2016-01-01

Coconut (Cocos nucifera L.), a member of the palm family (Arecaceae), is one of the most economically important crops in tropics, serving as an important source of food, drink, fuel, medicine, and construction material. Here we report an assembly of the coconut (C. nucifera, Oman local Tall cultivar) mitochondrial (mt) genome based on next-generation sequencing data. This genome, 678,653bp in length and 45.5% in GC content, encodes 72 proteins, 9 pseudogenes, 23 tRNAs, and 3 ribosomal RNAs. Within the assembly, we find that the chloroplast (cp) derived regions account for 5.07% of the total assembly length, including 13 proteins, 2 pseudogenes, and 11 tRNAs. The mt genome has a relatively large fraction of repeat content (17.26%), including both forward (tandem) and inverted (palindromic) repeats. Sequence variation analysis shows that the Ti/Tv ratio of the mt genome is lower as compared to that of the nuclear genome and neutral expectation. By combining public RNA-Seq data for coconut, we identify 734 RNA editing sites supported by at least two datasets. In summary, our data provides the second complete mt genome sequence in the family Arecaceae, essential for further investigations on mitochondrial biology of seed plants.
Characterization and Comparative Profiling of MiRNA Transcriptomes in Bighead Carp and Silver Carp

PubMed Central

Chi, Wei; Tong, Chaobo; Gan, Xiaoni; He, Shunping

2011-01-01

MicroRNAs (miRNAs) are small non-coding RNA molecules that are processed from large ‘hairpin’ precursors and function as post-transcriptional regulators of target genes. Although many individual miRNAs have recently been extensively studied, there has been very little research on miRNA transcriptomes in teleost fishes. By using high throughput sequencing technology, we have identified 167 and 166 conserved miRNAs (belonging to 108 families) in bighead carp (Hypophthalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix), respectively. We compared the expression patterns of conserved miRNAs by means of hierarchical clustering analysis and log2 ratio. Results indicated that there is not a strong correlation between sequence conservation and expression conservation, most of these miRNAs have similar expression patterns. However, high expression differences were also identified for several individual miRNAs. Several miRNA* sequences were also found in our dataset and some of them may have regulatory functions. Two computational strategies were used to identify novel miRNAs from un-annotated data in the two carps. A first strategy based on zebrafish genome, identified 8 and 22 novel miRNAs in bighead carp and silver carp, respectively. We postulate that these miRNAs should also exist in the zebrafish, but the methodologies used have not allowed for their detection. In the second strategy we obtained several carp-specific miRNAs, 31 in bighead carp and 32 in silver carp, which showed low expression. Gain and loss of family members were observed in several miRNA families, which suggests that duplication of animal miRNA genes may occur through evolutionary processes which are similar to the protein-coding genes. PMID:21858165
rpiCOOL: A tool for In Silico RNA-protein interaction detection using random forest.

PubMed

Akbaripour-Elahabad, Mohammad; Zahiri, Javad; Rafeh, Reza; Eslami, Morteza; Azari, Mahboobeh

2016-08-07

Understanding the principle of RNA-protein interactions (RPIs) is of critical importance to provide insights into post-transcriptional gene regulation and is useful to guide studies about many complex diseases. The limitations and difficulties associated with experimental determination of RPIs, call an urgent need to computational methods for RPI prediction. In this paper, we proposed a machine learning method to detect RNA-protein interactions based on sequence information. We used motif information and repetitive patterns, which have been extracted from experimentally validated RNA-protein interactions, in combination with sequence composition as descriptors to build a model to RPI prediction via a random forest classifier. About 20% of the "sequence motifs" and "nucleotide composition" features have been selected as the informative features with the feature selection methods. These results suggest that these two feature types contribute effectively in RPI detection. Results of 10-fold cross-validation experiments on three non-redundant benchmark datasets show a better performance of the proposed method in comparison with the current state-of-the-art methods in terms of various performance measures. In addition, the results revealed that the accuracy of the RPI prediction methods could vary considerably across different organisms. We have implemented the proposed method, namely rpiCOOL, as a stand-alone tool with a user friendly graphical user interface (GUI) that enables the researchers to predict RNA-protein interaction. The rpiCOOL is freely available at http://biocool.ir/rpicool.html for non-commercial uses. Copyright © 2016 Elsevier Ltd. All rights reserved.
RNAblueprint: flexible multiple target nucleic acid sequence design.

PubMed

Hammer, Stefan; Tschiatschek, Birgit; Flamm, Christoph; Hofacker, Ivo L; Findeiß, Sven

2017-09-15

Realizing the value of synthetic biology in biotechnology and medicine requires the design of molecules with specialized functions. Due to its close structure to function relationship, and the availability of good structure prediction methods and energy models, RNA is perfectly suited to be synthetically engineered with predefined properties. However, currently available RNA design tools cannot be easily adapted to accommodate new design specifications. Furthermore, complicated sampling and optimization methods are often developed to suit a specific RNA design goal, adding to their inflexibility. We developed a C ++ library implementing a graph coloring approach to stochastically sample sequences compatible with structural and sequence constraints from the typically very large solution space. The approach allows to specify and explore the solution space in a well defined way. Our library also guarantees uniform sampling, which makes optimization runs performant by not only avoiding re-evaluation of already found solutions, but also by raising the probability of finding better solutions for long optimization runs. We show that our software can be combined with any other software package to allow diverse RNA design applications. Scripting interfaces allow the easy adaption of existing code to accommodate new scenarios, making the whole design process very flexible. We implemented example design approaches written in Python to demonstrate these advantages. RNAblueprint , Python implementations and benchmark datasets are available at github: https://github.com/ViennaRNA . s.hammer@univie.ac.at, ivo@tbi.univie.ac.at or sven@tbi.univie.ac.at. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.
Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign

PubMed Central

2007-01-01

Background Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities. The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise RNA structure prediction. Results The proposed technique eliminates manual parameter selection in Dynalign and provides significant computational time savings in comparison to prior constraints in Dynalign while simultaneously providing a small improvement in the structural prediction accuracy. Savings are also realized in memory. In experiments over a 5S RNA dataset with average sequence length of approximately 120 nucleotides, the method reduces computation by a factor of 2. The method performs favorably in comparison to other programs for pairwise RNA structure prediction: yielding better accuracy, on average, and requiring significantly lesser computational resources. Conclusion Probabilistic analysis can be utilized in order to automate the determination of alignment constraints for pairwise RNA structure prediction methods in a principled fashion. These constraints can reduce the computational and memory requirements of these methods while maintaining or improving their accuracy of structural prediction. This extends the practical reach of these methods to longer length sequences. The revised Dynalign code is freely available for download. PMID:17445273
Phylogenetic relatedness determined between antibiotic resistance and 16S rRNA genes in actinobacteria.

PubMed

Sagova-Mareckova, Marketa; Ulanova, Dana; Sanderova, Petra; Omelka, Marek; Kamenik, Zdenek; Olsovska, Jana; Kopecky, Jan

2015-04-01

Distribution and evolutionary history of resistance genes in environmental actinobacteria provide information on intensity of antibiosis and evolution of specific secondary metabolic pathways at a given site. To this day, actinobacteria producing biologically active compounds were isolated mostly from soil but only a limited range of soil environments were commonly sampled. Consequently, soil remains an unexplored environment in search for novel producers and related evolutionary questions. Ninety actinobacteria strains isolated at contrasting soil sites were characterized phylogenetically by 16S rRNA gene, for presence of erm and ABC transporter resistance genes and antibiotic production. An analogous analysis was performed in silico with 246 and 31 strains from Integrated Microbial Genomes (JGI_IMG) database selected by the presence of ABC transporter genes and erm genes, respectively. In the isolates, distances of erm gene sequences were significantly correlated to phylogenetic distances based on 16S rRNA genes, while ABC transporter gene distances were not. The phylogenetic distance of isolates was significantly correlated to soil pH and organic matter content of isolation sites. In the analysis of JGI_IMG datasets the correlation between phylogeny of resistance genes and the strain phylogeny based on 16S rRNA genes or five housekeeping genes was observed for both the erm genes and ABC transporter genes in both actinobacteria and streptomycetes. However, in the analysis of sequences from genomes where both resistance genes occurred together the correlation was observed for both ABC transporter and erm genes in actinobacteria but in streptomycetes only in the erm gene. The type of erm resistance gene sequences was influenced by linkage to 16S rRNA gene sequences and site characteristics. The phylogeny of ABC transporter gene was correlated to 16S rRNA genes mainly above the genus level. The results support the concept of new specific secondary metabolite scaffolds occurring more likely in taxonomically distant producers but suggest that the antibiotic selection of gene pools is also influenced by site conditions.
Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

PubMed

Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I

2017-01-01

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.
Comprehensive RNA-Seq profiling to evaluate lactating sheep mammary gland transcriptome

PubMed Central

Suárez-Vega, Aroa; Gutiérrez-Gil, Beatriz; Klopp, Christophe; Tosser-Klopp, Gwenola; Arranz, Juan-José

2016-01-01

RNA-Seq enables the generation of extensive transcriptome information providing the capability to characterize transcripts (including alternative isoforms and polymorphism), to quantify expression and to identify differential regulation in a single experiment. Our aim in this study was to take advantage of using RNA-Seq high-throughput technology to provide a comprehensive transcriptome profiling of the sheep lactating mammary gland. Eight ewes of two dairy sheep breeds with differences in milk production traits were used in this experiment (four Churra and four Assaf ewes). Milk samples from these animals were collected on days 10, 50, 120 and 150 after lambing to cover the various physiological stages of the mammary gland across the complete lactation. RNA samples were extracted from milk somatic cells. The RNA-Seq dataset was generated using an Illumina HiSeq 2000 sequencer. The information reported here will be useful to understand the biology of lactation in sheep, providing also an opportunity to characterize their different patterns on milk production aptitude. PMID:27377755
Comprehensive RNA-Seq profiling to evaluate lactating sheep mammary gland transcriptome.

PubMed

Suárez-Vega, Aroa; Gutiérrez-Gil, Beatriz; Klopp, Christophe; Tosser-Klopp, Gwenola; Arranz, Juan-José

2016-07-05

RNA-Seq enables the generation of extensive transcriptome information providing the capability to characterize transcripts (including alternative isoforms and polymorphism), to quantify expression and to identify differential regulation in a single experiment. Our aim in this study was to take advantage of using RNA-Seq high-throughput technology to provide a comprehensive transcriptome profiling of the sheep lactating mammary gland. Eight ewes of two dairy sheep breeds with differences in milk production traits were used in this experiment (four Churra and four Assaf ewes). Milk samples from these animals were collected on days 10, 50, 120 and 150 after lambing to cover the various physiological stages of the mammary gland across the complete lactation. RNA samples were extracted from milk somatic cells. The RNA-Seq dataset was generated using an Illumina HiSeq 2000 sequencer. The information reported here will be useful to understand the biology of lactation in sheep, providing also an opportunity to characterize their different patterns on milk production aptitude.
Classification and assessment tools for structural motif discovery algorithms.

PubMed

Badr, Ghada; Al-Turaiki, Isra; Mathkour, Hassan

2013-01-01

Motif discovery is the problem of finding recurring patterns in biological data. Patterns can be sequential, mainly when discovered in DNA sequences. They can also be structural (e.g. when discovering RNA motifs). Finding common structural patterns helps to gain a better understanding of the mechanism of action (e.g. post-transcriptional regulation). Unlike DNA motifs, which are sequentially conserved, RNA motifs exhibit conservation in structure, which may be common even if the sequences are different. Over the past few years, hundreds of algorithms have been developed to solve the sequential motif discovery problem, while less work has been done for the structural case. In this paper, we survey, classify, and compare different algorithms that solve the structural motif discovery problem, where the underlying sequences may be different. We highlight their strengths and weaknesses. We start by proposing a benchmark dataset and a measurement tool that can be used to evaluate different motif discovery approaches. Then, we proceed by proposing our experimental setup. Finally, results are obtained using the proposed benchmark to compare available tools. To the best of our knowledge, this is the first attempt to compare tools solely designed for structural motif discovery. Results show that the accuracy of discovered motifs is relatively low. The results also suggest a complementary behavior among tools where some tools perform well on simple structures, while other tools are better for complex structures. We have classified and evaluated the performance of available structural motif discovery tools. In addition, we have proposed a benchmark dataset with tools that can be used to evaluate newly developed tools.
IM-TORNADO: A Tool for Comparison of 16S Reads from Paired-End Libraries

PubMed Central

Jeraldo, Patricio; Kalari, Krishna; Chen, Xianfeng; Bhavsar, Jaysheel; Mangalam, Ashutosh; White, Bryan; Nelson, Heidi; Kocher, Jean-Pierre; Chia, Nicholas

2014-01-01

Motivation 16S rDNA hypervariable tag sequencing has become the de facto method for accessing microbial diversity. Illumina paired-end sequencing, which produces two separate reads for each DNA fragment, has become the platform of choice for this application. However, when the two reads do not overlap, existing computational pipelines analyze data from read separately and underutilize the information contained in the paired-end reads. Results We created a workflow known as Illinois Mayo Taxon Organization from RNA Dataset Operations (IM-TORNADO) for processing non-overlapping reads while retaining maximal information content. Using synthetic mock datasets, we show that the use of both reads produced answers with greater correlation to those from full length 16S rDNA when looking at taxonomy, phylogeny, and beta-diversity. Availability and Implementation IM-TORNADO is freely available at http://sourceforge.net/projects/imtornado and produces BIOM format output for cross compatibility with other pipelines such as QIIME, mothur, and phyloseq. PMID:25506826
Transcriptome Analysis of the Portunus trituberculatus: De Novo Assembly, Growth-Related Gene Identification and Marker Discovery

PubMed Central

Lv, Jianjian; Liu, Ping; Gao, Baoquan; Wang, Yu; Wang, Zheng; Chen, Ping; Li, Jian

2014-01-01

Background The swimming crab, Portunus trituberculatus, is an important farmed species in China, has been attracting extensive studies, which require more and more genome background knowledge. To date, the sequencing of its whole genome is unavailable and transcriptomic information is also scarce for this species. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive transcript dataset for major tissues of Portunus trituberculatus by the Illumina paired-end sequencing technology. Results Total RNA was isolated from eyestalk, gill, heart, hepatopancreas and muscle. Equal quantities of RNA from each tissue were pooled to construct a cDNA library. Using the Illumina paired-end sequencing technology, we generated a total of 120,137 transcripts with an average length of 1037 bp. Further assembly analysis showed that all contigs contributed to 87,100 unigenes, of these, 16,029 unigenes (18.40% of the total) can be matched in the GenBank non-redundant database. Potential genes and their functions were predicted by GO, KEGG pathway mapping and COG analysis. Based on our sequence analysis and published literature, many putative genes with fundamental roles in growth and muscle development, including actin, myosin, tropomyosin, troponin and other potentially important candidate genes were identified for the first time in this specie. Furthermore, 22,673 SSRs and 66,191 high-confidence SNPs were identified in this EST dataset. Conclusion The transcriptome provides an invaluable new data for a functional genomics resource and future biological research in Portunus trituberculatus. The data will also instruct future functional studies to manipulate or select for genes influencing growth that should find practical applications in aquaculture breeding programs. The molecular markers identified in this study will provide a material basis for future genetic linkage and quantitative trait loci analyses, and will be essential for accelerating aquaculture breeding programs with this species. PMID:24722690
SSP: an interval integer linear programming for de novo transcriptome assembly and isoform discovery of RNA-seq reads.

PubMed

Safikhani, Zhaleh; Sadeghi, Mehdi; Pezeshk, Hamid; Eslahchi, Changiz

2013-01-01

Recent advances in the sequencing technologies have provided a handful of RNA-seq datasets for transcriptome analysis. However, reconstruction of full-length isoforms and estimation of the expression level of transcripts with a low cost are challenging tasks. We propose a novel de novo method named SSP that incorporates interval integer linear programming to resolve alternatively spliced isoforms and reconstruct the whole transcriptome from short reads. Experimental results show that SSP is fast and precise in determining different alternatively spliced isoforms along with the estimation of reconstructed transcript abundances. The SSP software package is available at http://www.bioinf.cs.ipm.ir/software/ssp. © 2013.
RNA Deep Sequencing Reveals Differential MicroRNA Expression during Development of Sea Urchin and Sea Star

PubMed Central

Kadri, Sabah; Hinman, Veronica F.; Benos, Panayiotis V.

2011-01-01

microRNAs (miRNAs) are small (20–23 nt), non-coding single stranded RNA molecules that act as post-transcriptional regulators of mRNA gene expression. They have been implicated in regulation of developmental processes in diverse organisms. The echinoderms, Strongylocentrotus purpuratus (sea urchin) and Patiria miniata (sea star) are excellent model organisms for studying development with well-characterized transcriptional networks. However, to date, nothing is known about the role of miRNAs during development in these organisms, except that the genes that are involved in the miRNA biogenesis pathway are expressed during their developmental stages. In this paper, we used Illumina Genome Analyzer (Illumina, Inc.) to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of sea urchin and sea star (total of 22,670,000 reads). Analysis of these data revealed the miRNA populations in these two species. We found that 47 and 38 known miRNAs are expressed in sea urchin and sea star, respectively, during early development (32 in common). We also found 13 potentially novel miRNAs in the sea urchin embryonic library. miRNA expression is generally conserved between the two species during development, but 7 miRNAs are highly expressed in only one species. We expect that our two datasets will be a valuable resource for everyone working in the field of developmental biology and the regulatory networks that affect it. The computational pipeline to analyze Illumina reads is available at http://www.benoslab.pitt.edu/services.html. PMID:22216218

Indel detection from DNA and RNA sequencing data with transIndel.

PubMed

Yang, Rendong; Van Etten, Jamie L; Dehm, Scott M

2018-04-19

Insertions and deletions (indels) are a major class of genomic variation associated with human disease. Indels are primarily detected from DNA sequencing (DNA-seq) data but their transcriptional consequences remain unexplored due to challenges in discriminating medium-sized and large indels from splicing events in RNA-seq data. Here, we developed transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid-sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. TransIndel exhibits competitive or superior performance over eight state-of-the-art indel detection tools on benchmarks using both synthetic and real DNA-seq data. Additionally, we applied transIndel to DNA-seq and RNA-seq datasets from 333 primary prostate cancer patients from The Cancer Genome Atlas (TCGA) and 59 metastatic prostate cancer patients from AACR-PCF Stand-Up- To-Cancer (SU2C) studies. TransIndel enhanced the taxonomy of DNA- and RNA-level alterations in prostate cancer by identifying recurrent FOXA1 indels as well as exitron splicing in genes implicated in disease progression. Our study demonstrates that transIndel is a robust tool for elucidation of medium- and large-sized indels from DNA-seq and RNA-seq data. Including RNA-seq in indel discovery efforts leads to significant improvements in sensitivity for identification of med-sized and large indels missed by DNA-seq, and reveals non-canonical RNA-splicing events in genes associated with disease pathology.
An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex

PubMed Central

Chen, Kenian; Sloan, Steven A.; Bennett, Mariko L.; Scholze, Anja R.; O'Keeffe, Sean; Phatnani, Hemali P.; Guarnieri, Paolo; Caneda, Christine; Ruderisch, Nadine; Deng, Shuyun; Liddelow, Shane A.; Zhang, Chaolin; Daneman, Richard; Maniatis, Tom; Barres, Ben A.

2014-01-01

The major cell classes of the brain differ in their developmental processes, metabolism, signaling, and function. To better understand the functions and interactions of the cell types that comprise these classes, we acutely purified representative populations of neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes from mouse cerebral cortex. We generated a transcriptome database for these eight cell types by RNA sequencing and used a sensitive algorithm to detect alternative splicing events in each cell type. Bioinformatic analyses identified thousands of new cell type-enriched genes and splicing isoforms that will provide novel markers for cell identification, tools for genetic manipulation, and insights into the biology of the brain. For example, our data provide clues as to how neurons and astrocytes differ in their ability to dynamically regulate glycolytic flux and lactate generation attributable to unique splicing of PKM2, the gene encoding the glycolytic enzyme pyruvate kinase. This dataset will provide a powerful new resource for understanding the development and function of the brain. To ensure the widespread distribution of these datasets, we have created a user-friendly website (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) that provides a platform for analyzing and comparing transciption and alternative splicing profiles for various cell classes in the brain. PMID:25186741
Serum-based six-miRNA signature as a potential marker for EC diagnosis: Comparison with TCGA miRNAseq dataset and identification of miRNA-mRNA target pairs by integrated analysis of TCGA miRNAseq and RNAseq datasets.

PubMed

Sharma, Priyanka; Saraya, Anoop; Sharma, Rinu

2018-01-30

To evaluate the diagnostic potential of a six microRNAs (miRNAs) panel consisting of miR-21, miR-144, miR-107, miR-342, miR-93 and miR-152 for esophageal cancer (EC) detection. The expression of miRNAs was analyzed in EC sera samples using quantitative real-time PCR. Risk score analysis was performed and linear regression models were then fitted to generate the six-miRNA panel. In addition, we made an effort to identify significantly dysregulated miRNAs and mRNAs in EC using the Cancer Genome Atlas (TCGA) miRNAseq and RNAseq datasets, respectively. Further, we identified significantly correlated miRNA-mRNA target pairs by integrating TCGA EC miRNAseq dataset with RNAseq dataset. The panel of circulating miRNAs showed enhanced sensitivity (87.5%) and specificity (90.48%) in terms of discriminating EC patients from normal subjects (area under the curve [AUC] = 0.968). Pathway enrichment analysis for potential targets of six miRNAs revealed 48 significant (P < 0.05) pathways, viz. pathways in cancer, mRNA surveillance, MAPK, Wnt, mTOR signaling, and so on. The expression data for mRNAs and miRNAs, downloaded from TCGA database, lead to identification of 2309 differentially expressed genes and 189 miRNAs. Gene ontology and pathway enrichment analysis showed that cell-cycle processes were most significantly enriched for differentially expressed mRNA. Integrated analysis of TCGA miRNAseq and RNAseq datasets resulted in identification of 53 063 significantly and negatively correlated miRNA-mRNA pairs. In summary, a novel and highly sensitive signature of serum miRNAs was identified for EC detection. Moreover, this is the first report identifying miRNA-mRNA target pairs from EC TCGA dataset, thus providing a comprehensive resource for understanding the interactions existing between miRNA and their target mRNAs in EC. © 2018 John Wiley & Sons Australia, Ltd.
Identification of sequence-structure RNA binding motifs for SELEX-derived aptamers.

PubMed

Hoinka, Jan; Zotenko, Elena; Friedman, Adam; Sauna, Zuben E; Przytycka, Teresa M

2012-06-15

Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process. To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.
Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life

PubMed Central

Sheik, Cody S.; Reese, Brandi Kiel; Twing, Katrina I.; Sylvan, Jason B.; Grim, Sharon L.; Schrenk, Matthew O.; Sogin, Mitchell L.; Colwell, Frederick S.

2018-01-01

Earth’s subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium, Aquabacterium, Ralstonia, and Acinetobacter. While the top five most frequently observed genera were Pseudomonas, Propionibacterium, Acinetobacter, Ralstonia, and Sphingomonas. The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth’s deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset. PMID:29780369
Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life.

PubMed

Sheik, Cody S; Reese, Brandi Kiel; Twing, Katrina I; Sylvan, Jason B; Grim, Sharon L; Schrenk, Matthew O; Sogin, Mitchell L; Colwell, Frederick S

2018-01-01

Earth's subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium , Aquabacterium , Ralstonia , and Acinetobacter . While the top five most frequently observed genera were Pseudomonas , Propionibacterium , Acinetobacter , Ralstonia , and Sphingomonas . The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth's deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset.
TSSer: an automated method to identify transcription start sites in prokaryotic genomes from differential RNA sequencing data.

PubMed

Jorjani, Hadi; Zavolan, Mihaela

2014-04-01

Accurate identification of transcription start sites (TSSs) is an essential step in the analysis of transcription regulatory networks. In higher eukaryotes, the capped analysis of gene expression technology enabled comprehensive annotation of TSSs in genomes such as those of mice and humans. In bacteria, an equivalent approach, termed differential RNA sequencing (dRNA-seq), has recently been proposed, but the application of this approach to a large number of genomes is hindered by the paucity of computational analysis methods. With few exceptions, when the method has been used, annotation of TSSs has been largely done manually. In this work, we present a computational method called 'TSSer' that enables the automatic inference of TSSs from dRNA-seq data. The method rests on a probabilistic framework for identifying both genomic positions that are preferentially enriched in the dRNA-seq data as well as preferentially captured relative to neighboring genomic regions. Evaluating our approach for TSS calling on several publicly available datasets, we find that TSSer achieves high consistency with the curated lists of annotated TSSs, but identifies many additional TSSs. Therefore, TSSer can accelerate genome-wide identification of TSSs in bacterial genomes and can aid in further characterization of bacterial transcription regulatory networks. TSSer is freely available under GPL license at http://www.clipz.unibas.ch/TSSer/index.php
A powerful and flexible approach to the analysis of RNA sequence count data

PubMed Central

Zhou, Yi-Hui; Xia, Kai; Wright, Fred A.

2011-01-01

Motivation: A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean–variance relationships provides a flexible testing regimen that ‘borrows’ information across genes, while easily incorporating design effects and additional covariates. Results: We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean–variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. Availability: An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq Contact: yzhou@bios.unc.edu; fwright@bios.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21810900
Virtual Northern analysis of the human genome.

PubMed

Hurowitz, Evan H; Drori, Iddo; Stodden, Victoria C; Donoho, David L; Brown, Patrick O

2007-05-23

We applied the Virtual Northern technique to human brain mRNA to systematically measure human mRNA transcript lengths on a genome-wide scale. We used separation by gel electrophoresis followed by hybridization to cDNA microarrays to measure 8,774 mRNA transcript lengths representing at least 6,238 genes at high (>90%) confidence. By comparing these transcript lengths to the Refseq and H-Invitational full-length cDNA databases, we found that nearly half of our measurements appeared to represent novel transcript variants. Comparison of length measurements determined by hybridization to different cDNAs derived from the same gene identified clones that potentially correspond to alternative transcript variants. We observed a close linear relationship between ORF and mRNA lengths in human mRNAs, identical in form to the relationship we had previously identified in yeast. Some functional classes of protein are encoded by mRNAs whose untranslated regions (UTRs) tend to be longer or shorter than average; these functional classes were similar in both human and yeast. Human transcript diversity is extensive and largely unannotated. Our length dataset can be used as a new criterion for judging the completeness of cDNAs and annotating mRNA sequences. Similar relationships between the lengths of the UTRs in human and yeast mRNAs and the functions of the proteins they encode suggest that UTR sequences serve an important regulatory role among eukaryotes.
The First Complete Chloroplast Genome Sequences in Actinidiaceae: Genome Structure and Comparative Analysis.

PubMed

Yao, Xiaohong; Tang, Ping; Li, Zuozhou; Li, Dawei; Liu, Yifei; Huang, Hongwen

2015-01-01

Actinidia chinensis is an important economic plant belonging to the basal lineage of the asterids. Availability of a complete Actinidia chloroplast genome sequence is crucial to understanding phylogenetic relationships among major lineages of angiosperms and facilitates kiwifruit genetic improvement. We report here the complete nucleotide sequences of the chloroplast genomes for Actinidia chinensis and A. chinensis var deliciosa obtained through de novo assembly of Illumina paired-end reads produced by total DNA sequencing. The total genome size ranges from 155,446 to 157,557 bp, with an inverted repeat (IR) of 24,013 to 24,391 bp, a large single copy region (LSC) of 87,984 to 88,337 bp and a small single copy region (SSC) of 20,332 to 20,336 bp. The genome encodes 113 different genes, including 79 unique protein-coding genes, 30 tRNA genes and 4 ribosomal RNA genes, with 16 duplicated in the inverted repeats, and a tRNA gene (trnfM-CAU) duplicated once in the LSC region. Comparisons of IR boundaries among four asterid species showed that IR/LSC borders were extended into the 5' portion of the psbA gene and IR contraction occurred in Actinidia. The clap gene has been lost from the chloroplast genome in Actinidia, and may have been transferred to the nucleus during chloroplast evolution. Twenty-seven polymorphic simple sequence repeat (SSR) loci were identified in the Actinidia chloroplast genome. Maximum parsimony analyses of a 72-gene, 16 taxa angiosperm dataset strongly support the placement of Actinidiaceae in Ericales within the basal asterids.
Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph.

PubMed

Benoit, Gaëtan; Lemaitre, Claire; Lavenier, Dominique; Drezen, Erwan; Dayris, Thibault; Uricaru, Raluca; Rizk, Guillaume

2015-09-14

Data volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method. We present a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Our approach, implemented in the software LEON, employs techniques derived from existing assembly principles. The method is based on a reference probabilistic de Bruijn Graph, built de novo from the set of reads and stored in a Bloom filter. Each read is encoded as a path in this graph, by memorizing an anchoring kmer and a list of bifurcations. The same probabilistic de Bruijn Graph is used to perform a lossy transformation of the quality scores, which allows to obtain higher compression rates without losing pertinent information for downstream analyses. LEON was run on various real sequencing datasets (whole genome, exome, RNA-seq or metagenomics). In all cases, LEON showed higher overall compression ratios than state-of-the-art compression software. On a C. elegans whole genome sequencing dataset, LEON divided the original file size by more than 20. LEON is an open source software, distributed under GNU affero GPL License, available for download at http://gatb.inria.fr/software/leon/.
Translational resistivity/conductivity of coding sequences during exponential growth of Escherichia coli.

PubMed

Takai, Kazuyuki

2017-01-21

Codon adaptation index (CAI) has been widely used for prediction of expression of recombinant genes in Escherichia coli and other organisms. However, CAI has no mechanistic basis that rationalizes its application to estimation of translational efficiency. Here, I propose a model based on which we could consider how codon usage is related to the level of expression during exponential growth of bacteria. In this model, translation of a gene is considered as an analog of electric current, and an analog of electric resistance corresponding to each gene is considered. "Translational resistance" is dependent on the steady-state concentration and the sequence of the mRNA species, and "translational resistivity" is dependent only on the mRNA sequence. The latter is the sum of two parts: one is the resistivity for the elongation reaction (coding sequence resistivity), and the other comes from all of the other steps of the decoding reaction. This electric circuit model clearly shows that some conditions should be met for codon composition of a coding sequence to correlate well with its expression level. On the other hand, I calculated relative frequency of each of the 61 sense codon triplets translated during exponential growth of E. coli from a proteomic dataset covering over 2600 proteins. A tentative method for estimating relative coding sequence resistivity based on the data is presented. Copyright Â© 2016. Published by Elsevier Ltd.
SEASTAR: systematic evaluation of alternative transcription start sites in RNA.

PubMed

Qin, Zhiyi; Stoilov, Peter; Zhang, Xuegong; Xing, Yi

2018-05-04

Alternative first exons diversify the transcriptomes of eukaryotes by producing variants of the 5' Untranslated Regions (5'UTRs) and N-terminal coding sequences. Accurate transcriptome-wide detection of alternative first exons typically requires specialized experimental approaches that are designed to identify the 5' ends of transcripts. We developed a computational pipeline SEASTAR that identifies first exons from RNA-seq data alone then quantifies and compares alternative first exon usage across multiple biological conditions. The exons inferred by SEASTAR coincide with transcription start sites identified directly by CAGE experiments and bear epigenetic hallmarks of active promoters. To determine if differential usage of alternative first exons can yield insights into the mechanism controlling gene expression, we applied SEASTAR to an RNA-seq dataset that tracked the reprogramming of mouse fibroblasts into induced pluripotent stem cells. We observed dynamic temporal changes in the usage of alternative first exons, along with correlated changes in transcription factor expression. Using a combined sequence motif and gene set enrichment analysis we identified N-Myc as a regulator of alternative first exon usage in the pluripotent state. Our results demonstrate that SEASTAR can leverage the available RNA-seq data to gain insights into the control of gene expression and alternative transcript variation in eukaryotic transcriptomes.
Association of coral algal symbionts with a diverse viral community responsive to heat shock.

PubMed

Brüwer, Jan D; Agrawal, Shobhit; Liew, Yi Jin; Aranda, Manuel; Voolstra, Christian R

2017-08-17

Stony corals provide the structural foundation of coral reef ecosystems and are termed holobionts given they engage in symbioses, in particular with photosynthetic dinoflagellates of the genus Symbiodinium. Besides Symbiodinium, corals also engage with bacteria affecting metabolism, immunity, and resilience of the coral holobiont, but the role of associated viruses is largely unknown. In this regard, the increase of studies using RNA sequencing (RNA-Seq) to assess gene expression provides an opportunity to elucidate viral signatures encompassed within the data via careful delineation of sequence reads and their source of origin. Here, we re-analyzed an RNA-Seq dataset from a cultured coral symbiont (Symbiodinium microadriaticum, Clade A1) across four experimental treatments (control, cold shock, heat shock, dark shock) to characterize associated viral diversity, abundance, and gene expression. Our approach comprised the filtering and removal of host sequence reads, subsequent phylogenetic assignment of sequence reads of putative viral origin, and the assembly and analysis of differentially expressed viral genes. About 15.46% (123 million) of all sequence reads were non-host-related, of which <1% could be classified as archaea, bacteria, or virus. Of these, 18.78% were annotated as virus and comprised a diverse community consistent across experimental treatments. Further, non-host related sequence reads assembled into 56,064 contigs, including 4856 contigs of putative viral origin that featured 43 differentially expressed genes during heat shock. The differentially expressed genes included viral kinases, ubiquitin, and ankyrin repeat proteins (amongst others), which are suggested to help the virus proliferate and inhibit the algal host's antiviral response. Our results suggest that a diverse viral community is associated with coral algal endosymbionts of the genus Symbiodinium, which prompts further research on their ecological role in coral health and resilience.
GenomicTools: a computational platform for developing high-throughput analytics in genomics.

PubMed

Tsirigos, Aristotelis; Haiminen, Niina; Bilal, Erhan; Utro, Filippo

2012-01-15

Recent advances in sequencing technology have resulted in the dramatic increase of sequencing data, which, in turn, requires efficient management of computational resources, such as computing time, memory requirements as well as prototyping of computational pipelines. We present GenomicTools, a flexible computational platform, comprising both a command-line set of tools and a C++ API, for the analysis and manipulation of high-throughput sequencing data such as DNA-seq, RNA-seq, ChIP-seq and MethylC-seq. GenomicTools implements a variety of mathematical operations between sets of genomic regions thereby enabling the prototyping of computational pipelines that can address a wide spectrum of tasks ranging from pre-processing and quality control to meta-analyses. Additionally, the GenomicTools platform is designed to analyze large datasets of any size by minimizing memory requirements. In practical applications, where comparable, GenomicTools outperforms existing tools in terms of both time and memory usage. The GenomicTools platform (version 2.0.0) was implemented in C++. The source code, documentation, user manual, example datasets and scripts are available online at http://code.google.com/p/ibm-cbc-genomic-tools.
Phylotranscriptomic consolidation of the jawed vertebrate timetree.

PubMed

Irisarri, Iker; Baurain, Denis; Brinkmann, Henner; Delsuc, Frédéric; Sire, Jean-Yves; Kupfer, Alexander; Petersen, Jörn; Jarek, Michael; Meyer, Axel; Vences, Miguel; Philippe, Hervé

2017-09-01

Phylogenomics is extremely powerful but introduces new challenges as no agreement exists on "standards" for data selection, curation and tree inference. We use jawed vertebrates (Gnathostomata) as model to address these issues. Despite considerable efforts in resolving their evolutionary history and macroevolution, few studies have included a full phylogenetic diversity of gnathostomes and some relationships remain controversial. We tested a novel bioinformatic pipeline to assemble large and accurate phylogenomic datasets from RNA sequencing and find this phylotranscriptomic approach successful and highly cost-effective. Increased sequencing effort up to ca. 10Gbp allows recovering more genes, but shallower sequencing (1.5Gbp) is sufficient to obtain thousands of full-length orthologous transcripts. We reconstruct a robust and strongly supported timetree of jawed vertebrates using 7,189 nuclear genes from 100 taxa, including 23 new transcriptomes from previously unsampled key species. Gene jackknifing of genomic data corroborates the robustness of our tree and allows calculating genome-wide divergence times by overcoming gene sampling bias. Mitochondrial genomes prove insufficient to resolve the deepest relationships because of limited signal and among-lineage rate heterogeneity. Our analyses emphasize the importance of large curated nuclear datasets to increase the accuracy of phylogenomics and provide a reference framework for the evolutionary history of jawed vertebrates.
GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data.

PubMed

Chen, Li; Reeve, James; Zhang, Lujun; Huang, Shengbing; Wang, Xuefeng; Chen, Jun

2018-01-01

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero-inflation remain largely undeveloped. Here we propose geometric mean of pairwise ratios-a simple but effective normalization method-for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.
The VirusBanker database uses a Java program to allow flexible searching through Bunyaviridae sequences.

PubMed

Fourment, Mathieu; Gibbs, Mark J

2008-02-05

Viruses of the Bunyaviridae have segmented negative-stranded RNA genomes and several of them cause significant disease. Many partial sequences have been obtained from the segments so that GenBank searches give complex results. Sequence databases usually use HTML pages to mediate remote sorting, but this approach can be limiting and may discourage a user from exploring a database. The VirusBanker database contains Bunyaviridae sequences and alignments and is presented as two spreadsheets generated by a Java program that interacts with a MySQL database on a server. Sequences are displayed in rows and may be sorted using information that is displayed in columns and includes data relating to the segment, gene, protein, species, strain, sequence length, terminal sequence and date and country of isolation. Bunyaviridae sequences and alignments may be downloaded from the second spreadsheet with titles defined by the user from the columns, or viewed when passed directly to the sequence editor, Jalview. VirusBanker allows large datasets of aligned nucleotide and protein sequences from the Bunyaviridae to be compiled and winnowed rapidly using criteria that are formulated heuristically.
Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding.

PubMed

Wu, Johnny C; Gardner, David P; Ozer, Stuart; Gutell, Robin R; Ren, Pengyu

2009-08-28

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.
The green impact: bacterioplankton response toward a phytoplankton spring bloom in the southern North Sea assessed by comparative metagenomic and metatranscriptomic approaches

PubMed Central

Wemheuer, Bernd; Wemheuer, Franziska; Hollensteiner, Jacqueline; Meyer, Frauke-Dorothee; Voget, Sonja; Daniel, Rolf

2015-01-01

Phytoplankton blooms exhibit a severe impact on bacterioplankton communities as they change nutrient availabilities and other environmental factors. In the current study, the response of a bacterioplankton community to a Phaeocystis globosa spring bloom was investigated in the southern North Sea. For this purpose, water samples were taken inside and reference samples outside of an algal spring bloom. Structural changes of the bacterioplankton community were assessed by amplicon-based analysis of 16S rRNA genes and transcripts generated from environmental DNA and RNA, respectively. Several marine groups responded to bloom presence. The abundance of the Roseobacter RCA cluster and the SAR92 clade significantly increased in bloom presence in the total and active fraction of the bacterial community. Functional changes were investigated by direct sequencing of environmental DNA and mRNA. The corresponding datasets comprised more than 500 million sequences across all samples. Metatranscriptomic data sets were mapped on representative genomes of abundant marine groups present in the samples and on assembled metagenomic and metatranscriptomic datasets. Differences in gene expression profiles between non-bloom and bloom samples were recorded. The genome-wide gene expression level of Planktomarina temperata, an abundant member of the Roseobacter RCA cluster, was higher inside the bloom. Genes that were differently expressed included transposases, which showed increased expression levels inside the bloom. This might contribute to the adaptation of this organism toward environmental stresses through genome reorganization. In addition, several genes affiliated to the SAR92 clade were significantly upregulated inside the bloom including genes encoding for proteins involved in isoleucine and leucine incorporation. Obtained results provide novel insights into compositional and functional variations of marine bacterioplankton communities as response to a phytoplankton bloom. PMID:26322028

An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development

DOE PAGES

Stelpflug, Scott C.; Sekhon, Rajandeep S.; Vaillancourt, Brieanne; ...

2015-12-30

Comprehensive and systematic transcriptome profiling provides valuable insight into biological and developmental processes that occur throughout the life cycle of a plant. We have enhanced our previously published microarray-based gene atlas of maize ( Zea mays L.) inbred B73 to now include 79 distinct replicated samples that have been interrogated using RNA sequencing (RNA-seq). The current version of the atlas includes 50 original array-based gene atlas samples, a time-course of 12 stalk and leaf samples postflowering, and an additional set of 17 samples from the maize seedling and adult root system. The entire dataset contains 4.6 billion mapped reads, withmore » an average of 20.5 million mapped reads per biological replicate, allowing for detection of genes with lower transcript abundance. As the new root samples represent key additions to the previously examined tissues, we highlight insights into the root transcriptome, which is represented by 28,894 (73.2%) annotated genes in maize. Additionally, we observed remarkable expression differences across both the longitudinal (four zones) and radial gradients (cortical parenchyma and stele) of the primary root supported by fourfold differential expression of 9353 and 4728 genes, respectively. Among the latter were 1110 genes that encode transcription factors, some of which are orthologs of previously characterized transcription factors known to regulate root development in Arabidopsis thaliana (L.) Heynh., while most are novel, and represent attractive targets for reverse genetics approaches to determine their roles in this important organ. As a result, this comprehensive transcriptome dataset is a powerful tool toward understanding maize development, physiology, and phenotypic diversity.« less
An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stelpflug, Scott C.; Sekhon, Rajandeep S.; Vaillancourt, Brieanne

Comprehensive and systematic transcriptome profiling provides valuable insight into biological and developmental processes that occur throughout the life cycle of a plant. We have enhanced our previously published microarray-based gene atlas of maize ( Zea mays L.) inbred B73 to now include 79 distinct replicated samples that have been interrogated using RNA sequencing (RNA-seq). The current version of the atlas includes 50 original array-based gene atlas samples, a time-course of 12 stalk and leaf samples postflowering, and an additional set of 17 samples from the maize seedling and adult root system. The entire dataset contains 4.6 billion mapped reads, withmore » an average of 20.5 million mapped reads per biological replicate, allowing for detection of genes with lower transcript abundance. As the new root samples represent key additions to the previously examined tissues, we highlight insights into the root transcriptome, which is represented by 28,894 (73.2%) annotated genes in maize. Additionally, we observed remarkable expression differences across both the longitudinal (four zones) and radial gradients (cortical parenchyma and stele) of the primary root supported by fourfold differential expression of 9353 and 4728 genes, respectively. Among the latter were 1110 genes that encode transcription factors, some of which are orthologs of previously characterized transcription factors known to regulate root development in Arabidopsis thaliana (L.) Heynh., while most are novel, and represent attractive targets for reverse genetics approaches to determine their roles in this important organ. As a result, this comprehensive transcriptome dataset is a powerful tool toward understanding maize development, physiology, and phenotypic diversity.« less
A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data.

PubMed

Cui, Xiaodong; Meng, Jia; Zhang, Shaowu; Rao, Manjeet K; Chen, Yidong; Huang, Yufei

2016-08-22

The recent advent of the state-of-art high throughput sequencing technology, known as Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-seq) revolutionizes the area of mRNA epigenetics and enables the biologists and biomedical researchers to have a global view of N (6)-Methyladenosine (m(6)A) on transcriptome. Yet there is a significant need for new computation tools for processing and analysing MeRIP-Seq data to gain a further insight into the function and m(6)A mRNA methylation. We developed a novel algorithm and an open source R package ( http://compgenomics.utsa.edu/metcluster ) for uncovering the potential types of m(6)A methylation by clustering the degree of m(6)A methylation peaks in MeRIP-Seq data. This algorithm utilizes a hierarchical graphical model to model the reads account variance and the underlying clusters of the methylation peaks. Rigorous statistical inference is performed to estimate the model parameter and detect the number of clusters. MeTCluster is evaluated on both simulated and real MeRIP-seq datasets and the results demonstrate its high accuracy in characterizing the clusters of methylation peaks. Our algorithm was applied to two different sets of real MeRIP-seq datasets and reveals a novel pattern that methylation peaks with less peak enrichment tend to clustered in the 5' end of both in both mRNAs and lncRNAs, whereas those with higher peak enrichment are more likely to be distributed in CDS and towards the 3'end of mRNAs and lncRNAs. This result might suggest that m(6)A's functions could be location specific. In this paper, a novel hierarchical graphical model based algorithm was developed for clustering the enrichment of methylation peaks in MeRIP-seq data. MeTCluster is written in R and is publicly available.
DMirNet: Inferring direct microRNA-mRNA association networks.

PubMed

Lee, Minsu; Lee, HyungJune

2016-12-05

MicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles. 1) Inferred correlations from the observed expression profiles using conventional correlation-based methods include numerous erroneous links or over-estimated edge weight due to the transitive information flow among direct associations. 2) Due to the high-dimension-low-sample-size problem on the microarray dataset, it is difficult to obtain an accurate and reliable estimate of the empirical correlations between all pairs of expression profiles. 3) Because the previously proposed computational methods usually suffer from varying performance across different datasets, a more reliable model that guarantees optimal or suboptimal performance across different datasets is highly needed. In this paper, we present DMirNet, a new framework for identifying direct miRNA-mRNA association networks. To tackle the aforementioned issues, DMirNet incorporates 1) three direct correlation estimation methods (namely Corpcor, SPACE, Network deconvolution) to infer direct miRNA-mRNA association networks, 2) the bootstrapping method to fully utilize insufficient training expression profiles, and 3) a rank-based Ensemble aggregation to build a reliable and robust model across different datasets. Our empirical experiments on three datasets demonstrate the combinatorial effects of necessary components in DMirNet. Additional performance comparison experiments show that DMirNet outperforms the state-of-the-art Ensemble-based model [1] which has shown the best performance across the same three datasets, with a factor of up to 1.29. Further, we identify 43 putative novel multi-cancer-related miRNA-mRNA association relationships from an inferred Top 1000 direct miRNA-mRNA association network. We believe that DMirNet is a promising method to identify novel direct miRNA-mRNA relations and to elucidate the direct miRNA-mRNA association networks. Since DMirNet infers direct relationships from the observed data, DMirNet can contribute to reconstructing various direct regulatory pathways, including, but not limited to, the direct miRNA-mRNA association networks.
GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda

PubMed Central

2014-01-01

Background Non-coding sequences such as microRNAs have important roles in disease processes. Computational microRNA target identification (CMTI) is becoming increasingly important since traditional experimental methods for target identification pose many difficulties. These methods are time-consuming, costly, and often need guidance from computational methods to narrow down candidate genes anyway. However, most CMTI methods are computationally demanding, since they need to handle not only several million query microRNA and reference RNA pairs, but also several million nucleotide comparisons within each given pair. Thus, the need to perform microRNA identification at such large scale has increased the demand for parallel computing. Methods Although most CMTI programs (e.g., the miRanda algorithm) are based on a modified Smith-Waterman (SW) algorithm, the existing parallel SW implementations (e.g., CUDASW++ 2.0/3.0, SWIPE) are unable to meet this demand in CMTI tasks. We present CUDA-miRanda, a fast microRNA target identification algorithm that takes advantage of massively parallel computing on Graphics Processing Units (GPU) using NVIDIA's Compute Unified Device Architecture (CUDA). CUDA-miRanda specifically focuses on the local alignment of short (i.e., ≤ 32 nucleotides) sequences against longer reference sequences (e.g., 20K nucleotides). Moreover, the proposed algorithm is able to report multiple alignments (up to 191 top scores) and the corresponding traceback sequences for any given (query sequence, reference sequence) pair. Results Speeds over 5.36 Giga Cell Updates Per Second (GCUPs) are achieved on a server with 4 NVIDIA Tesla M2090 GPUs. Compared to the original miRanda algorithm, which is evaluated on an Intel Xeon E5620@2.4 GHz CPU, the experimental results show up to 166 times performance gains in terms of execution time. In addition, we have verified that the exact same targets were predicted in both CUDA-miRanda and the original miRanda implementations through multiple test datasets. Conclusions We offer a GPU-based alternative to high performance compute (HPC) that can be developed locally at a relatively small cost. The community of GPU developers in the biomedical research community, particularly for genome analysis, is still growing. With increasing shared resources, this community will be able to advance CMTI in a very significant manner. Our source code is available at https://sourceforge.net/projects/cudamiranda/. PMID:25077821
(GTG)5 MSP-PCR fingerprinting as a technique for discrimination of wine associated yeasts?

PubMed

Ramírez-Castrillón, Mauricio; Mendes, Sandra Denise Camargo; Inostroza-Ponta, Mario; Valente, Patricia

2014-01-01

In microbiology, identification of all isolates by sequencing is still unfeasible in small research laboratories. Therefore, many yeast diversity studies follow a screening procedure consisting of clustering the yeast isolates using MSP-PCR fingerprinting, followed by identification of one or a few selected representatives of each cluster by sequencing. Although this procedure has been widely applied in the literature, it has not been properly validated. We evaluated a standardized protocol using MSP-PCR fingerprinting with the primers (GTG)5 and M13 for the discrimination of wine associated yeasts in South Brazil. Two datasets were used: yeasts isolated from bottled wines and vineyard environments. We compared the discriminatory power of both primers in a subset of 16 strains, choosing the primer (GTG)5 for further evaluation. Afterwards, we applied this technique to 245 strains, and compared the results with the identification obtained by partial sequencing of the LSU rRNA gene, considered as the gold standard. An array matrix was constructed for each dataset and used as input for clustering with two methods (hierarchical dendrograms and QAPGrid layout). For both yeast datasets, unrelated species were clustered in the same group. The sensitivity score of (GTG)5 MSP-PCR fingerprinting was high, but specificity was low. As a conclusion, the yeast diversity inferred in several previous studies may have been underestimated and some isolates were probably misidentified due to the compliance to this screening procedure.
(GTG)5 MSP-PCR Fingerprinting as a Technique for Discrimination of Wine Associated Yeasts?

PubMed Central

Inostroza-Ponta, Mario; Valente, Patricia

2014-01-01

In microbiology, identification of all isolates by sequencing is still unfeasible in small research laboratories. Therefore, many yeast diversity studies follow a screening procedure consisting of clustering the yeast isolates using MSP-PCR fingerprinting, followed by identification of one or a few selected representatives of each cluster by sequencing. Although this procedure has been widely applied in the literature, it has not been properly validated. We evaluated a standardized protocol using MSP-PCR fingerprinting with the primers (GTG)5 and M13 for the discrimination of wine associated yeasts in South Brazil. Two datasets were used: yeasts isolated from bottled wines and vineyard environments. We compared the discriminatory power of both primers in a subset of 16 strains, choosing the primer (GTG)5 for further evaluation. Afterwards, we applied this technique to 245 strains, and compared the results with the identification obtained by partial sequencing of the LSU rRNA gene, considered as the gold standard. An array matrix was constructed for each dataset and used as input for clustering with two methods (hierarchical dendrograms and QAPGrid layout). For both yeast datasets, unrelated species were clustered in the same group. The sensitivity score of (GTG)5 MSP-PCR fingerprinting was high, but specificity was low. As a conclusion, the yeast diversity inferred in several previous studies may have been underestimated and some isolates were probably misidentified due to the compliance to this screening procedure. PMID:25171185
A Comprehensive, Automatically Updated Fungal ITS Sequence Dataset for Reference-Based Chimera Control in Environmental Sequencing Efforts.

PubMed

Nilsson, R Henrik; Tedersoo, Leho; Ryberg, Martin; Kristiansson, Erik; Hartmann, Martin; Unterseher, Martin; Porter, Teresita M; Bengtsson-Palme, Johan; Walker, Donald M; de Sousa, Filipe; Gamper, Hannes Andres; Larsson, Ellen; Larsson, Karl-Henrik; Kõljalg, Urmas; Edgar, Robert C; Abarenkov, Kessy

2015-01-01

The nuclear ribosomal internal transcribed spacer (ITS) region is the most commonly chosen genetic marker for the molecular identification of fungi in environmental sequencing and molecular ecology studies. Several analytical issues complicate such efforts, one of which is the formation of chimeric-artificially joined-DNA sequences during PCR amplification or sequence assembly. Several software tools are currently available for chimera detection, but rely to various degrees on the presence of a chimera-free reference dataset for optimal performance. However, no such dataset is available for use with the fungal ITS region. This study introduces a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database for the molecular identification of fungi. This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. The performance of the dataset on a large set of artificial chimeras was above 99.5%, and we subsequently used the dataset to remove nearly 1,000 compromised fungal ITS sequences from public circulation. The dataset is available at http://unite.ut.ee/repository.php and is subject to web-based third-party curation.
A Comprehensive, Automatically Updated Fungal ITS Sequence Dataset for Reference-Based Chimera Control in Environmental Sequencing Efforts

PubMed Central

Nilsson, R. Henrik; Tedersoo, Leho; Ryberg, Martin; Kristiansson, Erik; Hartmann, Martin; Unterseher, Martin; Porter, Teresita M.; Bengtsson-Palme, Johan; Walker, Donald M.; de Sousa, Filipe; Gamper, Hannes Andres; Larsson, Ellen; Larsson, Karl-Henrik; Kõljalg, Urmas; Edgar, Robert C.; Abarenkov, Kessy

2015-01-01

The nuclear ribosomal internal transcribed spacer (ITS) region is the most commonly chosen genetic marker for the molecular identification of fungi in environmental sequencing and molecular ecology studies. Several analytical issues complicate such efforts, one of which is the formation of chimeric—artificially joined—DNA sequences during PCR amplification or sequence assembly. Several software tools are currently available for chimera detection, but rely to various degrees on the presence of a chimera-free reference dataset for optimal performance. However, no such dataset is available for use with the fungal ITS region. This study introduces a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database for the molecular identification of fungi. This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. The performance of the dataset on a large set of artificial chimeras was above 99.5%, and we subsequently used the dataset to remove nearly 1,000 compromised fungal ITS sequences from public circulation. The dataset is available at http://unite.ut.ee/repository.php and is subject to web-based third-party curation. PMID:25786896
Physiological recordings and RNA sequencing of the gustatory appendages of the yellow-fever mosquito Aedes aegypti.

PubMed

Sparks, Jackson T; Dickens, Joseph C

2014-12-30

Electrophysiological recording of action potentials from sensory neurons of mosquitoes provides investigators a glimpse into the chemical perception of these disease vectors. We have recently identified a bitter sensing neuron in the labellum of female Aedes aegypti that responds to DEET and other repellents, as well as bitter quinine, through direct electrophysiological investigation. These gustatory receptor neuron responses prompted our sequencing of total mRNA from both male and female labella and tarsi samples to elucidate the putative chemoreception genes expressed in these contact chemoreception tissues. Samples of tarsi were divided into pro-, meso- and metathoracic subtypes for both sexes. We then validated our dataset by conducting qRT-PCR on the same tissue samples and used statistical methods to compare results between the two methods. Studies addressing molecular function may now target specific genes to determine those involved in repellent perception by mosquitoes. These receptor pathways may be used to screen novel repellents towards disruption of host-seeking behavior to curb the spread of harmful viruses.
Genome-wide methylation analysis identified sexually dimorphic methylated regions in hybrid tilapia

PubMed Central

Wan, Zi Yi; Xia, Jun Hong; Lin, Grace; Wang, Le; Lin, Valerie C. L.; Yue, Gen Hua

2016-01-01

Sexual dimorphism is an interesting biological phenomenon. Previous studies showed that DNA methylation might play a role in sexual dimorphism. However, the overall picture of the genome-wide methylation landscape in sexually dimorphic species remains unclear. We analyzed the DNA methylation landscape and transcriptome in hybrid tilapia (Oreochromis spp.) using whole genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq). We found 4,757 sexually dimorphic differentially methylated regions (DMRs), with significant clusters of DMRs located on chromosomal regions associated with sex determination. CpG methylation in promoter regions was negatively correlated with the gene expression level. MAPK/ERK pathway was upregulated in male tilapia. We also inferred active cis-regulatory regions (ACRs) in skeletal muscle tissues from WGBS datasets, revealing sexually dimorphic cis-regulatory regions. These results suggest that DNA methylation contribute to sex-specific phenotypes and serve as resources for further investigation to analyze the functions of these regions and their contributions towards sexual dimorphisms. PMID:27782217
Discovery of Culex pipiens associated tunisia virus: a new ssRNA(+) virus representing a new insect associated virus family

PubMed Central

Bigot, Diane; Atyame, Célestine M; Weill, Mylène; Justy, Fabienne

2018-01-01

Abstract In the global context of arboviral emergence, deep sequencing unlocks the discovery of new mosquito-borne viruses. Mosquitoes of the species Culex pipiens, C. torrentium, and C. hortensis were sampled from 22 locations worldwide for transcriptomic analyses. A virus discovery pipeline was used to analyze the dataset of 0.7 billion reads comprising 22 individual transcriptomes. Two closely related 6.8 kb viral genomes were identified in C. pipiens and named as Culex pipiens associated tunisia virus (CpATV) strains Ayed and Jedaida. The CpATV genome contained four ORFs. ORF1 possessed helicase and RNA-dependent RNA polymerase (RdRp) domains related to new viral sequences recently found mainly in dipterans. ORF2 and 4 contained a capsid protein domain showing strong homology with Virgaviridae plant viruses. ORF3 displayed similarities with eukaryotic Rhoptry domain and a merozoite surface protein (MSP7) domain only found in mosquito-transmitted Plasmodium, suggesting possible interactions between CpATV and vertebrate cells. Estimation of a strong purifying selection exerted on each ORFs and the presence of a polymorphism maintained in the coding region of ORF3 suggested that both CpATV sequences are genuine functional viruses. CpATV is part of an entirely new and highly diversified group of viruses recently found in insects, and that bears the genomic hallmarks of a new viral family. PMID:29340209
New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.
Insight into the validity of Leptobrachium guangxiense (Anura: Megophryidae): evidence from mitochondrial DNA sequences and morphological characters.

PubMed

Chen, Weicai; Zhang, Wei; Zhou, Shichu; Li, Ning; Huang, Yong; Mo, Yunming

2013-01-01

Lepobrachiun guangxiense Fei, Mo, Ye and Jiang, 2009 (Anura: Megophryidae), is presently thought to be endemic to Shangsi, Guangxi Province, China. A molecular phylogenetic analysis and morphological data were performed to gain insight into the phylogenetic position of this species. Maximum parsimony, maximum likelihood, and Bayesian inference methods were employed to reconstruct phylogenetic relationship, using 1914 bp of sequences from mtDNA genes of 12S rRNA, tRNAVal and 16S rRNA. Topologies revealed that L. guangxiense and Tam Dao (Vietnam) L. chapaense lineage (3A) formed a monophyletic group with well-supported values. The uncorrected p-distance of ~1.4k bp 16S rRNA data-sets between Tam Dao L. chapaense lineage (3A) and L. guangxiense is only 0.1%. Morphologically, L. guangxiense and Tam Dao L. chapaense lineage (3A) shared the same characters, and are distinguishable from "true" L. chapaense from the type locality in Sa Pa, Vietnam. Based on morphological characters and mitochondrial DNA, we suggested that the Tam Dao lineages of L. chapaense are conspecific with L. guangxiense. This represents a range extension for L. guangxiense, and a new country record for Vietnam.
A Higher-Order Generalized Singular Value Decomposition for Comparison of Global mRNA Expression from Multiple Organisms

PubMed Central

Ponnapalli, Sri Priya; Saunders, Michael A.; Van Loan, Charles F.; Alter, Orly

2011-01-01

The number of high-dimensional datasets recording multiple aspects of a single phenomenon is increasing in many areas of science, accompanied by a need for mathematical frameworks that can compare multiple large-scale matrices with different row dimensions. The only such framework to date, the generalized singular value decomposition (GSVD), is limited to two matrices. We mathematically define a higher-order GSVD (HO GSVD) for N≥2 matrices , each with full column rank. Each matrix is exactly factored as Di = UiΣiVT, where V, identical in all factorizations, is obtained from the eigensystem SV = VΛ of the arithmetic mean S of all pairwise quotients of the matrices , i≠j. We prove that this decomposition extends to higher orders almost all of the mathematical properties of the GSVD. The matrix S is nondefective with V and Λ real. Its eigenvalues satisfy λk≥1. Equality holds if and only if the corresponding eigenvector vk is a right basis vector of equal significance in all matrices Di and Dj, that is σi,k/σj,k = 1 for all i and j, and the corresponding left basis vector ui,k is orthogonal to all other vectors in Ui for all i. The eigenvalues λk = 1, therefore, define the “common HO GSVD subspace.” We illustrate the HO GSVD with a comparison of genome-scale cell-cycle mRNA expression from S. pombe, S. cerevisiae and human. Unlike existing algorithms, a mapping among the genes of these disparate organisms is not required. We find that the approximately common HO GSVD subspace represents the cell-cycle mRNA expression oscillations, which are similar among the datasets. Simultaneous reconstruction in the common subspace, therefore, removes the experimental artifacts, which are dissimilar, from the datasets. In the simultaneous sequence-independent classification of the genes of the three organisms in this common subspace, genes of highly conserved sequences but significantly different cell-cycle peak times are correctly classified. PMID:22216090
The largest subunit of RNA polymerase II as a new marker gene to study assemblages of arbuscular mycorrhizal fungi in the field.

PubMed

Stockinger, Herbert; Peyret-Guzzon, Marine; Koegel, Sally; Bouffaud, Marie-Lara; Redecker, Dirk

2014-01-01

Due to the potential of arbuscular mycorrhizal fungi (AMF, Glomeromycota) to improve plant growth and soil quality, the influence of agricultural practice on their diversity continues to be an important research question. Up to now studies of community diversity in AMF have exclusively been based on nuclear ribosomal gene regions, which in AMF show high intra-organism polymorphism, seriously complicating interpretation of these data. We designed specific PCR primers for 454 sequencing of a region of the largest subunit of RNA polymerase II gene, and established a new reference dataset comprising all major AMF lineages. This gene is known to be monomorphic within fungal isolates but shows an excellent barcode gap between species. We designed a primer set to amplify all known lineages of AMF and demonstrated its applicability in combination with high-throughput sequencing in a long-term tillage experiment. The PCR primers showed a specificity of 99.94% for glomeromycotan sequences. We found evidence of significant shifts of the AMF communities caused by soil management and showed that tillage effects on different AMF taxa are clearly more complex than previously thought. The high resolving power of high-throughput sequencing highlights the need for quantitative measurements to efficiently detect these effects.
Complete Sequence and Analysis of Coconut Palm (Cocos nucifera) Mitochondrial Genome

PubMed Central

Zhao, Yuhui; Zeng, Jingyao; Alamer, Ali; Alanazi, Ibrahim O.; Alawad, Abdullah O.; Al-Sadi, Abdullah M.; Hu, Songnian; Yu, Jun

2016-01-01

Coconut (Cocos nucifera L.), a member of the palm family (Arecaceae), is one of the most economically important crops in tropics, serving as an important source of food, drink, fuel, medicine, and construction material. Here we report an assembly of the coconut (C. nucifera, Oman local Tall cultivar) mitochondrial (mt) genome based on next-generation sequencing data. This genome, 678,653bp in length and 45.5% in GC content, encodes 72 proteins, 9 pseudogenes, 23 tRNAs, and 3 ribosomal RNAs. Within the assembly, we find that the chloroplast (cp) derived regions account for 5.07% of the total assembly length, including 13 proteins, 2 pseudogenes, and 11 tRNAs. The mt genome has a relatively large fraction of repeat content (17.26%), including both forward (tandem) and inverted (palindromic) repeats. Sequence variation analysis shows that the Ti/Tv ratio of the mt genome is lower as compared to that of the nuclear genome and neutral expectation. By combining public RNA-Seq data for coconut, we identify 734 RNA editing sites supported by at least two datasets. In summary, our data provides the second complete mt genome sequence in the family Arecaceae, essential for further investigations on mitochondrial biology of seed plants. PMID:27736909
Transposable elements in TDP-43-mediated neurodegenerative disorders.

PubMed

Li, Wanhe; Jin, Ying; Prazak, Lisa; Hammell, Molly; Dubnau, Josh

2012-01-01

Elevated expression of specific transposable elements (TEs) has been observed in several neurodegenerative disorders. TEs also can be active during normal neurogenesis. By mining a series of deep sequencing datasets of protein-RNA interactions and of gene expression profiles, we uncovered extensive binding of TE transcripts to TDP-43, an RNA-binding protein central to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Second, we find that association between TDP-43 and many of its TE targets is reduced in FTLD patients. Third, we discovered that a large fraction of the TEs to which TDP-43 binds become de-repressed in mouse TDP-43 disease models. We propose the hypothesis that TE mis-regulation contributes to TDP-43 related neurodegenerative diseases.
A Novel Collection of snRNA-Like Promoters with Tissue-Specific Transcription Properties

PubMed Central

Garritano, Sonia; Gigoni, Arianna; Costa, Delfina; Malatesta, Paolo; Florio, Tullio; Cancedda, Ranieri; Pagano, Aldo

2012-01-01

We recently identified a novel dataset of snRNA-like trascriptional units in the human genome. The investigation of a subset of these elements showed that they play relevant roles in physiology and/or pathology. In this work we expand our collection of small RNAs taking advantage of a newly developed algorithm able to identify genome sequence stretches with RNA polymerase (pol) III type 3 promoter features thus constituting putative pol III binding sites. The bioinformatic analysis of a subset of these elements that map in introns of protein-coding genes in antisense configuration suggest their association with alternative splicing, similarly to other recently characterized small RNAs. Interestingly, the analysis of the transcriptional activity of these novel promoters shows that they are active in a cell-type specific manner, in accordance with the emerging body of evidence of a tissue/cell-specific activity of pol III. PMID:23109855
A novel collection of snRNA-like promoters with tissue-specific transcription properties.

PubMed

Garritano, Sonia; Gigoni, Arianna; Costa, Delfina; Malatesta, Paolo; Florio, Tullio; Cancedda, Ranieri; Pagano, Aldo

2012-01-01

We recently identified a novel dataset of snRNA-like trascriptional units in the human genome. The investigation of a subset of these elements showed that they play relevant roles in physiology and/or pathology. In this work we expand our collection of small RNAs taking advantage of a newly developed algorithm able to identify genome sequence stretches with RNA polymerase (pol) III type 3 promoter features thus constituting putative pol III binding sites. The bioinformatic analysis of a subset of these elements that map in introns of protein-coding genes in antisense configuration suggest their association with alternative splicing, similarly to other recently characterized small RNAs. Interestingly, the analysis of the transcriptional activity of these novel promoters shows that they are active in a cell-type specific manner, in accordance with the emerging body of evidence of a tissue/cell-specific activity of pol III.

The Discovery, Distribution, and Evolution of Viruses Associated with Drosophila melanogaster

PubMed Central

Webster, Claire L.; Waldron, Fergal M.; Robertson, Shaun; Crowson, Daisy; Ferrari, Giada; Quintana, Juan F.; Brouqui, Jean-Michel; Bayne, Elizabeth H.; Longdon, Ben; Buck, Amy H.; Lazzaro, Brian P.; Akorli, Jewelna; Haddrill, Penelope R.; Obbard, Darren J.

2015-01-01

Drosophila melanogaster is a valuable invertebrate model for viral infection and antiviral immunity, and is a focus for studies of insect-virus coevolution. Here we use a metagenomic approach to identify more than 20 previously undetected RNA viruses and a DNA virus associated with wild D. melanogaster. These viruses not only include distant relatives of known insect pathogens but also novel groups of insect-infecting viruses. By sequencing virus-derived small RNAs, we show that the viruses represent active infections of Drosophila. We find that the RNA viruses differ in the number and properties of their small RNAs, and we detect both siRNAs and a novel miRNA from the DNA virus. Analysis of small RNAs also allows us to identify putative viral sequences that lack detectable sequence similarity to known viruses. By surveying >2,000 individually collected wild adult Drosophila we show that more than 30% of D. melanogaster carry a detectable virus, and more than 6% carry multiple viruses. However, despite a high prevalence of the Wolbachia endosymbiont—which is known to be protective against virus infections in Drosophila—we were unable to detect any relationship between the presence of Wolbachia and the presence of any virus. Using publicly available RNA-seq datasets, we show that the community of viruses in Drosophila laboratories is very different from that seen in the wild, but that some of the newly discovered viruses are nevertheless widespread in laboratory lines and are ubiquitous in cell culture. By sequencing viruses from individual wild-collected flies we show that some viruses are shared between D. melanogaster and D. simulans. Our results provide an essential evolutionary and ecological context for host–virus interaction in Drosophila, and the newly reported viral sequences will help develop D. melanogaster further as a model for molecular and evolutionary virus research. PMID:26172158
Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison

PubMed Central

2013-01-01

Background Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons. Results We create a metric, called compression-based distance (CBD) for quantifying the degree of similarity between microbial communities. CBD uses the repetitive nature of hypervariable tag datasets and well-established compression algorithms to approximate the total information shared between two datasets. Three published microbiota datasets were used as test cases for CBD as an applicable tool. Our study revealed that CBD recaptured 100% of the statistically significant conclusions reported in the previous studies, while achieving a decrease in computational time required when compared to similar tools without expert user intervention. Conclusion CBD provides a simple, rapid, and accurate method for assessing distances between gastrointestinal tract microbiota 16S hypervariable tag datasets. PMID:23617892
Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison.

PubMed

Yang, Fang; Chia, Nicholas; White, Bryan A; Schook, Lawrence B

2013-04-23

Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons. We create a metric, called compression-based distance (CBD) for quantifying the degree of similarity between microbial communities. CBD uses the repetitive nature of hypervariable tag datasets and well-established compression algorithms to approximate the total information shared between two datasets. Three published microbiota datasets were used as test cases for CBD as an applicable tool. Our study revealed that CBD recaptured 100% of the statistically significant conclusions reported in the previous studies, while achieving a decrease in computational time required when compared to similar tools without expert user intervention. CBD provides a simple, rapid, and accurate method for assessing distances between gastrointestinal tract microbiota 16S hypervariable tag datasets.
Gene network inference by fusing data from diverse distributions

PubMed Central

Žitnik, Marinka; Zupan, Blaž

2015-01-01

Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies. Availability and implementation: Source code is at https://github.com/marinkaz/fusenet. Contact: blaz.zupan@fri.uni-lj.si Supplementary information: Supplementary information is available at Bioinformatics online. PMID:26072487
GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases.

PubMed

Zhu, Lihua Julie; Lawrence, Michael; Gupta, Ankit; Pagès, Hervé; Kucukural, Alper; Garber, Manuel; Wolfe, Scot A

2017-05-15

Genome editing technologies developed around the CRISPR-Cas9 nuclease system have facilitated the investigation of a broad range of biological questions. These nucleases also hold tremendous promise for treating a variety of genetic disorders. In the context of their therapeutic application, it is important to identify the spectrum of genomic sequences that are cleaved by a candidate nuclease when programmed with a particular guide RNA, as well as the cleavage efficiency of these sites. Powerful new experimental approaches, such as GUIDE-seq, facilitate the sensitive, unbiased genome-wide detection of nuclease cleavage sites within the genome. Flexible bioinformatics analysis tools for processing GUIDE-seq data are needed. Here, we describe an open source, open development software suite, GUIDEseq, for GUIDE-seq data analysis and annotation as a Bioconductor package in R. The GUIDEseq package provides a flexible platform with more than 60 adjustable parameters for the analysis of datasets associated with custom nuclease applications. These parameters allow data analysis to be tailored to different nuclease platforms with different length and complexity in their guide and PAM recognition sequences or their DNA cleavage position. They also enable users to customize sequence aggregation criteria, and vary peak calling thresholds that can influence the number of potential off-target sites recovered. GUIDEseq also annotates potential off-target sites that overlap with genes based on genome annotation information, as these may be the most important off-target sites for further characterization. In addition, GUIDEseq enables the comparison and visualization of off-target site overlap between different datasets for a rapid comparison of different nuclease configurations or experimental conditions. For each identified off-target, the GUIDEseq package outputs mapped GUIDE-Seq read count as well as cleavage score from a user specified off-target cleavage score prediction algorithm permitting the identification of genomic sequences with unexpected cleavage activity. The GUIDEseq package enables analysis of GUIDE-data from various nuclease platforms for any species with a defined genomic sequence. This software package has been used successfully to analyze several GUIDE-seq datasets. The software, source code and documentation are freely available at http://www.bioconductor.org/packages/release/bioc/html/GUIDEseq.html .
Identification of potential tumor-educated platelets RNA biomarkers in non-small-cell lung cancer by integrated bioinformatical analysis.

PubMed

Xue, Linlin; Xie, Li; Song, Xingguo; Song, Xianrang

2018-04-17

Platelets have emerged as key players in tumorigenesis and tumor progression. Tumor-educated platelet (TEP) RNA profile has the potential to diagnose non-small-cell lung cancer (NSCLC). The objective of this study was to identify potential TEP RNA biomarkers for the diagnosis of NSCLC and to explore the mechanisms in alternations of TEP RNA profile. The RNA-seq datasets GSE68086 and GSE89843 were downloaded from Gene Expression Omnibus DataSets (GEO DataSets). Then, the functional enrichment of the differentially expressed mRNAs was analyzed by the Database for Annotation Visualization and Integrated Discovery (DAVID). The miRNAs which regulated the differential mRNAs and the target mRNAs of miRNAs were identified by miRanda and miRDB. Then, the miRNA-mRNA regulatory network was visualized via Cytoscape software. Twenty consistently altered mRNAs (2 up-regulated and 18 down-regulated) were identified from the two GSE datasets, and they were significantly enriched in several biological processes, including transport and establishment of localization. Twenty identical miRNAs were found between exosomal miRNA-seq dataset and 229 miRNAs that regulated 20 consistently differential mRNAs in platelets. We also analyzed 13 spliceosomal mRNAs and their miRNA predictions; there were 27 common miRNAs between 206 differential exosomal miRNAs and 338 miRNAs that regulated 13 distinct spliceosomal mRNAs. This study identified 20 potential TEP RNA biomarkers in NSCLC for diagnosis by integrated bioinformatical analysis, and alternations in TEP RNA profile may be related to the post-transcriptional regulation and the splicing metabolisms of spliceosome. © 2018 Wiley Periodicals, Inc.
CLIP-seq analysis of multi-mapped reads discovers novel functional RNA regulatory sites in the human transcriptome.

PubMed

Zhang, Zijun; Xing, Yi

2017-09-19

Crosslinking or RNA immunoprecipitation followed by sequencing (CLIP-seq or RIP-seq) allows transcriptome-wide discovery of RNA regulatory sites. As CLIP-seq/RIP-seq reads are short, existing computational tools focus on uniquely mapped reads, while reads mapped to multiple loci are discarded. We present CLAM (CLIP-seq Analysis of Multi-mapped reads). CLAM uses an expectation-maximization algorithm to assign multi-mapped reads and calls peaks combining uniquely and multi-mapped reads. To demonstrate the utility of CLAM, we applied it to a wide range of public CLIP-seq/RIP-seq datasets involving numerous splicing factors, microRNAs and m6A RNA methylation. CLAM recovered a large number of novel RNA regulatory sites inaccessible by uniquely mapped reads. The functional significance of these sites was demonstrated by consensus motif patterns and association with alternative splicing (splicing factors), transcript abundance (AGO2) and mRNA half-life (m6A). CLAM provides a useful tool to discover novel protein-RNA interactions and RNA modification sites from CLIP-seq and RIP-seq data, and reveals the significant contribution of repetitive elements to the RNA regulatory landscape of the human transcriptome. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.
State-of-the-art on viral microRNAs in HPV infection and cancer development.

PubMed

Poltronieri, Palmiro; Sun, Binlian; Huang, Kai-Yao; Chang, Tzu-Hao; Lee, Tzong-Yi

2018-03-27

high-risk HPV subtypes are driving forces for human cancer development: HPV-16 and HPV-18 are responsible for most HPV-caused cancers. This review describes the present knowledge on HR-HPV genomes coding potential for viral miRNAs. HPV subtypes miRNA database, VIRmiRtar, has been constructed applying bioinformatics and a computational method, ViralMir, exploiting structural features, presence of hairpins, and validation by comparison with RNA sequencing datasets. Several miRNA candidates have been localised in the genomes of high-risk HPV subtypes. Among these, HPV-16 miR-1, miR-2 and miR-3. The database contains a list of host candidate gene targets that may be responsible for the oncogenesis in the various cellular environments. miRNA silencing therapies, based on specific cellular uptake of miRNA mimics and antagomiRs, directed towards HPV encoded miRNAs and/or microRNAs deregulated in the host cells, could be a valuable approach to support pharmaceutical interventions in the treatment of HPV dependent cancers. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
A Pipeline for High-Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics

PubMed Central

House, John S.; Grimm, Fabian A.; Jima, Dereje D.; Zhou, Yi-Hui; Rusyn, Ivan; Wright, Fred A.

2017-01-01

Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for in vitro toxicity screening. In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose–response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration–response points of departure. The methods are extensible to other forms of concentration–response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state. PMID:29163636
NCLscan: accurate identification of non-co-linear transcripts (fusion, trans-splicing and circular RNA) with a good balance between sensitivity and precision.

PubMed

Chuang, Trees-Juen; Wu, Chan-Shuo; Chen, Chia-Ying; Hung, Li-Yuan; Chiang, Tai-Wei; Yang, Min-Yu

2016-02-18

Analysis of RNA-seq data often detects numerous 'non-co-linear' (NCL) transcripts, which comprised sequence segments that are topologically inconsistent with their corresponding DNA sequences in the reference genome. However, detection of NCL transcripts involves two major challenges: removal of false positives arising from alignment artifacts and discrimination between different types of NCL transcripts (trans-spliced, circular or fusion transcripts). Here, we developed a new NCL-transcript-detecting method ('NCLscan'), which utilized a stepwise alignment strategy to almost completely eliminate false calls (>98% precision) without sacrificing true positives, enabling NCLscan outperform 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. With the high accuracy, NCLscan was applied to distinguishing between trans-spliced, circular and fusion transcripts on the basis of poly(A)- and nonpoly(A)-selected RNA-seq data. We showed that circular RNAs were expressed more ubiquitously, more abundantly and less cell type-specifically than trans-spliced and fusion transcripts. Our study thus describes a robust pipeline for the discovery of NCL transcripts, and sheds light on the fundamental biology of these non-canonical RNA events in human transcriptome. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Virtual Northern Analysis of the Human Genome

PubMed Central

Hurowitz, Evan H.; Drori, Iddo; Stodden, Victoria C.; Donoho, David L.; Brown, Patrick O.

2007-01-01

Background We applied the Virtual Northern technique to human brain mRNA to systematically measure human mRNA transcript lengths on a genome-wide scale. Methodology/Principal Findings We used separation by gel electrophoresis followed by hybridization to cDNA microarrays to measure 8,774 mRNA transcript lengths representing at least 6,238 genes at high (>90%) confidence. By comparing these transcript lengths to the Refseq and H-Invitational full-length cDNA databases, we found that nearly half of our measurements appeared to represent novel transcript variants. Comparison of length measurements determined by hybridization to different cDNAs derived from the same gene identified clones that potentially correspond to alternative transcript variants. We observed a close linear relationship between ORF and mRNA lengths in human mRNAs, identical in form to the relationship we had previously identified in yeast. Some functional classes of protein are encoded by mRNAs whose untranslated regions (UTRs) tend to be longer or shorter than average; these functional classes were similar in both human and yeast. Conclusions/Significance Human transcript diversity is extensive and largely unannotated. Our length dataset can be used as a new criterion for judging the completeness of cDNAs and annotating mRNA sequences. Similar relationships between the lengths of the UTRs in human and yeast mRNAs and the functions of the proteins they encode suggest that UTR sequences serve an important regulatory role among eukaryotes. PMID:17520019
De Novo Transcriptome Sequence Assembly from Coconut Leaves and Seeds with a Focus on Factors Involved in RNA-Directed DNA Methylation

PubMed Central

Huang, Ya-Yi; Lee, Chueh-Pai; Fu, Jason L.; Chang, Bill Chia-Han; Matzke, Antonius J. M.; Matzke, Marjori

2014-01-01

Coconut palm (Cocos nucifera) is a symbol of the tropics and a source of numerous edible and nonedible products of economic value. Despite its nutritional and industrial significance, coconut remains under-represented in public repositories for genomic and transcriptomic data. We report de novo transcript assembly from RNA-seq data and analysis of gene expression in seed tissues (embryo and endosperm) and leaves of a dwarf coconut variety. Assembly of 10 GB sequencing data for each tissue resulted in 58,211 total unigenes in embryo, 61,152 in endosperm, and 33,446 in leaf. Within each unigene pool, 24,857 could be annotated in embryo, 29,731 could be annotated in endosperm, and 26,064 could be annotated in leaf. A KEGG analysis identified 138, 138, and 139 pathways, respectively, in transcriptomes of embryo, endosperm, and leaf tissues. Given the extraordinarily large size of coconut seeds and the importance of small RNA-mediated epigenetic regulation during seed development in model plants, we used homology searches to identify putative homologs of factors required for RNA-directed DNA methylation in coconut. The findings suggest that RNA-directed DNA methylation is important during coconut seed development, particularly in maturing endosperm. This dataset will expand the genomics resources available for coconut and provide a foundation for more detailed analyses that may assist molecular breeding strategies aimed at improving this major tropical crop. PMID:25193496
multiDE: a dimension reduced model based statistical method for differential expression analysis using RNA-sequencing data with multiple treatment conditions.

PubMed

Kang, Guangliang; Du, Li; Zhang, Hong

2016-06-22

The growing complexity of biological experiment design based on high-throughput RNA sequencing (RNA-seq) is calling for more accommodative statistical tools. We focus on differential expression (DE) analysis using RNA-seq data in the presence of multiple treatment conditions. We propose a novel method, multiDE, for facilitating DE analysis using RNA-seq read count data with multiple treatment conditions. The read count is assumed to follow a log-linear model incorporating two factors (i.e., condition and gene), where an interaction term is used to quantify the association between gene and condition. The number of the degrees of freedom is reduced to one through the first order decomposition of the interaction, leading to a dramatically power improvement in testing DE genes when the number of conditions is greater than two. In our simulation situations, multiDE outperformed the benchmark methods (i.e. edgeR and DESeq2) even if the underlying model was severely misspecified, and the power gain was increasing in the number of conditions. In the application to two real datasets, multiDE identified more biologically meaningful DE genes than the benchmark methods. An R package implementing multiDE is available publicly at http://homepage.fudan.edu.cn/zhangh/softwares/multiDE . When the number of conditions is two, multiDE performs comparably with the benchmark methods. When the number of conditions is greater than two, multiDE outperforms the benchmark methods.
De novo transcriptome sequence assembly from coconut leaves and seeds with a focus on factors involved in RNA-directed DNA methylation.

PubMed

Huang, Ya-Yi; Lee, Chueh-Pai; Fu, Jason L; Chang, Bill Chia-Han; Matzke, Antonius J M; Matzke, Marjori

2014-09-04

Coconut palm (Cocos nucifera) is a symbol of the tropics and a source of numerous edible and nonedible products of economic value. Despite its nutritional and industrial significance, coconut remains under-represented in public repositories for genomic and transcriptomic data. We report de novo transcript assembly from RNA-seq data and analysis of gene expression in seed tissues (embryo and endosperm) and leaves of a dwarf coconut variety. Assembly of 10 GB sequencing data for each tissue resulted in 58,211 total unigenes in embryo, 61,152 in endosperm, and 33,446 in leaf. Within each unigene pool, 24,857 could be annotated in embryo, 29,731 could be annotated in endosperm, and 26,064 could be annotated in leaf. A KEGG analysis identified 138, 138, and 139 pathways, respectively, in transcriptomes of embryo, endosperm, and leaf tissues. Given the extraordinarily large size of coconut seeds and the importance of small RNA-mediated epigenetic regulation during seed development in model plants, we used homology searches to identify putative homologs of factors required for RNA-directed DNA methylation in coconut. The findings suggest that RNA-directed DNA methylation is important during coconut seed development, particularly in maturing endosperm. This dataset will expand the genomics resources available for coconut and provide a foundation for more detailed analyses that may assist molecular breeding strategies aimed at improving this major tropical crop. Copyright © 2014 Huang et al.
An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer.

PubMed

Ruggles, Kelly V; Tang, Zuojian; Wang, Xuya; Grover, Himanshu; Askenazi, Manor; Teubl, Jennifer; Cao, Song; McLellan, Michael D; Clauser, Karl R; Tabb, David L; Mertins, Philipp; Slebos, Robbert; Erdmann-Gilmore, Petra; Li, Shunqiang; Gunawardena, Harsha P; Xie, Ling; Liu, Tao; Zhou, Jian-Ying; Sun, Shisheng; Hoadley, Katherine A; Perou, Charles M; Chen, Xian; Davies, Sherri R; Maher, Christopher A; Kinsinger, Christopher R; Rodland, Karen D; Zhang, Hui; Zhang, Zhen; Ding, Li; Townsend, R Reid; Rodriguez, Henry; Chan, Daniel; Smith, Richard D; Liebler, Daniel C; Carr, Steven A; Payne, Samuel; Ellis, Matthew J; Fenyő, David

2016-03-01

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.
In-depth characterization of breast cancer tumor-promoting cell transcriptome by RNA sequencing and microarrays

PubMed Central

Soldà, Giulia; Merlino, Giuseppe; Fina, Emanuela; Brini, Elena; Moles, Anna; Cappelletti, Vera; Daidone, Maria Grazia

2016-01-01

Numerous studies have reported the existence of tumor-promoting cells (TPC) with self-renewal potential and a relevant role in drug resistance. However, pathways and modifications involved in the maintenance of such tumor subpopulations are still only partially understood. Sequencing-based approaches offer the opportunity for a detailed study of TPC including their transcriptome modulation. Using microarrays and RNA sequencing approaches, we compared the transcriptional profiles of parental MCF7 breast cancer cells with MCF7-derived TPC (i.e. MCFS). Data were explored using different bioinformatic approaches, and major findings were experimentally validated. The different analytical pipelines (Lifescope and Cufflinks based) yielded similar although not identical results. RNA sequencing data partially overlapped microarray results and displayed a higher dynamic range, although overall the two approaches concordantly predicted pathway modifications. Several biological functions were altered in TPC, ranging from production of inflammatory cytokines (i.e., IL-8 and MCP-1) to proliferation and response to steroid hormones. More than 300 non-coding RNAs were defined as differentially expressed, and 2,471 potential splicing events were identified. A consensus signature of genes up-regulated in TPC was derived and was found to be significantly associated with insensitivity to fulvestrant in a public breast cancer patient dataset. Overall, we obtained a detailed portrait of the transcriptome of a breast cancer TPC line, highlighted the role of non-coding RNAs and differential splicing, and identified a gene signature with a potential as a context-specific biomarker in patients receiving endocrine treatment. PMID:26556871
Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

PubMed Central

Kang, Eun Yong; Martin, Lisa J.; Mangul, Serghei; Isvilanonda, Warin; Zou, Jennifer; Ben-David, Eyal; Han, Buhm; Lusis, Aldons J.; Shifman, Sagiv; Eskin, Eleazar

2016-01-01

The study of the genetics of gene expression is of considerable importance to understanding the nature of common, complex diseases. The most widely applied approach to identifying relationships between genetic variation and gene expression is the expression quantitative trait loci (eQTL) approach. Here, we increased the computational power of eQTL with an alternative and complementary approach based on analyzing allele specific expression (ASE). We designed a novel analytical method to identify cis-acting regulatory variants based on genome sequencing and measurements of ASE from RNA-sequencing (RNA-seq) data. We evaluated the power and resolution of our method using simulated data. We then applied the method to map regulatory variants affecting gene expression in lymphoblastoid cell lines (LCLs) from 77 unrelated northern and western European individuals (CEU), which were part of the HapMap project. A total of 2309 SNPs were identified as being associated with ASE patterns. The SNPs associated with ASE were enriched within promoter regions and were significantly more likely to signal strong evidence for a regulatory role. Finally, among the candidate regulatory SNPs, we identified 108 SNPs that were previously associated with human immune diseases. With further improvements in quantifying ASE from RNA-seq, the application of our method to other datasets is expected to accelerate our understanding of the biological basis of common diseases. PMID:27765809
HNRNPLL stabilizes mRNAs for DNA replication proteins and promotes cell cycle progression in colorectal cancer cells.

PubMed

Sakuma, Keiichiro; Sasaki, Eiichi; Kimura, Kenya; Komori, Koji; Shimizu, Yasuhiro; Yatabe, Yasushi; Aoki, Masahiro

2018-06-05

HNRNPLL (heterogeneous nuclear ribonucleoprotein L-like), an RNA-binding protein that regulates alternative splicing of pre-mRNAs, has been shown to regulate differentiation of lymphocytes, as well as metastasis of colorectal cancer cells. Here we show that HNRNPLL promotes cell cycle progression and hence proliferation of colorectal cancer cells. Functional annotation analysis of those genes whose expression levels were changed by three-fold or more in RNA sequencing analysis between SW480 cells overexpressing HNRNPLL and those knocked down for HNRNPLL revealed enrichment of DNA replication-related genes by HNRNPLL overexpression. Among 13 genes detected in the DNA replication pathway, PCNA, RFC3, and FEN1 showed reproducible upregulation by HNRNPLL overexpression both at mRNA and protein levels in SW480 and HT29 cells. Importantly, knockdown of any of these genes alone suppressed the proliferation promoting effect induced by HNRNPLL overexpression. RNA-immunoprecipitation assay presented a binding of FLAG-tagged HNRNPLL to mRNA of these genes, and HNRNPLL overexpression significantly suppressed the downregulation of these genes during 12 hours of actinomycin D treatment, suggesting a role of HNRNPLL in mRNA stability. Finally, analysis of a public RNA sequencing dataset of clinical samples suggested a link between overexpression of HNRNPLL and that of PCNA, RFC3, and FEN1. This link was further supported by immunohistochemistry of colorectal cancer clinical samples, whereas expression of CDKN1A, which is known to inhibit the cooperative function of PCNA, RFC3, and FEN1, was negatively associated with HNRNPLL expression. These results indicate that HNRNPLL stabilizes mRNAs encoding regulators of DNA replication and promotes colorectal cancer cell proliferation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Active fungi amidst a marine subsurface RNA paleome

NASA Astrophysics Data System (ADS)

Orsi, W.; Biddle, J.; Edgcomb, V.

2012-12-01

The deep marine subsurface is a vast habitat for microbial life where cells may live on geologic timescales. Since extracellular DNA in sediments may be preserved on long timescales, ribosomal RNA (rRNA) is suggested to be a proxy for the active fraction of a microbial community in the subsurface. During an investigation of eukaryotic 18S rRNA signatures by amplicon pyrosequencing, metazoan, plant, and diatom rRNA signatures were recovered from marine sediments up to 2.7 million years old, suggesting that rRNA may be much more stable than previously considered in the marine subsurface. This finding confirms the concept of a paleome, extending it to include rRNA. Within the same dataset, unique profiles of fungi were found across a range of marine subsurface provinces exhibiting statistically significant correlations with total organic carbon (TOC), sulfide, and dissolved inorganic carbon (DIC). Sequences from metazoans, plants and diatoms showed different correlation patterns, consistent with a depth-controlled paleome. The fungal correlations with geochemistry allow the inference that some fungi are active and adapted for survival in the marine subsurface. A metatranscriptomic analysis of fungal derived mRNA confirms that fungi are metabolically active and utilize a range of organic and inorganic substrates in the marine subsurface.

Regulatory RNA binding proteins contribute to the transcriptome-wide splicing alterations in human cellular senescence.

PubMed

Dong, Qiongye; Wei, Lei; Zhang, Michael Q; Wang, Xiaowo

2018-06-24

Dysregulation of mRNA splicing has been observed in certain cellular senescence process. However, the common splicing alterations on the whole transcriptome shared by various types of senescence are poorly understood. In order to systematically identify senescence-associated transcriptomic changes in genome-wide scale, we collected RNA sequencing datasets of different human cell types with a variety of senescence-inducing methods from public databases and performed meta-analysis. First, we discovered that a group of RNA binding proteins were consistently down-regulated in diverse senescent samples and identified 406 senescence-associated common differential splicing events. Then, eight differentially expressed RNA binding proteins were predicted to regulate these senescence-associated splicing alterations through an enrichment analysis of their RNA binding information, including motif scanning and enhanced cross-linking immunoprecipitation data. In addition, we constructed the splicing regulatory modules that might contribute to senescence-associated biological processes. Finally, it was confirmed that knockdown of the predicted senescence-associated potential splicing regulators through shRNAs in HepG2 cell line could result in senescence-like splicing changes. Taken together, our work demonstrated a broad range of common changes in mRNA splicing switches and detected their central regulatory RNA binding proteins during senescence. These findings would help to better understand the coordinating splicing alterations in cellular senescence.
FGFR3, as a receptor tyrosine kinase, is associated with differentiated biological functions and improved survival of glioma patients.

PubMed

Wang, Zheng; Zhang, Chuanbao; Sun, Lihua; Liang, Jingshan; Liu, Xing; Li, Guanzhang; Yao, Kun; Zhang, Wei; Jiang, Tao

2016-12-20

Activation of receptor tyrosine kinases is common in Malignancies. FGFR3 fusion with TACC3 has been reported to have transforming effects in primary glioblastoma and display oncogenic activity in vitro and in vivo. We set out to investigate the role of FGFR3 in glioma through transcriptomic analysis. FGFR3 increased in Classical subtype and Neural subtype consistently in CGGA and TCGA cohort. Similar patterns of FGFR3 distribution through subtypes were observed in CGGA and TCGA samples. Gene ontology analysis was performed with genes that were significantly correlated with FGFR3 expression. We found that positively associated biological processes of FGFR3 were focused on differentiated cellular functions and neuronal activities, while negatively correlated biological processes focused on mitosis and cell cycle phase. Clinical investigation showed that higher FGFR3 expression predicted improved survival for glioma patients, especially in Proneural subtype. Moreover, FGFR3 showed very limited relevance with other receptor tyrosine kinases in glioma at transcriptome level. FGFR3 expression data of glioma was obtained from Chinese Glioma Genome Atlas (CGGA) and TCGA (The Cancer Genome Atlas). In total, RNA sequencing data of 325 glioma samples and mRNA microarray data of 301 samples from CGGA dataset were enrolled into this study. To consolidate the findings that we have revealed in CGGA dataset, RNA-seq data of 672 glioma samples from TCGA dataset were used as a validation cohort. R language was used as the main tool to perform statistical analysis and graphical work. FGFR3 expression increased in classical and neural subtypes and was associated with differentiated cellular functions. FGFR3 showed very limited correlation with other common receptor tyrosine kinases, and predicted improved survival for glioma patients.
FGFR3, as a receptor tyrosine kinase, is associated with differentiated biological functions and improved survival of glioma patients

PubMed Central

Wang, Zheng; Zhang, Chuanbao; Sun, Lihua; Liang, Jingshan; Liu, Xing; Li, Guanzhang; Yao, Kun; Zhang, Wei; Jiang, Tao

2016-01-01

Background Activation of receptor tyrosine kinases is common in Malignancies. FGFR3 fusion with TACC3 has been reported to have transforming effects in primary glioblastoma and display oncogenic activity in vitro and in vivo. We set out to investigate the role of FGFR3 in glioma through transcriptomic analysis. Results FGFR3 increased in Classical subtype and Neural subtype consistently in CGGA and TCGA cohort. Similar patterns of FGFR3 distribution through subtypes were observed in CGGA and TCGA samples. Gene ontology analysis was performed with genes that were significantly correlated with FGFR3 expression. We found that positively associated biological processes of FGFR3 were focused on differentiated cellular functions and neuronal activities, while negatively correlated biological processes focused on mitosis and cell cycle phase. Clinical investigation showed that higher FGFR3 expression predicted improved survival for glioma patients, especially in Proneural subtype. Moreover, FGFR3 showed very limited relevance with other receptor tyrosine kinases in glioma at transcriptome level. Materials and Methods FGFR3 expression data of glioma was obtained from Chinese Glioma Genome Atlas (CGGA) and TCGA (The Cancer Genome Atlas). In total, RNA sequencing data of 325 glioma samples and mRNA microarray data of 301 samples from CGGA dataset were enrolled into this study. To consolidate the findings that we have revealed in CGGA dataset, RNA-seq data of 672 glioma samples from TCGA dataset were used as a validation cohort. R language was used as the main tool to perform statistical analysis and graphical work. Conclusions FGFR3 expression increased in classical and neural subtypes and was associated with differentiated cellular functions. FGFR3 showed very limited correlation with other common receptor tyrosine kinases, and predicted improved survival for glioma patients. PMID:27829236
Analysis of energy-based algorithms for RNA secondary structure prediction

PubMed Central

2012-01-01

Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA) or pseudo-expected accuracy (pseudo-MEA) methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-)MEA-based methods, with respect to the latest datasets and energy parameters. Results We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-)MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence) of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms. Second, on our large datasets, the algorithm with best overall accuracy is a pseudo MEA-based algorithm of Hamada et al. that uses a generalized centroid estimator of base pairs. However, between MFE and other MEA-based methods, there is no clear winner in the sense that the relative accuracy of the MFE versus MEA-based algorithms changes depending on the underlying energy parameters. Third, of the four parameter sets we considered, the best accuracy for the MFE-, MEA-based, and pseudo-MEA-based methods is 0.686, 0.680, and 0.711, respectively (on a scale from 0 to 1 with 1 meaning perfect structure predictions) and is obtained with a thermodynamic parameter set obtained by Andronescu et al. called BL* (named after the Boltzmann likelihood method by which the parameters were derived). Conclusions Large datasets should be used to obtain reliable measures of the accuracy of RNA structure prediction algorithms, and average accuracies on specific classes (such as Group I introns and Transfer RNAs) should be interpreted with caution, considering the relatively small size of currently available datasets for such classes. The accuracy of the MEA-based methods is significantly higher when using the BL* parameter set of Andronescu et al. than when using the parameters of Mathews and Turner, and there is no significant difference between the accuracy of MEA-based methods and MFE when using the BL* parameters. The pseudo-MEA-based method of Hamada et al. with the BL* parameter set significantly outperforms all other MFE and MEA-based algorithms on our large data sets. PMID:22296803
Analysis of energy-based algorithms for RNA secondary structure prediction.

PubMed

Hajiaghayi, Monir; Condon, Anne; Hoos, Holger H

2012-02-01

RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA) or pseudo-expected accuracy (pseudo-MEA) methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-)MEA-based methods, with respect to the latest datasets and energy parameters. We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-)MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence) of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms. Second, on our large datasets, the algorithm with best overall accuracy is a pseudo MEA-based algorithm of Hamada et al. that uses a generalized centroid estimator of base pairs. However, between MFE and other MEA-based methods, there is no clear winner in the sense that the relative accuracy of the MFE versus MEA-based algorithms changes depending on the underlying energy parameters. Third, of the four parameter sets we considered, the best accuracy for the MFE-, MEA-based, and pseudo-MEA-based methods is 0.686, 0.680, and 0.711, respectively (on a scale from 0 to 1 with 1 meaning perfect structure predictions) and is obtained with a thermodynamic parameter set obtained by Andronescu et al. called BL* (named after the Boltzmann likelihood method by which the parameters were derived). Large datasets should be used to obtain reliable measures of the accuracy of RNA structure prediction algorithms, and average accuracies on specific classes (such as Group I introns and Transfer RNAs) should be interpreted with caution, considering the relatively small size of currently available datasets for such classes. The accuracy of the MEA-based methods is significantly higher when using the BL* parameter set of Andronescu et al. than when using the parameters of Mathews and Turner, and there is no significant difference between the accuracy of MEA-based methods and MFE when using the BL* parameters. The pseudo-MEA-based method of Hamada et al. with the BL* parameter set significantly outperforms all other MFE and MEA-based algorithms on our large data sets.
The VirusBanker database uses a Java program to allow flexible searching through Bunyaviridae sequences

PubMed Central

Fourment, Mathieu; Gibbs, Mark J

2008-01-01

Background Viruses of the Bunyaviridae have segmented negative-stranded RNA genomes and several of them cause significant disease. Many partial sequences have been obtained from the segments so that GenBank searches give complex results. Sequence databases usually use HTML pages to mediate remote sorting, but this approach can be limiting and may discourage a user from exploring a database. Results The VirusBanker database contains Bunyaviridae sequences and alignments and is presented as two spreadsheets generated by a Java program that interacts with a MySQL database on a server. Sequences are displayed in rows and may be sorted using information that is displayed in columns and includes data relating to the segment, gene, protein, species, strain, sequence length, terminal sequence and date and country of isolation. Bunyaviridae sequences and alignments may be downloaded from the second spreadsheet with titles defined by the user from the columns, or viewed when passed directly to the sequence editor, Jalview. Conclusion VirusBanker allows large datasets of aligned nucleotide and protein sequences from the Bunyaviridae to be compiled and winnowed rapidly using criteria that are formulated heuristically. PMID:18251994
Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

PubMed Central

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/ PMID:26363020
Biclustering as a method for RNA local multiple sequence alignment.

PubMed

Wang, Shu; Gutell, Robin R; Miranker, Daniel P

2007-12-15

Biclustering is a clustering method that simultaneously clusters both the domain and range of a relation. A challenge in multiple sequence alignment (MSA) is that the alignment of sequences is often intended to reveal groups of conserved functional subsequences. Simultaneously, the grouping of the sequences can impact the alignment; precisely the kind of dual situation biclustering is intended to address. We define a representation of the MSA problem enabling the application of biclustering algorithms. We develop a computer program for local MSA, BlockMSA, that combines biclustering with divide-and-conquer. BlockMSA simultaneously finds groups of similar sequences and locally aligns subsequences within them. Further alignment is accomplished by dividing both the set of sequences and their contents. The net result is both a multiple sequence alignment and a hierarchical clustering of the sequences. BlockMSA was tested on the subsets of the BRAliBase 2.1 benchmark suite that display high variability and on an extension to that suite to larger problem sizes. Also, alignments were evaluated of two large datasets of current biological interest, T box sequences and Group IC1 Introns. The results were compared with alignments computed by ClustalW, MAFFT, MUCLE and PROBCONS alignment programs using Sum of Pairs (SPS) and Consensus Count. Results for the benchmark suite are sensitive to problem size. On problems of 15 or greater sequences, BlockMSA is consistently the best. On none of the problems in the test suite are there appreciable differences in scores among BlockMSA, MAFFT and PROBCONS. On the T box sequences, BlockMSA does the most faithful job of reproducing known annotations. MAFFT and PROBCONS do not. On the Intron sequences, BlockMSA, MAFFT and MUSCLE are comparable at identifying conserved regions. BlockMSA is implemented in Java. Source code and supplementary datasets are available at http://aug.csres.utexas.edu/msa/
Identification and correction of systematic error in high-throughput sequence data

PubMed Central

2011-01-01

Background A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. Results We characterize and describe systematic errors using overlapping paired reads from high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that they are highly replicable across experiments. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq), and can be used with single-end datasets. Conclusions Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments. PMID:22099972
Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

PubMed

Marques, Yuri Bento; de Paiva Oliveira, Alcione; Ribeiro Vasconcelos, Ana Tereza; Cerqueira, Fabio Ribeiro

2016-12-15

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.
Deep RNA-Seq to unlock the gene bank of floral development in Sinapis arvensis.

PubMed

Liu, Jia; Mei, Desheng; Li, Yunchang; Huang, Shunmou; Hu, Qiong

2014-01-01

Sinapis arvensis is a weed with strong biological activity. Despite being a problematic annual weed that contaminates agricultural crop yield, it is a valuable alien germplasm resource. It can be utilized for broadening the genetic background of Brassica crops with desirable agricultural traits like resistance to blackleg (Leptosphaeria maculans), stem rot (Sclerotinia sclerotium) and pod shatter (caused by FRUITFULL gene). However, few genetic studies of S. arvensis were reported because of the lack of genomic resources. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive dataset for S. arvensis for the first time. We used Illumina paired-end sequencing technology to sequence the S. arvensis flower transcriptome and generated 40,981,443 reads that were assembled into 131,278 transcripts. We de novo assembled 96,562 high quality unigenes with an average length of 832 bp. A total of 33,662 full-length ORF complete sequences were identified, and 41,415 unigenes were mapped onto 128 pathways using the KEGG Pathway database. The annotated unigenes were compared against Brassica rapa, B. oleracea, B. napus and Arabidopsis thaliana. Among these unigenes, 76,324 were identified as putative homologs of annotated sequences in the public protein databases, of which 1194 were associated with plant hormone signal transduction and 113 were related to gibberellin homeostasis/signaling. Unigenes that did not match any of those sequence datasets were considered to be unique to S. arvensis. Furthermore, 21,321 simple sequence repeats were found. Our study will enhance the currently available resources for Brassicaceae and will provide a platform for future genomic studies for genetic improvement of Brassica crops.
Deep RNA-Seq to Unlock the Gene Bank of Floral Development in Sinapis arvensis

PubMed Central

Liu, Jia; Mei, Desheng; Li, Yunchang; Huang, Shunmou; Hu, Qiong

2014-01-01

Sinapis arvensis is a weed with strong biological activity. Despite being a problematic annual weed that contaminates agricultural crop yield, it is a valuable alien germplasm resource. It can be utilized for broadening the genetic background of Brassica crops with desirable agricultural traits like resistance to blackleg (Leptosphaeria maculans), stem rot (Sclerotinia sclerotium) and pod shatter (caused by FRUITFULL gene). However, few genetic studies of S. arvensis were reported because of the lack of genomic resources. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive dataset for S. arvensis for the first time. We used Illumina paired-end sequencing technology to sequence the S. arvensis flower transcriptome and generated 40,981,443 reads that were assembled into 131,278 transcripts. We de novo assembled 96,562 high quality unigenes with an average length of 832 bp. A total of 33,662 full-length ORF complete sequences were identified, and 41,415 unigenes were mapped onto 128 pathways using the KEGG Pathway database. The annotated unigenes were compared against Brassica rapa, B. oleracea, B. napus and Arabidopsis thaliana. Among these unigenes, 76,324 were identified as putative homologs of annotated sequences in the public protein databases, of which 1194 were associated with plant hormone signal transduction and 113 were related to gibberellin homeostasis/signaling. Unigenes that did not match any of those sequence datasets were considered to be unique to S. arvensis. Furthermore, 21,321 simple sequence repeats were found. Our study will enhance the currently available resources for Brassicaceae and will provide a platform for future genomic studies for genetic improvement of Brassica crops. PMID:25192023
MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark.

PubMed

Qin, Li-Xuan; Zhou, Qin

2014-01-01

MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.
MicroRNA Array Normalization: An Evaluation Using a Randomized Dataset as the Benchmark

PubMed Central

Qin, Li-Xuan; Zhou, Qin

2014-01-01

MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays. PMID:24905456
Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons

PubMed Central

Fischer, Sandra; Kumar, Neeraj

2017-01-01

The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea. PMID:28097056
Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons.

PubMed

Lagkouvardos, Ilias; Fischer, Sandra; Kumar, Neeraj; Clavel, Thomas

2017-01-01

The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha - and beta -diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea.
Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.
Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing.

PubMed

Renthal, William

2018-01-01

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.
A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.

PubMed

Wolff, Alexander; Bayerlová, Michaela; Gaedcke, Jochen; Kube, Dieter; Beißbarth, Tim

2018-01-01

Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.
Prospects and challenges for fungal metatranscriptomes of complex communities

DOE PAGES

Kuske, Cheryl Rae; Hesse, Cedar Nelson; Challacombe, Jean Faust; ...

2015-01-22

We report that the ability to extract and purify messenger RNA directly from plants, decomposing organic matter and soil, followed by high-throughput sequencing of the pool of expressed genes, has spawned the emerging research area of metatranscriptomics. Each metatranscriptome provides a snapshot of the composition and relative abundance of actively transcribed genes, and thus provides an assessment of the interactions between soil microorganisms and plants, and collective microbial metabolic processes in many environments. We highlight current approaches for analysis of fungal transcriptome and metatranscriptome datasets across a gradient of community complexity, and note benefits and pitfalls associated with those approaches.more » Finally, we discuss knowledge gaps that limit our current ability to interpret metatranscriptome datasets and suggest future research directions that will require concerted efforts within the scientific community.« less

Extraction of Molecular Features through Exome to Transcriptome Alignment

PubMed Central

Mudvari, Prakriti; Kowsari, Kamran; Cole, Charles; Mazumder, Raja; Horvath, Anelia

2014-01-01

Integrative Next Generation Sequencing (NGS) DNA and RNA analyses have very recently become feasible, and the published to date studies have discovered critical disease implicated pathways, and diagnostic and therapeutic targets. A growing number of exomes, genomes and transcriptomes from the same individual are quickly accumulating, providing unique venues for mechanistic and regulatory features analysis, and, at the same time, requiring new exploration strategies. In this study, we have integrated variation and expression information of four NGS datasets from the same individual: normal and tumor breast exomes and transcriptomes. Focusing on SNPcentered variant allelic prevalence, we illustrate analytical algorithms that can be applied to extract or validate potential regulatory elements, such as expression or growth advantage, imprinting, loss of heterozygosity (LOH), somatic changes, and RNA editing. In addition, we point to some critical elements that might bias the output and recommend alternative measures to maximize the confidence of findings. The need for such strategies is especially recognized within the growing appreciation of the concept of systems biology: integrative exploration of genome and transcriptome features reveal mechanistic and regulatory insights that reach far beyond linear addition of the individual datasets. PMID:24791251
An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets.

PubMed

Hill, Katherine E; Kelly, Andrew D; Kuijjer, Marieke L; Barry, William; Rattani, Ahmed; Garbutt, Cassandra C; Kissick, Haydn; Janeway, Katherine; Perez-Atayde, Antonio; Goldsmith, Jeffrey; Gebhardt, Mark C; Arredouani, Mohamed S; Cote, Greg; Hornicek, Francis; Choy, Edwin; Duan, Zhenfeng; Quackenbush, John; Haibe-Kains, Benjamin; Spentzos, Dimitrios

2017-05-15

A microRNA (miRNA) collection on the imprinted 14q32 MEG3 region has been associated with outcome in osteosarcoma. We assessed the clinical utility of this miRNA set and their association with methylation status. We integrated coding and non-coding RNA data from three independent annotated clinical osteosarcoma cohorts (n = 65, n = 27, and n = 25) and miRNA and methylation data from one in vitro (19 cell lines) and one clinical (NCI Therapeutically Applicable Research to Generate Effective Treatments (TARGET) osteosarcoma dataset, n = 80) dataset. We used time-dependent receiver operating characteristic (tdROC) analysis to evaluate the clinical value of candidate miRNA profiles and machine learning approaches to compare the coding and non-coding transcriptional programs of high- and low-risk osteosarcoma tumors and high- versus low-aggressiveness cell lines. In the cell line and TARGET datasets, we also studied the methylation patterns of the MEG3 imprinting control region on 14q32 and their association with miRNA expression and tumor aggressiveness. In the tdROC analysis, miRNA sets on 14q32 showed strong discriminatory power for recurrence and survival in the three clinical datasets. High- or low-risk tumor classification was robust to using different microRNA sets or classification methods. Machine learning approaches showed that genome-wide miRNA profiles and miRNA regulatory networks were quite different between the two outcome groups and mRNA profiles categorized the samples in a manner concordant with the miRNAs, suggesting potential molecular subtypes. Further, miRNA expression patterns were reproducible in comparing high-aggressiveness versus low-aggressiveness cell lines. Methylation patterns in the MEG3 differentially methylated region (DMR) also distinguished high-aggressiveness from low-aggressiveness cell lines and were associated with expression of several 14q32 miRNAs in both the cell lines and the large TARGET clinical dataset. Within the limits of available CpG array coverage, we observed a potential methylation-sensitive regulation of the non-coding RNA cluster by CTCF, a known enhancer-blocking factor. Loss of imprinting/methylation changes in the 14q32 non-coding region defines reproducible previously unrecognized osteosarcoma subtypes with distinct transcriptional programs and biologic and clinical behavior. Future studies will define the precise relationship between 14q32 imprinting, non-coding RNA expression, genomic enhancer binding, and tumor aggressiveness, with possible therapeutic implications for both early- and advanced-stage patients.
Deciphering Transcriptional Programming during Pod and Seed Development Using RNA-Seq in Pigeonpea (Cajanus cajan).

PubMed

Pazhamala, Lekha T; Agarwal, Gaurav; Bajaj, Prasad; Kumar, Vinay; Kulshreshtha, Akanksha; Saxena, Rachit K; Varshney, Rajeev K

2016-01-01

Seed development is an important event in plant life cycle that has interested humankind since ages, especially in crops of economic importance. Pigeonpea is an important grain legume of the semi-arid tropics, used mainly for its protein rich seeds. In order to understand the transcriptional programming during the pod and seed development, RNA-seq data was generated from embryo sac from the day of anthesis (0 DAA), seed and pod wall (5, 10, 20 and 30 DAA) of pigeonpea variety "Asha" (ICPL 87119) using Illumina HiSeq 2500. About 684 million sequencing reads have been generated from nine samples, which resulted in the identification of 27,441 expressed genes after sequence analysis. These genes have been studied for their differentially expression, co-expression, temporal and spatial gene expression. We have also used the RNA-seq data to identify important seed-specific transcription factors, biological processes and associated pathways during seed development process in pigeonpea. The comprehensive gene expression study from flowering to mature pod development in pigeonpea would be crucial in identifying candidate genes involved in seed traits directly or indirectly related to yield and quality. The dataset will serve as an important resource for gene discovery and deciphering the molecular mechanisms underlying various seed related traits.
Deciphering Transcriptional Programming during Pod and Seed Development Using RNA-Seq in Pigeonpea (Cajanus cajan)

PubMed Central

Pazhamala, Lekha T.; Agarwal, Gaurav; Bajaj, Prasad; Kumar, Vinay; Kulshreshtha, Akanksha; Saxena, Rachit K.; Varshney, Rajeev K.

2016-01-01

Seed development is an important event in plant life cycle that has interested humankind since ages, especially in crops of economic importance. Pigeonpea is an important grain legume of the semi-arid tropics, used mainly for its protein rich seeds. In order to understand the transcriptional programming during the pod and seed development, RNA-seq data was generated from embryo sac from the day of anthesis (0 DAA), seed and pod wall (5, 10, 20 and 30 DAA) of pigeonpea variety “Asha” (ICPL 87119) using Illumina HiSeq 2500. About 684 million sequencing reads have been generated from nine samples, which resulted in the identification of 27,441 expressed genes after sequence analysis. These genes have been studied for their differentially expression, co-expression, temporal and spatial gene expression. We have also used the RNA-seq data to identify important seed-specific transcription factors, biological processes and associated pathways during seed development process in pigeonpea. The comprehensive gene expression study from flowering to mature pod development in pigeonpea would be crucial in identifying candidate genes involved in seed traits directly or indirectly related to yield and quality. The dataset will serve as an important resource for gene discovery and deciphering the molecular mechanisms underlying various seed related traits. PMID:27760186
Next-Generation Sequencing of Two Mitochondrial Genomes from Family Pompilidae (Hymenoptera: Vespoidea) Reveal Novel Patterns of Gene Arrangement

PubMed Central

Chen, Peng-Yan; Zheng, Bo-Ying; Liu, Jing-Xian; Wei, Shu-Jun

2016-01-01

Animal mitochondrial genomes have provided large and diverse datasets for evolutionary studies. Here, the first two representative mitochondrial genomes from the family Pompilidae (Hymenoptera: Vespoidea) were determined using next-generation sequencing. The sequenced region of these two mitochondrial genomes from the species Auplopus sp. and Agenioideus sp. was 16,746 bp long with an A + T content of 83.12% and 16,596 bp long with an A + T content of 78.64%, respectively. In both species, all of the 37 typical mitochondrial genes were determined. The secondary structure of tRNA genes and rRNA genes were predicted and compared with those of other insects. Atypical trnS1 using abnormal anticodons TCT and lacking D-stem pairings was identified. There were 49 helices belonging to six domains in rrnL and 30 helices belonging to three domains in rrns present. Compared with the ancestral organization, four and two tRNA genes were rearranged in mitochondrial genomes of Auplopus and Agenioideus, respectively. In both species, trnM was shuffled upstream of the trnI-trnQ-trnM cluster, and trnA was translocated from the cluster trnA-trnR-trnN-trnS1-trnE-trnF to the region between nad1 and trnL1, which is novel to the Vespoidea. In Auplopus, the tRNA cluster trnW-trnC-trnY was shuffled to trnW-trnY-trnC. Phylogenetic analysis within Vespoidea revealed that Pompilidae and Mutillidae formed a sister lineage, and then sistered Formicidae. The genomes presented in this study have enriched the knowledge base of molecular markers, which is valuable in respect to studies about the gene rearrangement mechanism, genomic evolutionary processes and phylogeny of Hymenoptera. PMID:27727175
Improving the Quality of Positive Datasets for the Establishment of Machine Learning Models for pre-microRNA Detection.

PubMed

Demirci, Müşerref Duygu Saçar; Allmer, Jens

2017-07-28

MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.
Transcriptome analysis of Houttuynia cordata Thunb. by Illumina paired-end RNA sequencing and SSR marker discovery.

PubMed

Wei, Lin; Li, Shenghua; Liu, Shenggui; He, Anna; Wang, Dan; Wang, Jie; Tang, Yulian; Wu, Xianjin

2014-01-01

Houttuynia cordata Thunb. is an important traditional medical herb in China and other Asian countries, with high medicinal and economic value. However, a lack of available genomic information has become a limitation for research on this species. Thus, we carried out high-throughput transcriptomic sequencing of H. cordata to generate an enormous transcriptome sequence dataset for gene discovery and molecular marker development. Illumina paired-end sequencing technology produced over 56 million sequencing reads from H. cordata mRNA. Subsequent de novo assembly yielded 63,954 unigenes, 39,982 (62.52%) and 26,122 (40.84%) of which had significant similarity to proteins in the NCBI nonredundant protein and Swiss-Prot databases (E-value <10(-5)), respectively. Of these annotated unigenes, 30,131 and 15,363 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. In addition, 24,434 (38.21%) unigenes were mapped onto 128 pathways using the KEGG pathway database and 17,964 (44.93%) unigenes showed homology to Vitis vinifera (Vitaceae) genes in BLASTx analysis. Furthermore, 4,800 cDNA SSRs were identified as potential molecular markers. Fifty primer pairs were randomly selected to detect polymorphism among 30 samples of H. cordata; 43 (86%) produced fragments of expected size, suggesting that the unigenes were suitable for specific primer design and of high quality, and the SSR marker could be widely used in marker-assisted selection and molecular breeding of H. cordata in the future. This is the first application of Illumina paired-end sequencing technology to investigate the whole transcriptome of H. cordata and to assemble RNA-seq reads without a reference genome. These data should help researchers investigating the evolution and biological processes of this species. The SSR markers developed can be used for construction of high-resolution genetic linkage maps and for gene-based association analyses in H. cordata. This work will enable future functional genomic research and research into the distinctive active constituents of this genus.
Mango: multiple alignment with N gapped oligos.

PubMed

Zhang, Zefeng; Lin, Hao; Li, Ming

2008-06-01

Multiple sequence alignment is a classical and challenging task. The problem is NP-hard. The full dynamic programming takes too much time. The progressive alignment heuristics adopted by most state-of-the-art works suffer from the "once a gap, always a gap" phenomenon. Is there a radically new way to do multiple sequence alignment? In this paper, we introduce a novel and orthogonal multiple sequence alignment method, using both multiple optimized spaced seeds and new algorithms to handle these seeds efficiently. Our new algorithm processes information of all sequences as a whole and tries to build the alignment vertically, avoiding problems caused by the popular progressive approaches. Because the optimized spaced seeds have proved significantly more sensitive than the consecutive k-mers, the new approach promises to be more accurate and reliable. To validate our new approach, we have implemented MANGO: Multiple Alignment with N Gapped Oligos. Experiments were carried out on large 16S RNA benchmarks, showing that MANGO compares favorably, in both accuracy and speed, against state-of-the-art multiple sequence alignment methods, including ClustalW 1.83, MUSCLE 3.6, MAFFT 5.861, ProbConsRNA 1.11, Dialign 2.2.1, DIALIGN-T 0.2.1, T-Coffee 4.85, POA 2.0, and Kalign 2.0. We have further demonstrated the scalability of MANGO on very large datasets of repeat elements. MANGO can be downloaded at http://www.bioinfo.org.cn/mango/ and is free for academic usage.
Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

PubMed Central

White, James Robert; Nagarajan, Niranjan; Pop, Mihai

2009-01-01

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/. PMID:19360128
Noncontiguous finished genome sequence and description of Intestinimonas massiliensis sp. nov strain GD2T , the second Intestinimonas species cultured from the human gut.

PubMed

Afouda, Pamela; Durand, Guillaume A; Lagier, Jean-Christophe; Labas, Noémie; Cadoret, Fréderic; Armstrong, Nicholas; Raoult, Didier; Dubourg, Grégory

2018-04-14

Intestinimonas massiliensis sp. nov strain GD2 T is a new species of the genus Intestinimonas (the second, following Intestinimonas butyriciproducens gen. nov., sp. nov). First isolated from the gut microbiota of a healthy subject of French origin using a culturomics approach combined with taxono-genomics, it is strictly anaerobic, nonspore-forming, rod-shaped, with catalase- and oxidase-negative reactions. Its growth was observed after preincubation in an anaerobic blood culture enriched with sheep blood (5%) and rumen fluid (5%), incubated at 37°C. Its phenotypic and genotypic descriptions are presented in this paper with a full annotation of its genome sequence. This genome consists of 3,104,261 bp in length and contains 3,074 predicted genes, including 3,012 protein-coding genes and 62 RNA-coding genes. Strain GD2 T significantly produces butyrate and is frequently found among available 16S rRNA gene amplicon datasets, which leads consideration of Intestinimonas massiliensis as an important human gut commensal. © 2018 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.
Long Non-Coding RNA and Alternative Splicing Modulations in Parkinson's Leukocytes Identified by RNA Sequencing

PubMed Central

Soreq, Lilach; Guffanti, Alessandro; Salomonis, Nathan; Simchovitz, Alon; Israel, Zvi; Bergman, Hagai; Soreq, Hermona

2014-01-01

The continuously prolonged human lifespan is accompanied by increase in neurodegenerative diseases incidence, calling for the development of inexpensive blood-based diagnostics. Analyzing blood cell transcripts by RNA-Seq is a robust means to identify novel biomarkers that rapidly becomes a commonplace. However, there is lack of tools to discover novel exons, junctions and splicing events and to precisely and sensitively assess differential splicing through RNA-Seq data analysis and across RNA-Seq platforms. Here, we present a new and comprehensive computational workflow for whole-transcriptome RNA-Seq analysis, using an updated version of the software AltAnalyze, to identify both known and novel high-confidence alternative splicing events, and to integrate them with both protein-domains and microRNA binding annotations. We applied the novel workflow on RNA-Seq data from Parkinson's disease (PD) patients' leukocytes pre- and post- Deep Brain Stimulation (DBS) treatment and compared to healthy controls. Disease-mediated changes included decreased usage of alternative promoters and N-termini, 5′-end variations and mutually-exclusive exons. The PD regulated FUS and HNRNP A/B included prion-like domains regulated regions. We also present here a workflow to identify and analyze long non-coding RNAs (lncRNAs) via RNA-Seq data. We identified reduced lncRNA expression and selective PD-induced changes in 13 of over 6,000 detected leukocyte lncRNAs, four of which were inversely altered post-DBS. These included the U1 spliceosomal lncRNA and RP11-462G22.1, each entailing sequence complementarity to numerous microRNAs. Analysis of RNA-Seq from PD and unaffected controls brains revealed over 7,000 brain-expressed lncRNAs, of which 3,495 were co-expressed in the leukocytes including U1, which showed both leukocyte and brain increases. Furthermore, qRT-PCR validations confirmed these co-increases in PD leukocytes and two brain regions, the amygdala and substantia-nigra, compared to controls. This novel workflow allows deep multi-level inspection of RNA-Seq datasets and provides a comprehensive new resource for understanding disease transcriptome modifications in PD and other neurodegenerative diseases. PMID:24651478
Reads2Type: a web application for rapid microbial taxonomy identification.

PubMed

Saputra, Dhany; Rasmussen, Simon; Larsen, Mette V; Haddad, Nizar; Sperotto, Maria Maddalena; Aarestrup, Frank M; Lund, Ole; Sicheritz-Pontén, Thomas

2015-11-25

Identification of bacteria may be based on sequencing and molecular analysis of a specific locus such as 16S rRNA, or a set of loci such as in multilocus sequence typing. In the near future, healthcare institutions and routine diagnostic microbiology laboratories may need to sequence the entire genome of microbial isolates. Therefore we have developed Reads2Type, a web-based tool for taxonomy identification based on whole bacterial genome sequence data. Raw sequencing data provided by the user are mapped against a set of marker probes that are derived from currently available bacteria complete genomes. Using a dataset of 1003 whole genome sequenced bacteria from various sequencing platforms, Reads2Type was able to identify the species with 99.5 % accuracy and on the minutes time scale. In comparison with other tools, Reads2Type offers the advantage of not needing to transfer sequencing files, as the entire computational analysis is done on the computer of whom utilizes the web application. This also prevents data privacy issues to arise. The Reads2Type tool is available at http://www.cbs.dtu.dk/~dhany/reads2type.html.
Genome-Wide Identification and Comparative Analysis of Conserved and Novel MicroRNAs in Grafted Watermelon by High-Throughput Sequencing

PubMed Central

Liu, Na; Yang, Jinghua; Guo, Shaogui; Xu, Yong; Zhang, Mingfang

2013-01-01

MicroRNAs (miRNAs) are a class of endogenous small non-coding RNAs involved in the post-transcriptional gene regulation and play a critical role in plant growth, development and stresses response. However less is known about miRNAs involvement in grafting behaviors, especially with the watermelon (Citrullus lanatus L.) crop, which is one of the most important agricultural crops worldwide. Grafting method is commonly used in watermelon production in attempts to improve its adaptation to abiotic and biotic stresses, in particular to the soil-borne fusarium wilt disease. In this study, Solexa sequencing has been used to discover small RNA populations and compare miRNAs on genome-wide scale in watermelon grafting system. A total of 11,458,476, 11,614,094 and 9,339,089 raw reads representing 2,957,751, 2,880,328 and 2,964,990 unique sequences were obtained from the scions of self-grafted watermelon and watermelon grafted on-to bottle gourd and squash at two true-leaf stage, respectively. 39 known miRNAs belonging to 30 miRNA families and 80 novel miRNAs were identified in our small RNA dataset. Compared with self-grafted watermelon, 20 (5 known miRNA families and 15 novel miRNAs) and 47 (17 known miRNA families and 30 novel miRNAs) miRNAs were expressed significantly different in watermelon grafted on to bottle gourd and squash, respectively. MiRNAs expressed differentially when watermelon was grafted onto different rootstocks, suggesting that miRNAs might play an important role in diverse biological and metabolic processes in watermelon and grafting may possibly by changing miRNAs expressions to regulate plant growth and development as well as adaptation to stresses. The small RNA transcriptomes obtained in this study provided insights into molecular aspects of miRNA-mediated regulation in grafted watermelon. Obviously, this result would provide a basis for further unravelling the mechanism on how miRNAs information is exchanged between scion and rootstock in grafted watermelon, and its relevance to diverse biological processes and environmental adaptation. PMID:23468976
Triterpenoid Saponin Biosynthetic Pathway Profiling and Candidate Gene Mining of the Ilex asprella Root Using RNA-Seq

PubMed Central

Zheng, Xiasheng; Xu, Hui; Ma, Xinye; Zhan, Ruoting; Chen, Weiwen

2014-01-01

Ilex asprella, which contains abundant α-amyrin type triterpenoid saponins, is an anti-influenza herbal drug widely used in south China. In this work, we first analysed the transcriptome of the I. asprella root using RNA-Seq, which provided a dataset for functional gene mining. mRNA was isolated from the total RNA of the I. asprella root and reverse-transcribed into cDNA. Then, the cDNA library was sequenced using an Illumina HiSeq™ 2000, which generated 55,028,452 clean reads. De novo assembly of these reads generated 51,865 unigenes, in which 39,269 unigenes were annotated (75.71% yield). According to the structures of the triterpenoid saponins of I. asprella, a putative biosynthetic pathway downstream of 2,3-oxidosqualene was proposed and candidate unigenes in the transcriptome data that were potentially involved in the pathway were screened using homology-based BLAST and phylogenetic analysis. Further amplification and functional analysis of these putative unigenes will provide insight into the biosynthesis of Ilex triterpenoid saponins. PMID:24722569
ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq.

PubMed

Kucukural, Alper; Özadam, Hakan; Singh, Guramrit; Moore, Melissa J; Cenik, Can

2013-10-01

Unlike DNA, RNA abundances can vary over several orders of magnitude. Thus, identification of RNA-protein binding sites from high-throughput sequencing data presents unique challenges. Although peak identification in ChIP-Seq data has been extensively explored, there are few bioinformatics tools tailored for peak calling on analogous datasets for RNA-binding proteins. Here we describe ASPeak (abundance sensitive peak detection algorithm), an implementation of an algorithm that we previously applied to detect peaks in exon junction complex RNA immunoprecipitation in tandem experiments. Our peak detection algorithm yields stringent and robust target sets enabling sensitive motif finding and downstream functional analyses. ASPeak is implemented in Perl as a complete pipeline that takes bedGraph files as input. ASPeak implementation is freely available at https://sourceforge.net/projects/as-peak under the GNU General Public License. ASPeak can be run on a personal computer, yet is designed to be easily parallelizable. ASPeak can also run on high performance computing clusters providing efficient speedup. The documentation and user manual can be obtained from http://master.dl.sourceforge.net/project/as-peak/manual.pdf.
Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce.

PubMed

Nellore, Abhinav; Wilks, Christopher; Hansen, Kasper D; Leek, Jeffrey T; Langmead, Ben

2016-08-15

Public archives contain thousands of trillions of bases of valuable sequencing data. More than 40% of the Sequence Read Archive is human data protected by provisions such as dbGaP. To analyse dbGaP-protected data, researchers must typically work with IT administrators and signing officials to ensure all levels of security are implemented at their institution. This is a major obstacle, impeding reproducibility and reducing the utility of archived data. We present a protocol and software tool for analyzing protected data in a commercial cloud. The protocol, Rail-dbGaP, is applicable to any tool running on Amazon Web Services Elastic MapReduce. The tool, Rail-RNA v0.2, is a spliced aligner for RNA-seq data, which we demonstrate by running on 9662 samples from the dbGaP-protected GTEx consortium dataset. The Rail-dbGaP protocol makes explicit for the first time the steps an investigator must take to develop Elastic MapReduce pipelines that analyse dbGaP-protected data in a manner compliant with NIH guidelines. Rail-RNA automates implementation of the protocol, making it easy for typical biomedical investigators to study protected RNA-seq data, regardless of their local IT resources or expertise. Rail-RNA is available from http://rail.bio Technical details on the Rail-dbGaP protocol as well as an implementation walkthrough are available at https://github.com/nellore/rail-dbgap Detailed instructions on running Rail-RNA on dbGaP-protected data using Amazon Web Services are available at http://docs.rail.bio/dbgap/ : anellore@gmail.com or langmea@cs.jhu.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
Ranking viruses: measures of positional importance within networks define core viruses for rational polyvalent vaccine development.

PubMed

Anderson, Tavis K; Laegreid, William W; Cerutti, Francesco; Osorio, Fernando A; Nelson, Eric A; Christopher-Hennings, Jane; Goldberg, Tony L

2012-06-15

The extraordinary genetic and antigenic variability of RNA viruses is arguably the greatest challenge to the development of broadly effective vaccines. No single viral variant can induce sufficiently broad immunity, and incorporating all known naturally circulating variants into one multivalent vaccine is not feasible. Furthermore, no objective strategies currently exist to select actual viral variants that should be included or excluded in polyvalent vaccines. To address this problem, we demonstrate a method based on graph theory that quantifies the relative importance of viral variants. We demonstrate our method through application to the envelope glycoprotein gene of a particularly diverse RNA virus of pigs: porcine reproductive and respiratory syndrome virus (PRRSV). Using distance matrices derived from sequence nucleotide difference, amino acid difference and evolutionary distance, we constructed viral networks and used common network statistics to assign each sequence an objective ranking of relative 'importance'. To validate our approach, we use an independent published algorithm to score our top-ranked wild-type variants for coverage of putative T-cell epitopes across the 9383 sequences in our dataset. Top-ranked viruses achieve significantly higher coverage than low-ranked viruses, and top-ranked viruses achieve nearly equal coverage as a synthetic mosaic protein constructed in silico from the same set of 9383 sequences. Our approach relies on the network structure of PRRSV but applies to any diverse RNA virus because it identifies subsets of viral variants that are most important to overall viral diversity. We suggest that this method, through the objective quantification of variant importance, provides criteria for choosing viral variants for further characterization, diagnostics, surveillance and ultimately polyvalent vaccine development.
The evolution, diversity, and host associations of rhabdoviruses.

PubMed

Longdon, Ben; Murray, Gemma G R; Palmer, William J; Day, Jonathan P; Parker, Darren J; Welch, John J; Obbard, Darren J; Jiggins, Francis M

2015-01-01

Metagenomic studies are leading to the discovery of a hidden diversity of RNA viruses. These new viruses are poorly characterized and new approaches are needed predict the host species these viruses pose a risk to. The rhabdoviruses are a diverse family of RNA viruses that includes important pathogens of humans, animals, and plants. We have discovered thirty-two new rhabdoviruses through a combination of our own RNA sequencing of insects and searching public sequence databases. Combining these with previously known sequences we reconstructed the phylogeny of 195 rhabdovirus sequences, and produced the most in depth analysis of the family to date. In most cases we know nothing about the biology of the viruses beyond the host they were identified from, but our dataset provides a powerful phylogenetic approach to predict which are vector-borne viruses and which are specific to vertebrates or arthropods. By reconstructing ancestral and present host states we found that switches between major groups of hosts have occurred rarely during rhabdovirus evolution. This allowed us to propose seventy-six new likely vector-borne vertebrate viruses among viruses identified from vertebrates or biting insects. Based on currently available data, our analysis suggests it is likely there was a single origin of the known plant viruses and arthropod-borne vertebrate viruses, while vertebrate- and arthropod-specific viruses arose at least twice. There are also few transitions between aquatic and terrestrial ecosystems. Viruses also cluster together at a finer scale, with closely related viruses tending to be found in closely related hosts. Our data therefore suggest that throughout their evolution, rhabdoviruses have occasionally jumped between distantly related host species before spreading through related hosts in the same environment. This approach offers a way to predict the most probable biology and key traits of newly discovered viruses.
SwiSpot: modeling riboswitches by spotting out switching sequences.

PubMed

Barsacchi, Marco; Novoa, Eva Maria; Kellis, Manolis; Bechini, Alessio

2016-11-01

Riboswitches are cis-regulatory elements in mRNA, mostly found in Bacteria, which exhibit two main secondary structure conformations. Although one of them prevents the gene from being expressed, the other conformation allows its expression, and this switching process is typically driven by the presence of a specific ligand. Although there are a handful of known riboswitches, our knowledge in this field has been greatly limited due to our inability to identify their alternate structures from their sequences. Indeed, current methods are not able to predict the presence of the two functionally distinct conformations just from the knowledge of the plain RNA nucleotide sequence. Whether this would be possible, for which cases, and what prediction accuracy can be achieved, are currently open questions. Here we show that the two alternate secondary structures of riboswitches can be accurately predicted once the 'switching sequence' of the riboswitch has been properly identified. The proposed SwiSpot approach is capable of identifying the switching sequence inside a putative, complete riboswitch sequence, on the basis of pairing behaviors, which are evaluated on proper sets of configurations. Moreover, it is able to model the switching behavior of riboswitches whose generated ensemble covers both alternate configurations. Beyond structural predictions, the approach can also be paired to homology-based riboswitch searches. SwiSpot software, along with the reference dataset files, is available at: http://www.iet.unipi.it/a.bechini/swispot/Supplementary information: Supplementary data are available at Bioinformatics online. a.bechini@ing.unipi.it. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Fragment assignment in the cloud with eXpress-D

PubMed Central

2013-01-01

Background Probabilistic assignment of ambiguously mapped fragments produced by high-throughput sequencing experiments has been demonstrated to greatly improve accuracy in the analysis of RNA-Seq and ChIP-Seq, and is an essential step in many other sequence census experiments. A maximum likelihood method using the expectation-maximization (EM) algorithm for optimization is commonly used to solve this problem. However, batch EM-based approaches do not scale well with the size of sequencing datasets, which have been increasing dramatically over the past few years. Thus, current approaches to fragment assignment rely on heuristics or approximations for tractability. Results We present an implementation of a distributed EM solution to the fragment assignment problem using Spark, a data analytics framework that can scale by leveraging compute clusters within datacenters–“the cloud”. We demonstrate that our implementation easily scales to billions of sequenced fragments, while providing the exact maximum likelihood assignment of ambiguous fragments. The accuracy of the method is shown to be an improvement over the most widely used tools available and can be run in a constant amount of time when cluster resources are scaled linearly with the amount of input data. Conclusions The cloud offers one solution for the difficulties faced in the analysis of massive high-thoughput sequencing data, which continue to grow rapidly. Researchers in bioinformatics must follow developments in distributed systems–such as new frameworks like Spark–for ways to port existing methods to the cloud and help them scale to the datasets of the future. Our software, eXpress-D, is freely available at: http://github.com/adarob/express-d. PMID:24314033

The First Mitochondrial Genome for the Superfamily Hagloidea and Implications for Its Systematic Status in Ensifera

PubMed Central

Zhou, Zhijun; Shi, Fuming; Zhao, Ling

2014-01-01

Hagloidea Handlirsch, 1906 was an ancient group of Ensifera, that was much more diverse in the past extending at least into the Triassic, apparently diminishing in diversity through the Cretaceous, and now only represented by a few extant species. In this paper, we report the complete mitochondrial genome (mitogenome) of Tarragoilus diuturnus Gorochov, 2001, representing the first mitogenome of the superfamily Hagloidea. The size of the entire mitogenome of T. diuturnus is 16144 bp, containing 13 protein-coding genes (PCGs), 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes and one control region. The order and orientation of the gene arrangement pattern is identical to that of D. yakuba and most ensiferans species. A phylogenomic analysis was carried out based on the concatenated dataset of 13 PCGs and 2 rRNA genes from mitogenome sequences of 15 ensiferan species, comprising four superfamilies Grylloidea, Tettigonioidae, Rhaphidophoroidea and Hagloidea. Both maximum likelihood and Bayesian inference analyses strongly support Hagloidea T. diuturnus and Rhaphidophoroidea Troglophilus neglectus as forming a monophyletic group, sister to the Tettigonioidea. The relationships among four superfamilies of Ensifera were (Grylloidea, (Tettigonioidea, (Hagloidea, Rhaphidophoroidea))). PMID:24465850
Comparison of intraspecific, interspecific and intergeneric chloroplast diversity in Cycads

PubMed Central

Jiang, Guo-Feng; Hinsinger, Damien Daniel; Strijk, Joeri Sergej

2016-01-01

Cycads are among the most threatened plant species. Increasing the availability of genomic information by adding whole chloroplast data is a fundamental step in supporting phylogenetic studies and conservation efforts. Here, we assemble a dataset encompassing three taxonomic levels in cycads, including ten genera, three species in the genus Cycas and two individuals of C. debaoensis. Repeated sequences, SSRs and variations of the chloroplast were analyzed at the intraspecific, interspecific and intergeneric scale, and using our sequence data, we reconstruct a phylogenomic tree for cycads. The chloroplast was 162,094 bp in length, with 133 genes annotated, including 87 protein-coding, 37 tRNA and 8 rRNA genes. We found 7 repeated sequences and 39 SSRs. Seven loci showed promising levels of variations for application in DNA-barcoding. The chloroplast phylogeny confirmed the division of Cycadales in two suborders, each of them being monophyletic, revealing a contradiction with the current family circumscription and its evolution. Finally, 10 intraspecific SNPs were found. Our results showed that despite the extremely restricted distribution range of C. debaoensis, using complete chloroplast data is useful not only in intraspecific studies, but also to improve our understanding of cycad evolution and in defining conservation strategies for this emblematic group. PMID:27558458
WGSSAT: A High-Throughput Computational Pipeline for Mining and Annotation of SSR Markers From Whole Genomes.

PubMed

Pandey, Manmohan; Kumar, Ravindra; Srivastava, Prachi; Agarwal, Suyash; Srivastava, Shreya; Nagpure, Naresh S; Jena, Joy K; Kushwaha, Basdeo

2018-03-16

Mining and characterization of Simple Sequence Repeat (SSR) markers from whole genomes provide valuable information about biological significance of SSR distribution and also facilitate development of markers for genetic analysis. Whole genome sequencing (WGS)-SSR Annotation Tool (WGSSAT) is a graphical user interface pipeline developed using Java Netbeans and Perl scripts which facilitates in simplifying the process of SSR mining and characterization. WGSSAT takes input in FASTA format and automates the prediction of genes, noncoding RNA (ncRNA), core genes, repeats and SSRs from whole genomes followed by mapping of the predicted SSRs onto a genome (classified according to genes, ncRNA, repeats, exonic, intronic, and core gene region) along with primer identification and mining of cross-species markers. The program also generates a detailed statistical report along with visualization of mapped SSRs, genes, core genes, and RNAs. The features of WGSSAT were demonstrated using Takifugu rubripes data. This yielded a total of 139 057 SSR, out of which 113 703 SSR primer pairs were uniquely amplified in silico onto a T. rubripes (fugu) genome. Out of 113 703 mined SSRs, 81 463 were from coding region (including 4286 exonic and 77 177 intronic), 7 from RNA, 267 from core genes of fugu, whereas 105 641 SSR and 601 SSR primer pairs were uniquely mapped onto the medaka genome. WGSSAT is tested under Ubuntu Linux. The source code, documentation, user manual, example dataset and scripts are available online at https://sourceforge.net/projects/wgssat-nbfgr.
Visual exploration of parameter influence on phylogenetic trees.

PubMed

Hess, Martin; Bremm, Sebastian; Weissgraeber, Stephanie; Hamacher, Kay; Goesele, Michael; Wiemeyer, Josef; von Landesberger, Tatiana

2014-01-01

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.
Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data.

PubMed

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/. © The Author(s) 2015. Published by Oxford University Press.
A comprehensive simulation study on classification of RNA-Seq data.

PubMed

Zararsız, Gökmen; Goksuluk, Dincer; Korkmaz, Selcuk; Eldem, Vahap; Zararsiz, Gozde Erturk; Duru, Izzet Parug; Ozturk, Ahmet

2017-01-01

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.
Evaluation of commercially available small RNASeq library preparation kits using low input RNA.

PubMed

Yeri, Ashish; Courtright, Amanda; Danielson, Kirsty; Hutchins, Elizabeth; Alsop, Eric; Carlson, Elizabeth; Hsieh, Michael; Ziegler, Olivia; Das, Avash; Shah, Ravi V; Rozowsky, Joel; Das, Saumya; Van Keuren-Jensen, Kendall

2018-05-05

Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms. In this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome). The concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies.
SAMSA2: a standalone metatranscriptome analysis pipeline.

PubMed

Westreich, Samuel T; Treiber, Michelle L; Mills, David A; Korf, Ian; Lemay, Danielle G

2018-05-21

Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.
CrossQuery: a web tool for easy associative querying of transcriptome data.

PubMed

Wagner, Toni U; Fischer, Andreas; Thoma, Eva C; Schartl, Manfred

2011-01-01

Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deep-sequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.
The ISMARA client

PubMed Central

Ioannidis, Vassilios; van Nimwegen, Erik; Stockinger, Heinz

2016-01-01

ISMARA ( ismara.unibas.ch) automatically infers the key regulators and regulatory interactions from high-throughput gene expression or chromatin state data. However, given the large sizes of current next generation sequencing (NGS) datasets, data uploading times are a major bottleneck. Additionally, for proprietary data, users may be uncomfortable with uploading entire raw datasets to an external server. Both these problems could be alleviated by providing a means by which users could pre-process their raw data locally, transferring only a small summary file to the ISMARA server. We developed a stand-alone client application that pre-processes large input files (RNA-seq or ChIP-seq data) on the user's computer for performing ISMARA analysis in a completely automated manner, including uploading of small processed summary files to the ISMARA server. This reduces file sizes by up to a factor of 1000, and upload times from many hours to mere seconds. The client application is available from ismara.unibas.ch/ISMARA/client. PMID:28232860
RBind: computational network method to predict RNA binding sites.

PubMed

Wang, Kaili; Jian, Yiren; Wang, Huiwen; Zeng, Chen; Zhao, Yunjie

2018-04-26

Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction. The codes and datasets are available at https://zhaolab.com.cn/RBind. yjzhaowh@mail.ccnu.edu.cn. Supplementary data are available at Bioinformatics online.
Analyses of Sporocarps, Morphotyped Ectomycorrhizae, Environmental ITS and LSU Sequences Identify Common Genera that Occur at a Periglacial Site

PubMed Central

Jumpponen, Ari; Brown, Shawn P.; Trappe, James M.; Cázares, Efrén; Strömmer, Rauni

2015-01-01

Periglacial substrates exposed by retreating glaciers represent extreme and sensitive environments defined by a variety of abiotic stressors that challenge organismal establishment and survival. The simple communities often residing at these sites enable their analyses in depth. We utilized existing data and mined published sporocarp, morphotyped ectomycorrhizae (ECM), as well as environmental sequence data of internal transcribed spacer (ITS) and large subunit (LSU) regions of the ribosomal RNA gene to identify taxa that occur at a glacier forefront in the North Cascades Mountains in Washington State in the USA. The discrete data types consistently identified several common and widely distributed genera, perhaps best exemplified by Inocybe and Laccaria. Although we expected low diversity and richness, our environmental sequence data included 37 ITS and 26 LSU operational taxonomic units (OTUs) that likely form ECM. While environmental surveys of metabarcode markers detected large numbers of targeted ECM taxa, both the fruiting body and the morphotype datasets included genera that were undetected in either of the metabarcode datasets. These included hypogeous (Hymenogaster) and epigeous (Lactarius) taxa, some of which may produce large sporocarps but may possess small and/or spatially patchy genets. We highlight the importance of combining various data types to provide a comprehensive view of a fungal community, even in an environment assumed to host communities of low species richness and diversity. PMID:29376900
viGEN: An Open Source Pipeline for the Detection and Quantification of Viral RNA in Human Tumors.

PubMed

Bhuvaneshwar, Krithika; Song, Lei; Madhavan, Subha; Gusev, Yuriy

2018-01-01

An estimated 17% of cancers worldwide are associated with infectious causes. The extent and biological significance of viral presence/infection in actual tumor samples is generally unknown but could be measured using human transcriptome (RNA-seq) data from tumor samples. We present an open source bioinformatics pipeline viGEN, which allows for not only the detection and quantification of viral RNA, but also variants in the viral transcripts. The pipeline includes 4 major modules: The first module aligns and filter out human RNA sequences; the second module maps and count (remaining un-aligned) reads against reference genomes of all known and sequenced human viruses; the third module quantifies read counts at the individual viral-gene level thus allowing for downstream differential expression analysis of viral genes between case and controls groups. The fourth module calls variants in these viruses. To the best of our knowledge, there are no publicly available pipelines or packages that would provide this type of complete analysis in one open source package. In this paper, we applied the viGEN pipeline to two case studies. We first demonstrate the working of our pipeline on a large public dataset, the TCGA cervical cancer cohort. In the second case study, we performed an in-depth analysis on a small focused study of TCGA liver cancer patients. In the latter cohort, we performed viral-gene quantification, viral-variant extraction and survival analysis. This allowed us to find differentially expressed viral-transcripts and viral-variants between the groups of patients, and connect them to clinical outcome. From our analyses, we show that we were able to successfully detect the human papilloma virus among the TCGA cervical cancer patients. We compared the viGEN pipeline with two metagenomics tools and demonstrate similar sensitivity/specificity. We were also able to quantify viral-transcripts and extract viral-variants using the liver cancer dataset. The results presented corresponded with published literature in terms of rate of detection, and impact of several known variants of HBV genome. This pipeline is generalizable, and can be used to provide novel biological insights into microbial infections in complex diseases and tumorigeneses. Our viral pipeline could be used in conjunction with additional type of immuno-oncology analysis based on RNA-seq data of host RNA for cancer immunology applications. The source code, with example data and tutorial is available at: https://github.com/ICBI/viGEN/.
QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii.

PubMed

Shen, Bang; Powell, Robin H; Behnke, Michael S

2017-06-22

Scientific knowledge is intrinsically linked to available technologies and methods. This article will present two methods that allowed for the identification and verification of a drug resistance gene in the Apicomplexan parasite Toxoplasma gondii, the method of Quantitative Trait Locus (QTL) mapping using a Whole Genome Sequence (WGS) -based genetic map and the method of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 -based gene editing. The approach of QTL mapping allows one to test if there is a correlation between a genomic region(s) and a phenotype. Two datasets are required to run a QTL scan, a genetic map based on the progeny of a recombinant cross and a quantifiable phenotype assessed in each of the progeny of that cross. These datasets are then formatted to be compatible with R/qtl software that generates a QTL scan to identify significant loci correlated with the phenotype. Although this can greatly narrow the search window of possible candidates, QTLs span regions containing a number of genes from which the causal gene needs to be identified. Having WGS of the progeny was critical to identify the causal drug resistance mutation at the gene level. Once identified, the candidate mutation can be verified by genetic manipulation of drug sensitive parasites. The most facile and efficient method to genetically modify T. gondii is the CRISPR/Cas9 system. This system comprised of just 2 components both encoded on a single plasmid, a single guide RNA (gRNA) containing a 20 bp sequence complementary to the genomic target and the Cas9 endonuclease that generates a double-strand DNA break (DSB) at the target, repair of which allows for insertion or deletion of sequences around the break site. This article provides detailed protocols to use CRISPR/Cas9 based genome editing tools to verify the gene responsible for sinefungin resistance and to construct transgenic parasites.
QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

PubMed Central

Shen, Bang; Powell, Robin H.; Behnke, Michael S.

2017-01-01

Scientific knowledge is intrinsically linked to available technologies and methods. This article will present two methods that allowed for the identification and verification of a drug resistance gene in the Apicomplexan parasite Toxoplasma gondii, the method of Quantitative Trait Locus (QTL) mapping using a Whole Genome Sequence (WGS) -based genetic map and the method of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 -based gene editing. The approach of QTL mapping allows one to test if there is a correlation between a genomic region(s) and a phenotype. Two datasets are required to run a QTL scan, a genetic map based on the progeny of a recombinant cross and a quantifiable phenotype assessed in each of the progeny of that cross. These datasets are then formatted to be compatible with R/qtl software that generates a QTL scan to identify significant loci correlated with the phenotype. Although this can greatly narrow the search window of possible candidates, QTLs span regions containing a number of genes from which the causal gene needs to be identified. Having WGS of the progeny was critical to identify the causal drug resistance mutation at the gene level. Once identified, the candidate mutation can be verified by genetic manipulation of drug sensitive parasites. The most facile and efficient method to genetically modify T. gondii is the CRISPR/Cas9 system. This system comprised of just 2 components both encoded on a single plasmid, a single guide RNA (gRNA) containing a 20 bp sequence complementary to the genomic target and the Cas9 endonuclease that generates a double-strand DNA break (DSB) at the target, repair of which allows for insertion or deletion of sequences around the break site. This article provides detailed protocols to use CRISPR/Cas9 based genome editing tools to verify the gene responsible for sinefungin resistance and to construct transgenic parasites. PMID:28671645
Annotation of the Transcriptome from Taenia pisiformis and Its Comparative Analysis with Three Taeniidae Species

PubMed Central

Yang, Deying; Fu, Yan; Wu, Xuhang; Xie, Yue; Nie, Huaming; Chen, Lin; Nong, Xiang; Gu, Xiaobin; Wang, Shuxian; Peng, Xuerong; Yan, Ning; Zhang, Runhui; Zheng, Wanpeng; Yang, Guangyou

2012-01-01

Background Taenia pisiformis is one of the most common intestinal tapeworms and can cause infections in canines. Adult T. pisiformis (canines as definitive hosts) and Cysticercus pisiformis (rabbits as intermediate hosts) cause significant health problems to the host and considerable socio-economic losses as a consequence. No complete genomic data regarding T. pisiformis are currently available in public databases. RNA-seq provides an effective approach to analyze the eukaryotic transcriptome to generate large functional gene datasets that can be used for further studies. Methodology/Principal Findings In this study, 2.67 million sequencing clean reads and 72,957 unigenes were generated using the RNA-seq technique. Based on a sequence similarity search with known proteins, a total of 26,012 unigenes (no redundancy) were identified after quality control procedures via the alignment of four databases. Overall, 15,920 unigenes were mapped to 203 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Through analyzing the glycolysis/gluconeogenesis and axonal guidance pathways, we achieved an in-depth understanding of the biochemistry of T. pisiformis. Here, we selected four unigenes at random and obtained their full-length cDNA clones using RACE PCR. Functional distribution characteristics were gained through comparing four cestode species (72,957 unigenes of T. pisiformis, 30,700 ESTs of T. solium, 1,058 ESTs of Eg+Em [conserved ESTs between Echinococcus granulosus and Echinococcus multilocularis]), with the cluster of orthologous groups (COG) and gene ontology (GO) functional classification systems. Furthermore, the conserved common genes in these four cestode species were obtained and aligned by the KEGG database. Conclusion This study provides an extensive transcriptome dataset obtained from the deep sequencing of T. pisiformis in a non-model whole genome. The identification of conserved genes may provide novel approaches for potential drug targets and vaccinations against cestode infections. Research can now accelerate into the functional genomics, immunity and gene expression profiles of cestode species. PMID:22514598
RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets.

PubMed

Scheuch, Matthias; Höper, Dirk; Beer, Martin

2015-03-03

Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck. To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS - Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets. RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.
De novo transcriptome assembly for a non-model species, the blood-sucking bug Triatoma brasiliensis, a vector of Chagas disease.

PubMed

Marchant, A; Mougel, F; Almeida, C; Jacquin-Joly, E; Costa, J; Harry, M

2015-04-01

High throughput sequencing (HTS) provides new research opportunities for work on non-model organisms, such as differential expression studies between populations exposed to different environmental conditions. However, such transcriptomic studies first require the production of a reference assembly. The choice of sampling procedure, sequencing strategy and assembly workflow is crucial. To develop a reliable reference transcriptome for Triatoma brasiliensis, the major Chagas disease vector in Northeastern Brazil, different de novo assembly protocols were generated using various datasets and software. Both 454 and Illumina sequencing technologies were applied on RNA extracted from antennae and mouthparts from single or pooled individuals. The 454 library yielded 278 Mb. Fifteen Illumina libraries were constructed and yielded nearly 360 million RNA-seq single reads and 46 million RNA-seq paired-end reads for nearly 45 Gb. For the 454 reads, we used three assemblers, Newbler, CAP3 and/or MIRA and for the Illumina reads, the Trinity assembler. Ten assembly workflows were compared using these programs separately or in combination. To compare the assemblies obtained, quantitative and qualitative criteria were used, including contig length, N50, contig number and the percentage of chimeric contigs. Completeness of the assemblies was estimated using the CEGMA pipeline. The best assembly (57,657 contigs, completeness of 80 %, <1 % chimeric contigs) was a hybrid assembly leading to recommend the use of (1) a single individual with large representation of biological tissues, (2) merging both long reads and short paired-end Illumina reads, (3) several assemblers in order to combine the specific advantages of each.
Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

PubMed

Richard, Arianne C; Lyons, Paul A; Peters, James E; Biasci, Daniele; Flint, Shaun M; Lee, James C; McKinney, Eoin F; Siegel, Richard M; Smith, Kenneth G C

2014-08-04

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.
Base-Calling Algorithm with Vocabulary (BCV) Method for Analyzing Population Sequencing Chromatograms

PubMed Central

Fantin, Yuri S.; Neverov, Alexey D.; Favorov, Alexander V.; Alvarez-Figueroa, Maria V.; Braslavskaya, Svetlana I.; Gordukova, Maria A.; Karandashova, Inga V.; Kuleshov, Konstantin V.; Myznikova, Anna I.; Polishchuk, Maya S.; Reshetov, Denis A.; Voiciehovskaya, Yana A.; Mironov, Andrei A.; Chulanov, Vladimir P.

2013-01-01

Sanger sequencing is a common method of reading DNA sequences. It is less expensive than high-throughput methods, and it is appropriate for numerous applications including molecular diagnostics. However, sequencing mixtures of similar DNA of pathogens with this method is challenging. This is important because most clinical samples contain such mixtures, rather than pure single strains. The traditional solution is to sequence selected clones of PCR products, a complicated, time-consuming, and expensive procedure. Here, we propose the base-calling with vocabulary (BCV) method that computationally deciphers Sanger chromatograms obtained from mixed DNA samples. The inputs to the BCV algorithm are a chromatogram and a dictionary of sequences that are similar to those we expect to obtain. We apply the base-calling function on a test dataset of chromatograms without ambiguous positions, as well as one with 3–14% sequence degeneracy. Furthermore, we use BCV to assemble a consensus sequence for an HIV genome fragment in a sample containing a mixture of viral DNA variants and to determine the positions of the indels. Finally, we detect drug-resistant Mycobacterium tuberculosis strains carrying frameshift mutations mixed with wild-type bacteria in the pncA gene, and roughly characterize bacterial communities in clinical samples by direct 16S rRNA sequencing. PMID:23382983

Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data.

PubMed

Paisitkriangkrai, Sakrapee; Quek, Kelly; Nievergall, Eva; Jabbour, Anissa; Zannettino, Andrew; Kok, Chung Hoow

2018-06-07

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .
ProbFold: a probabilistic method for integration of probing data in RNA secondary structure prediction.

PubMed

Sahoo, Sudhakar; Świtnicki, Michał P; Pedersen, Jakob Skou

2016-09-01

Recently, new RNA secondary structure probing techniques have been developed, including Next Generation Sequencing based methods capable of probing transcriptome-wide. These techniques hold great promise for improving structure prediction accuracy. However, each new data type comes with its own signal properties and biases, which may even be experiment specific. There is therefore a growing need for RNA structure prediction methods that can be automatically trained on new data types and readily extended to integrate and fully exploit multiple types of data. Here, we develop and explore a modular probabilistic approach for integrating probing data in RNA structure prediction. It can be automatically trained given a set of known structures with probing data. The approach is demonstrated on SHAPE datasets, where we evaluate and selectively model specific correlations. The approach often makes superior use of the probing data signal compared to other methods. We illustrate the use of ProbFold on multiple data types using both simulations and a small set of structures with both SHAPE, DMS and CMCT data. Technically, the approach combines stochastic context-free grammars (SCFGs) with probabilistic graphical models. This approach allows rapid adaptation and integration of new probing data types. ProbFold is implemented in C ++. Models are specified using simple textual formats. Data reformatting is done using separate C ++ programs. Source code, statically compiled binaries for x86 Linux machines, C ++ programs, example datasets and a tutorial is available from http://moma.ki.au.dk/prj/probfold/ : jakob.skou@clin.au.dk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
All-atom four-body knowledge-based statistical potential to distinguish native tertiary RNA structures from nonnative folds.

PubMed

Masso, Majid

2018-09-14

Scientific breakthroughs in recent decades have uncovered the capability of RNA molecules to fulfill a wide array of structural, functional, and regulatory roles in living cells, leading to a concomitantly significant increase in both the number and diversity of experimentally determined RNA three-dimensional (3D) structures. Atomic coordinates from a representative training set of solved RNA structures, displaying low sequence and structure similarity, facilitate derivation of knowledge-based energy functions. Here we develop an all-atom four-body statistical potential and evaluate its capacity to distinguish native RNA 3D structures from nonnative folds based on calculated free energy scores. Atomic four-body nearest-neighbors are objectively identified by their occurrence as tetrahedral vertices in the Delaunay tessellations of RNA structures, and rates of atomic quadruplet interactions expected by chance are obtained from a multinomial reference distribution. Our four-body energy function, referred to as RAMP (ribonucleic acids multibody potential), is subsequently derived by applying the inverted Boltzmann principle to the frequency data, yielding an energy score for each type of atomic quadruplet interaction. Several well-known benchmark datasets reveal that RAMP is comparable with, and often outperforms, existing knowledge- and physics-based energy functions. To the best of our knowledge, this is the first study detailing an RNA tertiary structure-based multibody statistical potential and its comparative evaluation. Copyright © 2018 Elsevier Ltd. All rights reserved.
Biogeography of the ecosystems of the healthy human body.

PubMed

Zhou, Yanjiao; Gao, Hongyu; Mihindukulasuriya, Kathie A; La Rosa, Patricio S; Wylie, Kristine M; Vishnivetskaya, Tatiana; Podar, Mircea; Warner, Barb; Tarr, Phillip I; Nelson, David E; Fortenberry, J Dennis; Holland, Martin J; Burr, Sarah E; Shannon, William D; Sodergren, Erica; Weinstock, George M

2013-01-14

Characterizing the biogeography of the microbiome of healthy humans is essential for understanding microbial associated diseases. Previous studies mainly focused on a single body habitat from a limited set of subjects. Here, we analyzed one of the largest microbiome datasets to date and generated a biogeographical map that annotates the biodiversity, spatial relationships, and temporal stability of 22 habitats from 279 healthy humans. We identified 929 genera from more than 24 million 16S rRNA gene sequences of 22 habitats, and we provide a baseline of inter-subject variation for healthy adults. The oral habitat has the most stable microbiota with the highest alpha diversity, while the skin and vaginal microbiota are less stable and show lower alpha diversity. The level of biodiversity in one habitat is independent of the biodiversity of other habitats in the same individual. The abundances of a given genus at a body site in which it dominates do not correlate with the abundances at body sites where it is not dominant. Additionally, we observed the human microbiota exhibit both cosmopolitan and endemic features. Finally, comparing datasets of different projects revealed a project-based clustering pattern, emphasizing the significance of standardization of metagenomic studies. The data presented here extend the definition of the human microbiome by providing a more complete and accurate picture of human microbiome biogeography, addressing questions best answered by a large dataset of subjects and body sites that are deeply sampled by sequencing.
Biogeography of the ecosystems of the healthy human body

PubMed Central

2013-01-01

Background Characterizing the biogeography of the microbiome of healthy humans is essential for understanding microbial associated diseases. Previous studies mainly focused on a single body habitat from a limited set of subjects. Here, we analyzed one of the largest microbiome datasets to date and generated a biogeographical map that annotates the biodiversity, spatial relationships, and temporal stability of 22 habitats from 279 healthy humans. Results We identified 929 genera from more than 24 million 16S rRNA gene sequences of 22 habitats, and we provide a baseline of inter-subject variation for healthy adults. The oral habitat has the most stable microbiota with the highest alpha diversity, while the skin and vaginal microbiota are less stable and show lower alpha diversity. The level of biodiversity in one habitat is independent of the biodiversity of other habitats in the same individual. The abundances of a given genus at a body site in which it dominates do not correlate with the abundances at body sites where it is not dominant. Additionally, we observed the human microbiota exhibit both cosmopolitan and endemic features. Finally, comparing datasets of different projects revealed a project-based clustering pattern, emphasizing the significance of standardization of metagenomic studies. Conclusions The data presented here extend the definition of the human microbiome by providing a more complete and accurate picture of human microbiome biogeography, addressing questions best answered by a large dataset of subjects and body sites that are deeply sampled by sequencing. PMID:23316946
Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells.

PubMed

Winterhoff, Boris J; Maile, Makayla; Mitra, Amit Kumar; Sebe, Attila; Bazzaro, Martina; Geller, Melissa A; Abrahante, Juan E; Klein, Molly; Hellweg, Raffaele; Mullany, Sally A; Beckman, Kenneth; Daniel, Jerry; Starr, Timothy K

2017-03-01

The purpose of this study was to determine the level of heterogeneity in high grade serous ovarian cancer (HGSOC) by analyzing RNA expression in single epithelial and cancer associated stromal cells. In addition, we explored the possibility of identifying subgroups based on pathway activation and pre-defined signatures from cancer stem cells and chemo-resistant cells. A fresh, HGSOC tumor specimen derived from ovary was enzymatically digested and depleted of immune infiltrating cells. RNA sequencing was performed on 92 single cells and 66 of these single cell datasets passed quality control checks. Sequences were analyzed using multiple bioinformatics tools, including clustering, principle components analysis, and geneset enrichment analysis to identify subgroups and activated pathways. Immunohistochemistry for ovarian cancer, stem cell and stromal markers was performed on adjacent tumor sections. Analysis of the gene expression patterns identified two major subsets of cells characterized by epithelial and stromal gene expression patterns. The epithelial group was characterized by proliferative genes including genes associated with oxidative phosphorylation and MYC activity, while the stromal group was characterized by increased expression of extracellular matrix (ECM) genes and genes associated with epithelial-to-mesenchymal transition (EMT). Neither group expressed a signature correlating with published chemo-resistant gene signatures, but many cells, predominantly in the stromal subgroup, expressed markers associated with cancer stem cells. Single cell sequencing provides a means of identifying subpopulations of cancer cells within a single patient. Single cell sequence analysis may prove to be critical for understanding the etiology, progression and drug resistance in ovarian cancer. Copyright Â© 2017 Elsevier Inc. All rights reserved.
Benchmark Dataset for Whole Genome Sequence Compression.

PubMed

C L, Biji; S Nair, Achuthsankar

2017-01-01

The research in DNA data compression lacks a standard dataset to test out compression tools specific to DNA. This paper argues that the current state of achievement in DNA compression is unable to be benchmarked in the absence of such scientifically compiled whole genome sequence dataset and proposes a benchmark dataset using multistage sampling procedure. Considering the genome sequence of organisms available in the National Centre for Biotechnology and Information (NCBI) as the universe, the proposed dataset selects 1,105 prokaryotes, 200 plasmids, 164 viruses, and 65 eukaryotes. This paper reports the results of using three established tools on the newly compiled dataset and show that their strength and weakness are evident only with a comparison based on the scientifically compiled benchmark dataset. The sample dataset and the respective links are available @ https://sourceforge.net/projects/benchmarkdnacompressiondataset/.
Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling.

PubMed

Łabaj, Paweł P; Leparc, Germán G; Linggi, Bryan E; Markillie, Lye Meng; Wiley, H Steven; Kreil, David P

2011-07-01

Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means. We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, <30% of all transcripts could be quantified reliably with a relative error<20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision. rnaseq10@boku.ac.at
VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

PubMed Central

Lai, Zhongwu; Markovets, Aleksandra; Ahdesmaki, Miika; Chapman, Brad; Hofmann, Oliver; McEwen, Robert; Johnson, Justin; Dougherty, Brian; Barrett, J. Carl; Dry, Jonathan R.

2016-01-01

Abstract Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research. PMID:27060149
Integrative Analysis of Many RNA-Seq Datasets to Study Alternative Splicing

PubMed Central

Li, Wenyuan; Dai, Chao; Kang, Shuli; Zhou, Xianghong Jasmine

2014-01-01

Alternative splicing is an important gene regulatory mechanism that dramatically increases the complexity of the proteome. However, how alternative splicing is regulated and how transcription and splicing are coordinated are still poorly understood, and functions of transcript isoforms have been studied only in a few limited cases. Nowadays, RNA-seq technology provides an exceptional opportunity to study alternative splicing on genome-wide scales and in an unbiased manner. With the rapid accumulation of data in public repositories, new challenges arise from the urgent need to effectively integrate many different RNA-seq datasets for study alterative splicing. This paper discusses a set of advanced computational methods that can integrate and analyze many RNA-seq datasets to systematically identify splicing modules, unravel the coupling of transcription and splicing, and predict the functions of splicing isoforms on a genome-wide scale. PMID:24583115
A transcriptome resource for the koala (Phascolarctos cinereus): insights into koala retrovirus transcription and sequence diversity.

PubMed

Hobbs, Matthew; Pavasovic, Ana; King, Andrew G; Prentis, Peter J; Eldridge, Mark D B; Chen, Zhiliang; Colgan, Donald J; Polkinghorne, Adam; Wilkins, Marc R; Flanagan, Cheyne; Gillett, Amber; Hanger, Jon; Johnson, Rebecca N; Timms, Peter

2014-09-11

The koala, Phascolarctos cinereus, is a biologically unique and evolutionarily distinct Australian arboreal marsupial. The goal of this study was to sequence the transcriptome from several tissues of two geographically separate koalas, and to create the first comprehensive catalog of annotated transcripts for this species, enabling detailed analysis of the unique attributes of this threatened native marsupial, including infection by the koala retrovirus. RNA-Seq data was generated from a range of tissues from one male and one female koala and assembled de novo into transcripts using Velvet-Oases. Transcript abundance in each tissue was estimated. Transcripts were searched for likely protein-coding regions and a non-redundant set of 117,563 putative protein sequences was produced. In similarity searches there were 84,907 (72%) sequences that aligned to at least one sequence in the NCBI nr protein database. The best alignments were to sequences from other marsupials. After applying a reciprocal best hit requirement of koala sequences to those from tammar wallaby, Tasmanian devil and the gray short-tailed opossum, we estimate that our transcriptome dataset represents approximately 15,000 koala genes. The marsupial alignment information was used to look for potential gene duplications and we report evidence for copy number expansion of the alpha amylase gene, and of an aldehyde reductase gene.Koala retrovirus (KoRV) transcripts were detected in the transcriptomes. These were analysed in detail and the structure of the spliced envelope gene transcript was determined. There was appreciable sequence diversity within KoRV, with 233 sites in the KoRV genome showing small insertions/deletions or single nucleotide polymorphisms. Both koalas had sequences from the KoRV-A subtype, but the male koala transcriptome has, in addition, sequences more closely related to the KoRV-B subtype. This is the first report of a KoRV-B-like sequence in a wild population. This transcriptomic dataset is a useful resource for molecular genetic studies of the koala, for evolutionary genetic studies of marsupials, for validation and annotation of the koala genome sequence, and for investigation of koala retrovirus. Annotated transcripts can be browsed and queried at http://koalagenome.org.
iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.

PubMed

Xu, Zhao-Chun; Wang, Peng; Qiu, Wang-Ren; Xiao, Xuan

2017-08-15

Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed. To identify the splice donor sites and splice acceptor sites accurately and quickly, a deep sparse auto-encoder model with two hidden layers, called iSS-PC, was constructed based on minimum error law, in which we incorporated twelve physical-chemical properties of the dinucleotides within DNA into PseDNC to formulate given sequence samples via a battery of cross-covariance and auto-covariance transformations. In this paper, five-fold cross-validation test results based on the same benchmark data-sets indicated that the new predictor remarkably outperformed the existing prediction methods in this field. Furthermore, it is expected that many other related problems can be also studied by this approach. To implement classification accurately and quickly, an easy-to-use web-server for identifying slicing sites has been established for free access at: http://www.jci-bioinfo.cn/iSS-PC.
ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets

PubMed Central

Brozovic, Matija; Dantec, Christelle; Dardaillon, Justine; Dauga, Delphine; Faure, Emmanuel; Gineste, Mathieu; Louis, Alexandra; Naville, Magali; Nitta, Kazuhiro R; Piette, Jacques; Reeves, Wendy; Scornavacca, Céline; Simion, Paul; Vincentelli, Renaud; Bellec, Maelle; Aicha, Sameh Ben; Fagotto, Marie; Guéroult-Bellone, Marion; Haeussler, Maximilian; Jacox, Edwin; Lowe, Elijah K; Mendez, Mickael; Roberge, Alexis; Stolfi, Alberto; Yokomori, Rui; Cambillau, Christian; Christiaen, Lionel; Delsuc, Frédéric; Douzery, Emmanuel; Dumollard, Rémi; Kusakabe, Takehiro; Nakai, Kenta; Nishida, Hiroki; Satou, Yutaka; Swalla, Billie; Veeman, Michael; Volff, Jean-Nicolas

2018-01-01

Abstract ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates. PMID:29149270
TIMPs of parasitic helminths - a large-scale analysis of high-throughput sequence datasets.

PubMed

Cantacessi, Cinzia; Hofmann, Andreas; Pickering, Darren; Navarro, Severine; Mitreva, Makedonka; Loukas, Alex

2013-05-30

Tissue inhibitors of metalloproteases (TIMPs) are a multifunctional family of proteins that orchestrate extracellular matrix turnover, tissue remodelling and other cellular processes. In parasitic helminths, such as hookworms, TIMPs have been proposed to play key roles in the host-parasite interplay, including invasion of and establishment in the vertebrate animal hosts. Currently, knowledge of helminth TIMPs is limited to a small number of studies on canine hookworms, whereas no information is available on the occurrence of TIMPs in other parasitic helminths causing neglected diseases. In the present study, we conducted a large-scale investigation of TIMP proteins of a range of neglected human parasites including the hookworm Necator americanus, the roundworm Ascaris suum, the liver flukes Clonorchis sinensis and Opisthorchis viverrini, as well as the schistosome blood flukes. This entailed mining available transcriptomic and/or genomic sequence datasets for the presence of homologues of known TIMPs, predicting secondary structures of defined protein sequences, systematic phylogenetic analyses and assessment of differential expression of genes encoding putative TIMPs in the developmental stages of A. suum, N. americanus and Schistosoma haematobium which infect the mammalian hosts. A total of 15 protein sequences with high homology to known eukaryotic TIMPs were predicted from the complement of sequence data available for parasitic helminths and subjected to in-depth bioinformatic analyses. Supported by the availability of gene manipulation technologies such as RNA interference and/or transgenesis, this work provides a basis for future functional explorations of helminth TIMPs and, in particular, of their role/s in fundamental biological pathways linked to long-term establishment in the vertebrate hosts, with a view towards the development of novel approaches for the control of neglected helminthiases.
TIMPs of parasitic helminths – a large-scale analysis of high-throughput sequence datasets

PubMed Central

2013-01-01

Background Tissue inhibitors of metalloproteases (TIMPs) are a multifunctional family of proteins that orchestrate extracellular matrix turnover, tissue remodelling and other cellular processes. In parasitic helminths, such as hookworms, TIMPs have been proposed to play key roles in the host-parasite interplay, including invasion of and establishment in the vertebrate animal hosts. Currently, knowledge of helminth TIMPs is limited to a small number of studies on canine hookworms, whereas no information is available on the occurrence of TIMPs in other parasitic helminths causing neglected diseases. Methods In the present study, we conducted a large-scale investigation of TIMP proteins of a range of neglected human parasites including the hookworm Necator americanus, the roundworm Ascaris suum, the liver flukes Clonorchis sinensis and Opisthorchis viverrini, as well as the schistosome blood flukes. This entailed mining available transcriptomic and/or genomic sequence datasets for the presence of homologues of known TIMPs, predicting secondary structures of defined protein sequences, systematic phylogenetic analyses and assessment of differential expression of genes encoding putative TIMPs in the developmental stages of A. suum, N. americanus and Schistosoma haematobium which infect the mammalian hosts. Results A total of 15 protein sequences with high homology to known eukaryotic TIMPs were predicted from the complement of sequence data available for parasitic helminths and subjected to in-depth bioinformatic analyses. Conclusions Supported by the availability of gene manipulation technologies such as RNA interference and/or transgenesis, this work provides a basis for future functional explorations of helminth TIMPs and, in particular, of their role/s in fundamental biological pathways linked to long-term establishment in the vertebrate hosts, with a view towards the development of novel approaches for the control of neglected helminthiases. PMID:23721526
NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types.

PubMed

Lee, Sejoon; Lee, Soohyun; Ouellette, Scott; Park, Woong-Yang; Lee, Eunjung A; Park, Peter J

2017-06-20

In many next-generation sequencing (NGS) studies, multiple samples or data types are profiled for each individual. An important quality control (QC) step in these studies is to ensure that datasets from the same subject are properly paired. Given the heterogeneity of data types, file types and sequencing depths in a multi-dimensional study, a robust program that provides a standardized metric for genotype comparisons would be useful. Here, we describe NGSCheckMate, a user-friendly software package for verifying sample identities from FASTQ, BAM or VCF files. This tool uses a model-based method to compare allele read fractions at known single-nucleotide polymorphisms, considering depth-dependent behavior of similarity metrics for identical and unrelated samples. Our evaluation shows that NGSCheckMate is effective for a variety of data types, including exome sequencing, whole-genome sequencing, RNA-seq, ChIP-seq, targeted sequencing and single-cell whole-genome sequencing, with a minimal requirement for sequencing depth (>0.5X). An alignment-free module can be run directly on FASTQ files for a quick initial check. We recommend using this software as a QC step in NGS studies. https://github.com/parklab/NGSCheckMate. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer's disease.

PubMed

Patrick, Ellis; Rajagopal, Sathyapriya; Wong, Hon-Kit Andus; McCabe, Cristin; Xu, Jishu; Tang, Anna; Imboywa, Selina H; Schneider, Julie A; Pochet, Nathalie; Krichevsky, Anna M; Chibnik, Lori B; Bennett, David A; De Jager, Philip L

2017-07-01

Given multiple studies of brain microRNA (miRNA) in relation to Alzheimer's disease (AD) with few consistent results and the heterogeneity of this disease, the objective of this study was to explore their mechanism by evaluating their relation to different elements of Alzheimer's disease pathology, confounding factors and mRNA expression data from the same subjects in the same brain region. We report analyses of expression profiling of miRNA (n = 700 subjects) and lincRNA (n = 540 subjects) from the dorsolateral prefrontal cortex of individuals participating in two longitudinal cohort studies of aging. We confirm the association of two well-established miRNA (miR-132, miR-129) with pathologic AD in our dataset and then further characterize this association in terms of its component neuritic β-amyloid plaques and neurofibrillary tangle pathologies. Additionally, we identify one new miRNA (miR-99) and four lincRNA that are associated with these traits. Many other previously reported associations of microRNA with AD are associated with the confounders quantified in our longitudinal cohort. Finally, by performing analyses integrating both miRNA and RNA sequence data from the same individuals (525 samples), we characterize the impact of AD associated miRNA on human brain expression: we show that the effects of miR-132 and miR-129-5b converge on certain genes such as EP300 and find a role for miR200 and its target genes in AD using an integrated miRNA/mRNA analysis. Overall, miRNAs play a modest role in human AD, but we observe robust evidence that a small number of miRNAs are responsible for specific alterations in the cortical transcriptome that are associated with AD.
Biogeochemical Typing of Paddy Field by a Data-Driven Approach Revealing Sub-Systems within a Complex Environment - A Pipeline to Filtrate, Organize and Frame Massive Dataset from Multi-Omics Analyses

PubMed Central

Ogawa, Diogo M. O.; Moriya, Shigeharu; Tsuboi, Yuuri; Date, Yasuhiro; Prieto-da-Silva, Álvaro R. B.; Rádis-Baptista, Gandhi; Yamane, Tetsuo; Kikuchi, Jun

2014-01-01

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems. PMID:25330259
Biogeochemical typing of paddy field by a data-driven approach revealing sub-systems within a complex environment--a pipeline to filtrate, organize and frame massive dataset from multi-omics analyses.

PubMed

Ogawa, Diogo M O; Moriya, Shigeharu; Tsuboi, Yuuri; Date, Yasuhiro; Prieto-da-Silva, Álvaro R B; Rádis-Baptista, Gandhi; Yamane, Tetsuo; Kikuchi, Jun

2014-01-01

We propose the technique of biogeochemical typing (BGC typing) as a novel methodology to set forth the sub-systems of organismal communities associated to the correlated chemical profiles working within a larger complex environment. Given the intricate characteristic of both organismal and chemical consortia inherent to the nature, many environmental studies employ the holistic approach of multi-omics analyses undermining as much information as possible. Due to the massive amount of data produced applying multi-omics analyses, the results are hard to visualize and to process. The BGC typing analysis is a pipeline built using integrative statistical analysis that can treat such huge datasets filtering, organizing and framing the information based on the strength of the various mutual trends of the organismal and chemical fluctuations occurring simultaneously in the environment. To test our technique of BGC typing, we choose a rich environment abounding in chemical nutrients and organismal diversity: the surficial freshwater from Japanese paddy fields and surrounding waters. To identify the community consortia profile we employed metagenomics as high throughput sequencing (HTS) for the fragments amplified from Archaea rRNA, universal 16S rRNA and 18S rRNA; to assess the elemental content we employed ionomics by inductively coupled plasma optical emission spectroscopy (ICP-OES); and for the organic chemical profile, metabolomics employing both Fourier transformed infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (1H-NMR) all these analyses comprised our multi-omics dataset. The similar trends between the community consortia against the chemical profiles were connected through correlation. The result was then filtered, organized and framed according to correlation strengths and peculiarities. The output gave us four BGC types displaying uniqueness in community and chemical distribution, diversity and richness. We conclude therefore that the BGC typing is a successful technique for elucidating the sub-systems of organismal communities with associated chemical profiles in complex ecosystems.
Preparation of metagenomic libraries from naturally occurring marine viruses.

PubMed

Solonenko, Sergei A; Sullivan, Matthew B

2013-01-01

Microbes are now well recognized as major drivers of the biogeochemical cycling that fuels the Earth, and their viruses (phages) are known to be abundant and important in microbial mortality, horizontal gene transfer, and modulating microbial metabolic output. Investigation of environmental phages has been frustrated by an inability to culture the vast majority of naturally occurring diversity coupled with the lack of robust, quantitative, culture-independent methods for studying this uncultured majority. However, for double-stranded DNA phages, a quantitative viral metagenomic sample-to-sequence workflow now exists. Here, we review these advances with special emphasis on the technical details of preparing DNA sequencing libraries for metagenomic sequencing from environmentally relevant low-input DNA samples. Library preparation steps broadly involve manipulating the sample DNA by fragmentation, end repair and adaptor ligation, size fractionation, and amplification. One critical area of future research and development is parallel advances for alternate nucleic acid types such as single-stranded DNA and RNA viruses that are also abundant in nature. Combinations of recent advances in fragmentation (e.g., acoustic shearing and tagmentation), ligation reactions (adaptor-to-template ratio reference table availability), size fractionation (non-gel-sizing), and amplification (linear amplification for deep sequencing and linker amplification protocols) enhance our ability to generate quantitatively representative metagenomic datasets from low-input DNA samples. Such datasets are already providing new insights into the role of viruses in marine systems and will continue to do so as new environments are explored and synergies and paradigms emerge from large-scale comparative analyses. © 2013 Elsevier Inc. All rights reserved.

Association of tRNA methyltransferase NSUN2/IGF-II molecular signature with ovarian cancer survival.

PubMed

Yang, Jia-Cheng; Risch, Eric; Zhang, Meiqin; Huang, Chan; Huang, Huatian; Lu, Lingeng

2017-09-01

To investigate the association between NSUN2/IGF-II signature and ovarian cancer survival. Using a publicly accessible dataset of RNA sequencing and clinical follow-up data, we performed Classification and Regression Tree and survival analyses. Patients with NSUN2 high IGF-II low had significantly superior overall and disease progression-free survival, followed by NSUN2 low IGF-II low , NSUN2 high IGF-II high and NSUN2 low IGF-II high (p < 0.0001 for overall, p = 0.0024 for progression-free survival, respectively). The associations of NSUN2/IGF-II signature with the risks of death and relapse remained significant in multivariate Cox regression models. Random-effects meta-analyses show the upregulated NSUN2 and IGF-II expression in ovarian cancer versus normal tissues. The NSUN2/IGF-II signature associates with heterogeneous outcome and may have clinical implications in managing ovarian cancer.
DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

PubMed

Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

2018-01-01

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Molecular insights into Cassava brown streak virus susceptibility and resistance by profiling of the early host response.

PubMed

Anjanappa, Ravi B; Mehta, Devang; Okoniewski, Michal J; Szabelska-Berȩsewicz, Alicja; Gruissem, Wilhelm; Vanderschuren, Hervé

2018-02-01

Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) are responsible for significant cassava yield losses in eastern sub-Saharan Africa. To study the possible mechanisms of plant resistance to CBSVs, we inoculated CBSV-susceptible and CBSV-resistant cassava varieties with a mixed infection of CBSVs using top-cleft grafting. Transcriptome profiling of the two cassava varieties was performed at the earliest time point of full infection (28 days after grafting) in the susceptible scions. The expression of genes encoding proteins in RNA silencing, salicylic acid pathways and callose deposition was altered in the susceptible cassava variety, but transcriptional changes were limited in the resistant variety. In total, the expression of 585 genes was altered in the resistant variety and 1292 in the susceptible variety. Transcriptional changes led to the activation of β-1,3-glucanase enzymatic activity and a reduction in callose deposition in the susceptible cassava variety. Time course analysis also showed that CBSV replication in susceptible cassava induced a strong up-regulation of RDR1, a gene previously reported to be a susceptibility factor in other potyvirus-host pathosystems. The differences in the transcriptional responses to CBSV infection indicated that susceptibility involves the restriction of callose deposition at plasmodesmata. Aniline blue staining of callose deposits also indicated that the resistant variety displays a moderate, but significant, increase in callose deposition at the plasmodesmata. Transcriptome data suggested that resistance does not involve typical antiviral defence responses (i.e. RNA silencing and salicylic acid). A meta-analysis of the current RNA-sequencing (RNA-seq) dataset and selected potyvirus-host and virus-cassava RNA-seq datasets revealed that the conservation of the host response across pathosystems is restricted to genes involved in developmental processes. © 2017 THE AUTHORS. MOLECULAR PLANT PATHOLOGY PUBLISHED BY BRITISH SOCIETY FOR PLANT PATHOLOGY AND JOHN WILEY & SONS LTD.
A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

PubMed

Zhang, Haitao; Wu, Chenxue; Chen, Zewei; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.
A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PubMed Central

Wu, Chenxue; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules. PMID:28767687
Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides.

PubMed

Panwar, Bharat; Raghava, Gajendra P S

2015-04-01

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/). Copyright © 2015 Elsevier Inc. All rights reserved.
Integrated modeling of protein-coding genes in the Manduca sexta genome using RNA-Seq data from the biochemical model insect

PubMed Central

Cao, Xiaolong; Jiang, Haobo

2015-01-01

The genome sequence of Manduca sexta was recently determined using 454 technology. Cufflinks and MAKER2 were used to establish gene models in the genome assembly based on the RNA-Seq data and other species' sequences. Aided by the extensive RNA-Seq data from 50 tissue samples at various life stages, annotators over the world (including the present authors) have manually confirmed and improved a small percentage of the models after spending months of effort. While such collaborative efforts are highly commendable, many of the predicted genes still have problems which may hamper future research on this insect species. As a biochemical model representing lepidopteran pests, M. sexta has been used extensively to study insect physiological processes for over five decades. In this work, we assembled Manduca datasets Cufflinks 3.0, Trinity 4.0, and Oases 4.0 to assist the manual annotation efforts and development of Official Gene Set (OGS) 2.0. To further improve annotation quality, we developed methods to evaluate gene models in the MAKER2, Cufflinks, Oases and Trinity assemblies and selected the best ones to constitute MCOT 1.0 after thorough crosschecking. MCOT 1.0 has 18,089 genes encoding 31,666 proteins: 32.8% match OGS 2.0 models perfectly or near perfectly, 11,747 differ considerably, and 29.5% are absent in OGS 2.0. Future automation of this process is anticipated to greatly reduce human efforts in generating comprehensive, reliable models of structural genes in other genome projects where extensive RNA-Seq data are available. PMID:25612938
PASTA: splice junction identification from RNA-Sequencing data

PubMed Central

2013-01-01

Background Next generation transcriptome sequencing (RNA-Seq) is emerging as a powerful experimental tool for the study of alternative splicing and its regulation, but requires ad-hoc analysis methods and tools. PASTA (Patterned Alignments for Splicing and Transcriptome Analysis) is a splice junction detection algorithm specifically designed for RNA-Seq data, relying on a highly accurate alignment strategy and on a combination of heuristic and statistical methods to identify exon-intron junctions with high accuracy. Results Comparisons against TopHat and other splice junction prediction software on real and simulated datasets show that PASTA exhibits high specificity and sensitivity, especially at lower coverage levels. Moreover, PASTA is highly configurable and flexible, and can therefore be applied in a wide range of analysis scenarios: it is able to handle both single-end and paired-end reads, it does not rely on the presence of canonical splicing signals, and it uses organism-specific regression models to accurately identify junctions. Conclusions PASTA is a highly efficient and sensitive tool to identify splicing junctions from RNA-Seq data. Compared to similar programs, it has the ability to identify a higher number of real splicing junctions, and provides highly annotated output files containing detailed information about their location and characteristics. Accurate junction data in turn facilitates the reconstruction of the splicing isoforms and the analysis of their expression levels, which will be performed by the remaining modules of the PASTA pipeline, still under development. Use of PASTA can therefore enable the large-scale investigation of transcription and alternative splicing. PMID:23557086
Transcriptomic data of pre-meiotic stage of floret development in apomictic and sexual types of guinea grass (Panicum maximum Jacq.).

PubMed

Radhakrishna, Auji; Dwivedi, Krishna Kumar; Srivastava, Manoj Kumar; Roy, A K; Malaviya, D R; Kaushal, P

2018-06-01

Guinea grass ( Panicum maximum Jacq), an important fodder crop of humid and sub-humid tropical regions, reproduces through apomixis, a method of clonal propagation through seeds. Lack of knowledge of the genetic and molecular control of this phenomena has hindered the genetic improvement of this crop. The dataset provided here represents the first RNA-Seq based assembly and analysis of florets at pre-meiotic stage from the apomictic and sexual genotypes of guinea grass. The raw sequence files in FASTQ format were deposited in the NCBI SRA database with accession number SRP115883. A total of 24.8 Gb raw sequence data, corresponding to 17,96,65,827 raw reads was obtained by paired end sequencing. We used Trinity for de-novo assembly and identified 57,647 transcripts in sexual and 49,093 transcripts in apomictic type. This transcriptome data will be useful for identification and comparative analysis of genes regulating the mode of reproduction in grasses.
Identification of Human Lineage-Specific Transcriptional Coregulators Enabled by a Glossary of Binding Modules and Tunable Genomic Backgrounds.

PubMed

Mariani, Luca; Weinand, Kathryn; Vedenko, Anastasia; Barrera, Luis A; Bulyk, Martha L

2017-09-27

Transcription factors (TFs) control cellular processes by binding specific DNA motifs to modulate gene expression. Motif enrichment analysis of regulatory regions can identify direct and indirect TF binding sites. Here, we created a glossary of 108 non-redundant TF-8mer "modules" of shared specificity for 671 metazoan TFs from publicly available and new universal protein binding microarray data. Analysis of 239 ENCODE TF chromatin immunoprecipitation sequencing datasets and associated RNA sequencing profiles suggest the 8mer modules are more precise than position weight matrices in identifying indirect binding motifs and their associated tethering TFs. We also developed GENRE (genomically equivalent negative regions), a tunable tool for construction of matched genomic background sequences for analysis of regulatory regions. GENRE outperformed four state-of-the-art approaches to background sequence construction. We used our TF-8mer glossary and GENRE in the analysis of the indirect binding motifs for the co-occurrence of tethering factors, suggesting novel TF-TF interactions. We anticipate that these tools will aid in elucidating tissue-specific gene-regulatory programs. Copyright © 2017 Elsevier Inc. All rights reserved.
NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data

PubMed Central

2013-01-01

Background RNA-Seq technology has been used widely in transcriptome study, and one of the most important applications is to estimate the expression level of genes and their alternative splicing isoforms. There have been several algorithms published to estimate the expression based on different models. Recently Wu et al. published a method that can accurately estimate isoform level expression by considering position-related sequencing biases using nonparametric models. The method has advantages in handling different read distributions, but there hasn’t been an efficient program to implement this algorithm. Results We developed an efficient implementation of the algorithm in the program NURD. It uses a binary interval search algorithm. The program can correct both the global tendency of sequencing bias in the data and local sequencing bias specific to each gene. The correction makes the isoform expression estimation more reliable under various read distributions. And the implementation is computationally efficient in both the memory cost and running time and can be readily scaled up for huge datasets. Conclusion NURD is an efficient and reliable tool for estimating the isoform expression level. Given the reads mapping result and gene annotation file, NURD will output the expression estimation result. The package is freely available for academic use at http://bioinfo.au.tsinghua.edu.cn/software/NURD/. PMID:23837734
rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data.

PubMed

Shen, Shihao; Park, Juw Won; Lu, Zhi-xiang; Lin, Lan; Henry, Michael D; Wu, Ying Nian; Zhou, Qing; Xing, Yi

2014-12-23

Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.
Proposal of Vespertiliibacter pulmonis gen. nov., sp. nov. and two genomospecies as new members of the family Pasteurellaceae isolated from European bats.

PubMed

Mühldorfer, Kristin; Speck, Stephanie; Wibbelt, Gudrun

2014-07-01

Five bacterial strains isolated from bats of the family Vespertilionidae were characterized by phenotypic tests and multilocus sequence analysis (MLSA) using the 16S rRNA gene and four housekeeping genes (rpoA, rpoB, infB, recN). Phylogenetic analyses of individual and combined datasets indicated that the five strains represent a monophyletic cluster within the family Pasteurellaceae. Comparison of 16S rRNA gene sequences demonstrated a high degree of similarity (98.3-99.9%) among the group of bat-derived strains, while searches in nucleotide databases indicated less than 96% sequence similarity to known members of the Pasteurellaceae. The housekeeping genes rpoA, rpoB, infB and recN provided higher resolution compared with the 16S rRNA gene and subdivided the group according to the bat species from which the strains were isolated. Three strains derived from noctule bats shared 98.6-100% sequence similarity in all four genes investigated, whereas, based on rpoB, infB and recN gene sequences, 91.8-96% similarity was observed with and between the remaining two strains isolated from a serotine bat and a pipistrelle bat, respectively. Genome relatedness as deduced from recN gene sequences correlated well with the results of MLSA and indicated that the five strains represent a new genus. Based on these results, it is proposed to classify the five strains derived from bats within Vespertiliibacter pulmonis gen. nov., sp. nov. (the type species), Vespertiliibacter genomospecies 1 and Vespertiliibacter genomospecies 2. The genus can be distinguished phenotypically from recognized genera of the Pasteurellaceae by at least three characteristics. All strains are nutritionally fastidious and require a chemically defined supplement with NAD for growth. The DNA G+C content of strain E127/08(T) is 38.2 mol%. The type strain of Vespertiliibacter pulmonis gen. nov., sp. nov. is E127/08(T) ( = CCUG 64585(T) = DSM 27238(T)). The reference strains of Vespertiliibacter genomospecies 1 and 2 are E145/08 and E157/08, respectively. © 2014 IUMS.
Transcriptome Analysis of Houttuynia cordata Thunb. by Illumina Paired-End RNA Sequencing and SSR Marker Discovery

PubMed Central

Wei, Lin; Li, Shenghua; Liu, Shenggui; He, Anna; Wang, Dan; Wang, Jie; Tang, Yulian; Wu, Xianjin

2014-01-01

Background Houttuynia cordata Thunb. is an important traditional medical herb in China and other Asian countries, with high medicinal and economic value. However, a lack of available genomic information has become a limitation for research on this species. Thus, we carried out high-throughput transcriptomic sequencing of H. cordata to generate an enormous transcriptome sequence dataset for gene discovery and molecular marker development. Principal Findings Illumina paired-end sequencing technology produced over 56 million sequencing reads from H. cordata mRNA. Subsequent de novo assembly yielded 63,954 unigenes, 39,982 (62.52%) and 26,122 (40.84%) of which had significant similarity to proteins in the NCBI nonredundant protein and Swiss-Prot databases (E-value <10−5), respectively. Of these annotated unigenes, 30,131 and 15,363 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. In addition, 24,434 (38.21%) unigenes were mapped onto 128 pathways using the KEGG pathway database and 17,964 (44.93%) unigenes showed homology to Vitis vinifera (Vitaceae) genes in BLASTx analysis. Furthermore, 4,800 cDNA SSRs were identified as potential molecular markers. Fifty primer pairs were randomly selected to detect polymorphism among 30 samples of H. cordata; 43 (86%) produced fragments of expected size, suggesting that the unigenes were suitable for specific primer design and of high quality, and the SSR marker could be widely used in marker-assisted selection and molecular breeding of H. cordata in the future. Conclusions This is the first application of Illumina paired-end sequencing technology to investigate the whole transcriptome of H. cordata and to assemble RNA-seq reads without a reference genome. These data should help researchers investigating the evolution and biological processes of this species. The SSR markers developed can be used for construction of high-resolution genetic linkage maps and for gene-based association analyses in H. cordata. This work will enable future functional genomic research and research into the distinctive active constituents of this genus. PMID:24392108
Genomics dataset of unidentified disclosed isolates.

PubMed

Rekadwad, Bhagwan N

2016-09-01

Analysis of DNA sequences is necessary for higher hierarchical classification of the organisms. It gives clues about the characteristics of organisms and their taxonomic position. This dataset is chosen to find complexities in the unidentified DNA in the disclosed patents. A total of 17 unidentified DNA sequences were thoroughly analyzed. The quick response codes were generated. AT/GC content of the DNA sequences analysis was carried out. The QR is helpful for quick identification of isolates. AT/GC content is helpful for studying their stability at different temperatures. Additionally, a dataset on cleavage code and enzyme code studied under the restriction digestion study, which helpful for performing studies using short DNA sequences was reported. The dataset disclosed here is the new revelatory data for exploration of unique DNA sequences for evaluation, identification, comparison and analysis.
The maize stripe virus major noncapsid protein messenger RNA transcripts contain heterogeneous leader sequences at their 5' termini.

PubMed

Huiet, L; Feldstein, P A; Tsai, J H; Falk, B W

1993-12-01

Primer extension analyses and a PCR-based cloning strategy were used to identify and characterize 5' nucleotide sequences on the maize stripe virus (MStV) RNA4 mRNA transcripts encoding the major noncapsid protein (NCP). Direct RNA sequence analysis by primer extension showed that the NCP mRNA transcripts had 10-15 nucleotides beyond the 5' terminus of the MStV RNA4 nucleotide sequence. MStV genomic RNAs isolated from ribonucleoprotein particles (RNPs) lacked the additional 5' nucleotides. cDNA clones representing the 5' region of the mRNA transcripts were constructed, and the nucleotide sequences of the 5' regions were determined for 16 clones. Each was found to have a distinct 10-15 nucleotide sequence immediately 5' of the MStV RNA4 sequence. Eleven of 16 clones had the correct MStV RNA4 5' nucleotide sequence, while five showed minor variations at or near the 5' most MStV RNA4 nucleotide. These characteristics show strong similarities to other viral mRNA transcripts which are synthesized by cap snatching.
Profiling mRNAs of Two Cuscuta Species Reveals Possible Candidate Transcripts Shared by Parasitic Plants

PubMed Central

Wijeratne, Saranga; Fraga, Martina; Meulia, Tea; Doohan, Doug; Li, Zhaohu; Qu, Feng

2013-01-01

Dodders are among the most important parasitic plants that cause serious yield losses in crop plants. In this report, we sought to unveil the genetic basis of dodder parasitism by profiling the trancriptomes of Cuscuta pentagona and C. suaveolens, two of the most common dodder species using a next-generation RNA sequencing platform. De novo assembly of the sequence reads resulted in more than 46,000 isotigs and contigs (collectively referred to as expressed sequence tags or ESTs) for each species, with more than half of them predicted to encode proteins that share significant sequence similarities with known proteins of non-parasitic plants. Comparing our datasets with transcriptomes of 12 other fully sequenced plant species confirmed a close evolutionary relationship between dodder and tomato. Using a rigorous set of filtering parameters, we were able to identify seven pairs of ESTs that appear to be shared exclusively by parasitic plants, thus providing targets for tailored management approaches. In addition, we also discovered ESTs with sequences similarities to known plant viruses, including cryptic viruses, in the dodder sequence assemblies. Together this study represents the first comprehensive transcriptome profiling of parasitic plants in the Cuscuta genus, and is expected to contribute to our understanding of the molecular mechanisms of parasitic plant-host plant interactions. PMID:24312295
Profiling mRNAs of two Cuscuta species reveals possible candidate transcripts shared by parasitic plants.

PubMed

Jiang, Linjian; Wijeratne, Asela J; Wijeratne, Saranga; Fraga, Martina; Meulia, Tea; Doohan, Doug; Li, Zhaohu; Qu, Feng

2013-01-01

Dodders are among the most important parasitic plants that cause serious yield losses in crop plants. In this report, we sought to unveil the genetic basis of dodder parasitism by profiling the trancriptomes of Cuscuta pentagona and C. suaveolens, two of the most common dodder species using a next-generation RNA sequencing platform. De novo assembly of the sequence reads resulted in more than 46,000 isotigs and contigs (collectively referred to as expressed sequence tags or ESTs) for each species, with more than half of them predicted to encode proteins that share significant sequence similarities with known proteins of non-parasitic plants. Comparing our datasets with transcriptomes of 12 other fully sequenced plant species confirmed a close evolutionary relationship between dodder and tomato. Using a rigorous set of filtering parameters, we were able to identify seven pairs of ESTs that appear to be shared exclusively by parasitic plants, thus providing targets for tailored management approaches. In addition, we also discovered ESTs with sequences similarities to known plant viruses, including cryptic viruses, in the dodder sequence assemblies. Together this study represents the first comprehensive transcriptome profiling of parasitic plants in the Cuscuta genus, and is expected to contribute to our understanding of the molecular mechanisms of parasitic plant-host plant interactions.
Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

PubMed

Pan, Yuliang; Wang, Zixiang; Zhan, Weihua; Deng, Lei

2018-05-01

Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. The PrabHot webserver is freely available at http://denglab.org/PrabHot/. leideng@csu.edu.cn. Supplementary data are available at Bioinformatics online.
The conserved CAAGAAAGA spacer sequence is an essential element for the formation of 3' termini of the sea urchin H3 histone mRNA by RNA processing.

PubMed Central

Georgiev, O; Birnstiel, M L

1985-01-01

Analysis of cDNA sequences obtained from the small nuclear RNA U7 has previously suggested specific contacts, by base pairing, between the conserved stem-loop structure and CAAGAAAGA sequence of the histone pre-mRNA and the 5'-terminal sequence of the U7 RNA during RNA processing. In order to test some aspects of the model we have created a series of linker scan, deletion and insertion mutants of the 3' terminus of a sea urchin H3 histone gene and have injected mutant DNAs or in vitro synthesized precursors into frog oocyte nuclei for interpretation. We find that, in addition to the stem-loop structure of the mRNA, the CAAGAAAGA spacer transcript within the histone pre-mRNA is required absolutely for RNA processing, as predicted from our model. Spacer sequences immediately downstream of the CAAGAAAGA motif are not complementary to U7 RNA. Nevertheless, they are necessary for obtaining a maximal rate of RNA processing, as is the ACCA sequence coding for the 3' terminus of the mature mRNA. An increase of distance between the mRNA palindrome and the CAAGAAAGA by as little as six nucleotides abolishes all processing. It may, therefore, be useful to regard both these sequence motifs as part of one and the same RNA processing signal with narrowly defined topologies. Interestingly, U7 RNA-dependent 3' processing of histone pre-mRNA can occur in RNA injection experiments only when the in vitro synthesized pre-mRNA contains sequence extensions well beyond the region of sequence complementarities to the U7 RNA. In addition to directing 3' processing the terminal mRNA sequences may have a role in histone mRNA stabilization in the cytoplasmic compartment. Images Fig. 3. Fig. 4. Fig. 5. Fig. 6. Fig. 7. PMID:2410259

Phylogenetic diversity of Pasteurellaceae and horizontal gene transfer of leukotoxin in wild and domestic sheep.

PubMed

Kelley, Scott T; Cassirer, E Frances; Weiser, Glen C; Safaee, Shirin

2007-01-01

Wild and domestic animal populations are known to be sources and reservoirs of emerging diseases. There is also a growing recognition that horizontal genetic transfer (HGT) plays an important role in bacterial pathogenesis. We used molecular phylogenetic methods to assess diversity and cross-transmission rates of Pasteurellaceae bacteria in populations of bighorn sheep, Dall's sheep, domestic sheep and domestic goats. Members of the Pasteurellaceae cause an array of deadly illnesses including bacterial pneumonia known as "pasteurellosis", a particularly devastating disease for bighorn sheep. A phylogenetic analysis of a combined dataset of two RNA genes (16S ribosomal RNA and RNAse P RNA) revealed remarkable evolutionary diversity among Pasteurella trehalosi and Mannheimia (Pasteurella) haemolytica bacteria isolated from sheep and goats. Several phylotypes appeared to associate with particular host species, though we found numerous instances of apparent cross-transmission among species and populations. Statistical analyses revealed that host species, geographic locale and biovariant classification, but not virulence, correlated strongly with Pasteurellaceae phylogeny. Sheep host species correlated with P. trehalosi isolates phylogeny (PTP test; P=0.002), but not with the phylogeny of M. haemolytica isolates, suggesting that P. trehalosi bacteria may be more host specific. With regards to populations within species, we also discovered a strong correlation between geographic locale and isolate phylogeny in the Rocky Mountain bighorn sheep (PTP test; P=0.001). We also investigated the potential for HGT of the leukotoxin A (lktA) gene, which produces a toxin that plays an integral role in causing disease. Comparative analysis of the combined RNA gene phylogeny and the lktA phylogenies revealed considerable incongruence between the phylogenies, suggestive of HGT. Furthermore, we found identical lktA alleles in unrelated bacterial species, some of which had been isolated from sheep in distantly removed populations. For example, lktA sequences from P. trehalosi isolated from remote Alaskan Dall's sheep were 100% identical over a 900-nucleotide stretch to sequences determined from M. haemolytica isolated from domestic sheep in the UK. This extremely high degree of sequence similarity of lktA sequences among distinct bacterial species suggests that HGT has played a role in the evolution of lktA in wild hosts.
Comparison of ribosomal RNA removal methods for transcriptome sequencing workflows in teleost fish

USDA-ARS?s Scientific Manuscript database

RNA sequencing (RNA-Seq) is becoming the standard for transcriptome analysis. Removal of contaminating ribosomal RNA (rRNA) is a priority in the preparation of libraries suitable for sequencing. rRNAs are commonly removed from total RNA via either mRNA selection or rRNA depletion. These methods have...
Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate.

PubMed

Liu, Xuejun; Shi, Xinxin; Chen, Chunlin; Zhang, Li

2015-10-16

The high-throughput sequencing technology, RNA-Seq, has been widely used to quantify gene and isoform expression in the study of transcriptome in recent years. Accurate expression measurement from the millions or billions of short generated reads is obstructed by difficulties. One is ambiguous mapping of reads to reference transcriptome caused by alternative splicing. This increases the uncertainty in estimating isoform expression. The other is non-uniformity of read distribution along the reference transcriptome due to positional, sequencing, mappability and other undiscovered sources of biases. This violates the uniform assumption of read distribution for many expression calculation approaches, such as the direct RPKM calculation and Poisson-based models. Many methods have been proposed to address these difficulties. Some approaches employ latent variable models to discover the underlying pattern of read sequencing. However, most of these methods make bias correction based on surrounding sequence contents and share the bias models by all genes. They therefore cannot estimate gene- and isoform-specific biases as revealed by recent studies. We propose a latent variable model, NLDMseq, to estimate gene and isoform expression. Our method adopts latent variables to model the unknown isoforms, from which reads originate, and the underlying percentage of multiple spliced variants. The isoform- and exon-specific read sequencing biases are modeled to account for the non-uniformity of read distribution, and are identified by utilizing the replicate information of multiple lanes of a single library run. We employ simulation and real data to verify the performance of our method in terms of accuracy in the calculation of gene and isoform expression. Results show that NLDMseq obtains competitive gene and isoform expression compared to popular alternatives. Finally, the proposed method is applied to the detection of differential expression (DE) to show its usefulness in the downstream analysis. The proposed NLDMseq method provides an approach to accurately estimate gene and isoform expression from RNA-Seq data by modeling the isoform- and exon-specific read sequencing biases. It makes use of a latent variable model to discover the hidden pattern of read sequencing. We have shown that it works well in both simulations and real datasets, and has competitive performance compared to popular methods. The method has been implemented as a freely available software which can be found at https://github.com/PUGEA/NLDMseq.
A reference human genome dataset of the BGISEQ-500 sequencer.

PubMed

Huang, Jie; Liang, Xinming; Xuan, Yuankai; Geng, Chunyu; Li, Yuxiang; Lu, Haorong; Qu, Shoufang; Mei, Xianglin; Chen, Hongbo; Yu, Ting; Sun, Nan; Rao, Junhua; Wang, Jiahao; Zhang, Wenwei; Chen, Ying; Liao, Sha; Jiang, Hui; Liu, Xin; Yang, Zhaopeng; Mu, Feng; Gao, Shangxian

2017-05-01

BGISEQ-500 is a new desktop sequencer developed by BGI. Using DNA nanoball and combinational probe anchor synthesis developed from Complete Genomics™ sequencing technologies, it generates short reads at a large scale. Here, we present the first human whole-genome sequencing dataset of BGISEQ-500. The dataset was generated by sequencing the widely used cell line HG001 (NA12878) in two sequencing runs of paired-end 50 bp (PE50) and two sequencing runs of paired-end 100 bp (PE100). We also include examples of the raw images from the sequencer for reference. Finally, we identified variations using this dataset, estimated the accuracy of the variations, and compared to that of the variations identified from similar amounts of publicly available HiSeq2500 data. We found similar single nucleotide polymorphism (SNP) detection accuracy for the BGISEQ-500 PE100 data (false positive rate [FPR] = 0.00020%, sensitivity = 96.20%) compared to the PE150 HiSeq2500 data (FPR = 0.00017%, sensitivity = 96.60%) better SNP detection accuracy than the PE50 data (FPR = 0.0006%, sensitivity = 94.15%). But for insertions and deletions (indels), we found lower accuracy for BGISEQ-500 data (FPR = 0.00069% and 0.00067% for PE100 and PE50 respectively, sensitivity = 88.52% and 70.93%) than the HiSeq2500 data (FPR = 0.00032%, sensitivity = 96.28%). Our dataset can serve as the reference dataset, providing basic information not just for future development, but also for all research and applications based on the new sequencing platform. © The Authors 2017. Published by Oxford University Press.
Phylogeny of the túngara frog genus Engystomops (= Physalaemus pustulosus species group; Anura: Leptodactylidae).

PubMed

Ron, Santiago R; Santos, Juan C; Cannatella, David C

2006-05-01

We present a phylogeny of the Neotropical genus Engystomops (= Physalaemus pustulosus species group) based on sequences of approximately 2.4 kb of mtDNA, (12S rRNA, valine-tRNA, and 16S rRNA) and propose a phylogenetic nomenclature. The phylogeny includes all described taxa and two unnamed species. All analyses indicate that Engystomops is monophyletic and contains two basal allopatric clades. Clade I (Edentulus) includes E. pustulosus and the Amazonian E. petersi + E. cf. freibergi. Clade II (Duovox) includes all species distributed in W Ecuador and NW Peru. Brevivox, a clade of small-sized species is strongly supported within Duovox. Populations of Engystomops pustulosus fall into two well-supported clades, each of which occupies two disjunct portions of the species range. Overall, our phylogeny is congruent with most previous hypotheses. This study is among the few published species-level phylogenies of Neotropical amphibians derived from molecular datasets. A review of the proportion of new species detected by similar studies suggests that the increasing use of molecular techniques will lead to the discovery of a vast number of species of Neotropical amphibians.
Enhancing the Resolution of Rumen Microbial Classification from Metatranscriptomic Data Using Kraken and Mothur

PubMed Central

Neves, Andre L. A.; Li, Fuyong; Ghoshal, Bibaswan; McAllister, Tim; Guan, Le L.

2017-01-01

The advent of next generation sequencing and bioinformatics tools have greatly advanced our knowledge about the phylogenetic diversity and ecological role of microbes inhabiting the mammalian gut. However, there is a lack of information on the evaluation of these computational tools in the context of the rumen microbiome as these programs have mostly been benchmarked on real or simulated datasets generated from human studies. In this study, we compared the outcomes of two methods, Kraken (mRNA based) and a pipeline developed in-house based on Mothur (16S rRNA based), to assess the taxonomic profiles (bacteria and archaea) of rumen microbial communities using total RNA sequencing of rumen fluid collected from 12 cattle with differing feed conversion ratios (FCR). Both approaches revealed a similar phyla distribution of the most abundant taxa, with Bacteroidetes, Firmicutes, and Proteobacteria accounting for approximately 80% of total bacterial abundance. For bacterial taxa, although 69 genera were commonly detected by both methods, an additional 159 genera were exclusively identified by Kraken. Kraken detected 423 species, while Mothur was not able to assign bacterial sequences to the species level. For archaea, both methods generated similar results only for the abundance of Methanomassiliicoccaceae (previously referred as RCC), which comprised more than 65% of the total archaeal families. Taxon R4-41B was exclusively identified by Mothur in the rumen of feed efficient bulls, whereas Kraken uniquely identified Methanococcaceae in inefficient bulls. Although Kraken enhanced the microbial classification at the species level, identification of bacteria or archaea in the rumen is limited due to a lack of reference genomes for the rumen microbiome. The findings from this study suggest that the development of the combined pipelines using Mothur and Kraken is needed for a more inclusive and representative classification of microbiomes. PMID:29270165
Improved annotation with de novo transcriptome assembly in four social amoeba species.

PubMed

Singh, Reema; Lawal, Hajara M; Schilde, Christina; Glöckner, Gernot; Barton, Geoffrey J; Schaap, Pauline; Cole, Christian

2017-01-31

Annotation of gene models and transcripts is a fundamental step in genome sequencing projects. Often this is performed with automated prediction pipelines, which can miss complex and atypical genes or transcripts. RNA sequencing (RNA-seq) data can aid the annotation with empirical data. Here we present de novo transcriptome assemblies generated from RNA-seq data in four Dictyostelid species: D. discoideum, P. pallidum, D. fasciculatum and D. lacteum. The assemblies were incorporated with existing gene models to determine corrections and improvement on a whole-genome scale. This is the first time this has been performed in these eukaryotic species. An initial de novo transcriptome assembly was generated by Trinity for each species and then refined with Program to Assemble Spliced Alignments (PASA). The completeness and quality were assessed with the Benchmarking Universal Single-Copy Orthologs (BUSCO) and Transrate tools at each stage of the assemblies. The final datasets of 11,315-12,849 transcripts contained 5,610-7,712 updates and corrections to >50% of existing gene models including changes to hundreds or thousands of protein products. Putative novel genes are also identified and alternative splice isoforms were observed for the first time in P. pallidum, D. lacteum and D. fasciculatum. In taking a whole transcriptome approach to genome annotation with empirical data we have been able to enrich the annotations of four existing genome sequencing projects. In doing so we have identified updates to the majority of the gene annotations across all four species under study and found putative novel genes and transcripts which could be worthy for follow-up. The new transcriptome data we present here will be a valuable resource for genome curators in the Dictyostelia and we propose this effective methodology for use in other genome annotation projects.
Genome-wide transcription start site profiling in biofilm-grown Burkholderia cenocepacia J2315.

PubMed

Sass, Andrea M; Van Acker, Heleen; Förstner, Konrad U; Van Nieuwerburgh, Filip; Deforce, Dieter; Vogel, Jörg; Coenye, Tom

2015-10-13

Burkholderia cenocepacia is a soil-dwelling Gram-negative Betaproteobacterium with an important role as opportunistic pathogen in humans. Infections with B. cenocepacia are very difficult to treat due to their high intrinsic resistance to most antibiotics. Biofilm formation further adds to their antibiotic resistance. B. cenocepacia harbours a large, multi-replicon genome with a high GC-content, the reference genome of strain J2315 includes 7374 annotated genes. This study aims to annotate transcription start sites and identify novel transcripts on a whole genome scale. RNA extracted from B. cenocepacia J2315 biofilms was analysed by differential RNA-sequencing and the resulting dataset compared to data derived from conventional, global RNA-sequencing. Transcription start sites were annotated and further analysed according to their position relative to annotated genes. Four thousand ten transcription start sites were mapped over the whole B. cenocepacia genome and the primary transcription start site of 2089 genes expressed in B. cenocepacia biofilms were defined. For 64 genes a start codon alternative to the annotated one was proposed. Substantial antisense transcription for 105 genes and two novel protein coding sequences were identified. The distribution of internal transcription start sites can be used to identify genomic islands in B. cenocepacia. A potassium pump strongly induced only under biofilm conditions was found and 15 non-coding small RNAs highly expressed in biofilms were discovered. Mapping transcription start sites across the B. cenocepacia genome added relevant information to the J2315 annotation. Genes and novel regulatory RNAs putatively involved in B. cenocepacia biofilm formation were identified. These findings will help in understanding regulation of B. cenocepacia biofilm formation.
The suppression of tomato defence response genes upon potato cyst nematode infection indicates a key regulatory role of miRNAs.

PubMed

Święcicka, Magdalena; Skowron, Waldemar; Cieszyński, Piotr; Dąbrowska-Bronk, Joanna; Matuszkiewicz, Mateusz; Filipecki, Marcin; Koter, Marek Daniel

2017-04-01

Potato cyst nematode Globodera rostochiensis is an obligate parasite of solanaceous plants, triggering metabolic and morphological changes in roots which may result in substantial crop yield losses. Previously, we used the cDNA-AFLP to study the transcriptional dynamics in nematode infected tomato roots. Now, we present the rescreening of already published, upregulated transcript-derived fragment dataset using the most current tomato transcriptome sequences. Our reanalysis allowed to add 54 novel genes to 135, already found as upregulated in tomato roots upon G. rostochiensis infection (in total - 189). We also created completely new catalogue of downregulated sequences leading to the discovery of 76 novel genes. Functional classification of candidates showed that the 'wound, stress and defence response' category was enriched in the downregulated genes. We confirmed the transcriptional dynamics of six genes by qRT-PCR. To place our results in a broader context, we compared the tomato data with Arabidopsis thaliana, revealing similar proportions of upregulated and downregulated genes as well as similar enrichment of defence related transcripts in the downregulated group. Since transcript suppression is quite common in plant-nematode interactions, we assessed the possibility of miRNA-mediated inverse correlation on several tomato sequences belonging to NB-LRR and receptor-like kinase families. The qRT-PCR of miRNAs and putative target transcripts showed an opposite expression pattern in 9 cases. These results together with in silico analyses of potential miRNA targeting to the full repertoire of tomato R-genes show that miRNA mediated gene suppression may be a key regulatory mechanism during nematode parasitism. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
miRge - A Multiplexed Method of Processing Small RNA-Seq Data to Determine MicroRNA Entropy

PubMed Central

Myers, Jason R.; Gupta, Simone; Weng, Lien-Chun; Ashton, John M.; Cornish, Toby C.; Pandey, Akhilesh; Halushka, Marc K.

2015-01-01

Small RNA RNA-seq for microRNAs (miRNAs) is a rapidly developing field where opportunities still exist to create better bioinformatics tools to process these large datasets and generate new, useful analyses. We built miRge to be a fast, smart small RNA-seq solution to process samples in a highly multiplexed fashion. miRge employs a Bayesian alignment approach, whereby reads are sequentially aligned against customized mature miRNA, hairpin miRNA, noncoding RNA and mRNA sequence libraries. miRNAs are summarized at the level of raw reads in addition to reads per million (RPM). Reads for all other RNA species (tRNA, rRNA, snoRNA, mRNA) are provided, which is useful for identifying potential contaminants and optimizing small RNA purification strategies. miRge was designed to optimally identify miRNA isomiRs and employs an entropy based statistical measurement to identify differential production of isomiRs. This allowed us to identify decreasing entropy in isomiRs as stem cells mature into retinal pigment epithelial cells. Conversely, we show that pancreatic tumor miRNAs have similar entropy to matched normal pancreatic tissues. In a head-to-head comparison with other miRNA analysis tools (miRExpress 2.0, sRNAbench, omiRAs, miRDeep2, Chimira, UEA small RNA Workbench), miRge was faster (4 to 32-fold) and was among the top-two methods in maximally aligning miRNAs reads per sample. Moreover, miRge has no inherent limits to its multiplexing. miRge was capable of simultaneously analyzing 100 small RNA-Seq samples in 52 minutes, providing an integrated analysis of miRNA expression across all samples. As miRge was designed for analysis of single as well as multiple samples, miRge is an ideal tool for high and low-throughput users. miRge is freely available at http://atlas.pathology.jhu.edu/baras/miRge.html. PMID:26571139
DAMe: a toolkit for the initial processing of datasets with PCR replicates of double-tagged amplicons for DNA metabarcoding analyses.

PubMed

Zepeda-Mendoza, Marie Lisandra; Bohmann, Kristine; Carmona Baez, Aldo; Gilbert, M Thomas P

2016-05-03

DNA metabarcoding is an approach for identifying multiple taxa in an environmental sample using specific genetic loci and taxa-specific primers. When combined with high-throughput sequencing it enables the taxonomic characterization of large numbers of samples in a relatively time- and cost-efficient manner. One recent laboratory development is the addition of 5'-nucleotide tags to both primers producing double-tagged amplicons and the use of multiple PCR replicates to filter erroneous sequences. However, there is currently no available toolkit for the straightforward analysis of datasets produced in this way. We present DAMe, a toolkit for the processing of datasets generated by double-tagged amplicons from multiple PCR replicates derived from an unlimited number of samples. Specifically, DAMe can be used to (i) sort amplicons by tag combination, (ii) evaluate PCR replicates dissimilarity, and (iii) filter sequences derived from sequencing/PCR errors, chimeras, and contamination. This is attained by calculating the following parameters: (i) sequence content similarity between the PCR replicates from each sample, (ii) reproducibility of each unique sequence across the PCR replicates, and (iii) copy number of the unique sequences in each PCR replicate. We showcase the insights that can be obtained using DAMe prior to taxonomic assignment, by applying it to two real datasets that vary in their complexity regarding number of samples, sequencing libraries, PCR replicates, and used tag combinations. Finally, we use a third mock dataset to demonstrate the impact and importance of filtering the sequences with DAMe. DAMe allows the user-friendly manipulation of amplicons derived from multiple samples with PCR replicates built in a single or multiple sequencing libraries. It allows the user to: (i) collapse amplicons into unique sequences and sort them by tag combination while retaining the sample identifier and copy number information, (ii) identify sequences carrying unused tag combinations, (iii) evaluate the comparability of PCR replicates of the same sample, and (iv) filter tagged amplicons from a number of PCR replicates using parameters of minimum length, copy number, and reproducibility across the PCR replicates. This enables an efficient analysis of complex datasets, and ultimately increases the ease of handling datasets from large-scale studies.
Small RNAome profiling from human skeletal muscle: novel miRNAs and their targets associated with cancer cachexia.

PubMed

Narasimhan, Ashok; Ghosh, Sunita; Stretch, Cynthia; Greiner, Russell; Bathe, Oliver F; Baracos, Vickie; Damaraju, Sambasivarao

2017-06-01

MicroRNAs (miRs) are small non-coding RNAs that regulate gene (mRNA) expression. Although the pathological role of miRs have been studied in muscle wasting conditions such as myotonic and muscular dystrophy, their roles in cancer cachexia (CC) are still emerging. The objectives are (i) to profile human skeletal muscle expressed miRs; (ii) to identify differentially expressed (DE) miRs between cachectic and non-cachectic cancer patients; (iii) to identify mRNA targets for the DE miRs to gain mechanistic insights; and (iv) to investigate if miRs show potential prognostic and predictive value. Study subjects were classified based on the international consensus diagnostic criteria for CC. Forty-two cancer patients were included, of which 22 were cachectic cases and 20 were non-cachectic cancer controls. Total RNA isolated from muscle biopsies were subjected to next-generation sequencing. A total of 777 miRs were profiled, and 82 miRs with read counts of ≥5 in 80% of samples were retained for analysis. We identified eight DE miRs (up-regulated, fold change of ≥1.4 at P < 0.05). A total of 191 potential mRNA targets were identified for the DE miRs using previously described human skeletal muscle mRNA expression data (n = 90), and a majority of them were also confirmed in an independent mRNA transcriptome dataset. Ingenuity pathway analysis identified pathways related to myogenesis and inflammation. qRT-PCR analysis of representative miRs showed similar direction of effect (P < 0.05), as observed in next-generation sequencing. The identified miRs also showed prognostic and predictive value. In all, we identified eight novel miRs associated with CC. © 2017 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of the Society on Sarcopenia, Cachexia and Wasting Disorders.
The nucleotide sequence of the intergenic region between the 5.8S and 26S rRNA genes of the yeast ribosomal RNA operon. Possible implications for the interaction between 5.8S and 26S rRNA and the processing of the primary transcript.

PubMed Central

Veldman, G M; Klootwijk, J; van Heerikhuizen, H; Planta, R J

1981-01-01

We have determined the nucleotide sequence of part of a cloned yeast ribosomal RNA operon extending from the 5.8S RNA gene downstream into the 5' -terminal region of the 26S RNA gene. We mapped the pertinent processing sites, viz. the 5' end of 26S rRNA and the 3'ends of 5.8S rRNA and its immediate precursor, 7S RNA. At the 3' end of 7S RNA we find the sequence UCGUUU which is very similar to the type I consensus sequence UCAUUA/U present at the 3' ends of 17S, 5.8S and 26S rRNA as well as 18S precursor rRNA in yeast. At the 5' end of the 26S RNA gene we find a sequence of thirteen nucleotides which is homologous to the type II sequence present at the 5' termini of both the 17S and the 5.8S RNA gene. These findings further support the suggestion put forward earlier (G.M. Veldman et al. (1980) Nucl. Acids Res. 8, 2907-2920) that both consensus sequences are involved in the recognition of precursor rRNA by the processing nuclease(s). We discuss a model for the processing of yeast rRNA in which a processing enzyme sequentially recognizes several combinations of a type I and a type II consensus sequence. We also describe the existence of a significant base complementarity between sequences in the 5' -terminal region of 26S rRNA and the 3' -terminal region of 5.8S rRNA. We suggest that base pairing between these sequences contributes to the binding between 5.8S and 26S rRNA. Images PMID:7312619
Terminator oligo blocking efficiently eliminates rRNA from Drosophila small RNA sequencing libraries.

PubMed

Wickersheim, Michelle L; Blumenstiel, Justin P

2013-11-01

A large number of methods are available to deplete ribosomal RNA reads from high-throughput RNA sequencing experiments. Such methods are critical for sequencing Drosophila small RNAs between 20 and 30 nucleotides because size selection is not typically sufficient to exclude the highly abundant class of 30 nucleotide 2S rRNA. Here we demonstrate that pre-annealing terminator oligos complimentary to Drosophila 2S rRNA prior to 5' adapter ligation and reverse transcription efficiently depletes 2S rRNA sequences from the sequencing reaction in a simple and inexpensive way. This depletion is highly specific and is achieved with minimal perturbation of miRNA and piRNA profiles.
Diverse Bacterial Groups Contribute to the Alkane Degradation Potential of Chronically Polluted Subantarctic Coastal Sediments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guibert, Lilian M.; Loviso, Claudia L.; Borglin, Sharon

We aimed to gain insight into the alkane degradation potential of microbial communities from chronically polluted sediments of a subantarctic coastal environment using a combination of metagenomic approaches. A total of 6178 sequences annotated as alkane-1-monooxygenases (EC 1.14.15.3) were retrieved from a shotgun metagenomic dataset that included two sites analyzed in triplicate. The majority of the sequences binned with AlkB described in Bacteroidetes (32 ± 13 %) or Proteobacteria (29 ± 7 %), although a large proportion remained unclassified at the phylum level. Operational taxonomic unit (OTU)-based analyses showed small differences in AlkB distribution among samples that could be correlatedmore » with alkane concentrations, as well as with site-specific variations in pH and salinity. A number of low-abundance OTUs, mostly affiliated with Actinobacterial sequences, were found to be only present in the most contaminated samples. On the other hand, the molecular screening of a large-insert metagenomic library of intertidal sediments from one of the sampling sites identified two genomic fragments containing novel alkB gene sequences, as well as various contiguous genes related to lipid metabolism. Both genomic fragments were affiliated with the phylum Planctomycetes, and one could be further assigned to the genus Rhodopirellula due to the presence of a partial sequence of the 23S ribosomal RNA (rRNA) gene. This work highlights the diversity of bacterial groups contributing to the alkane degradation potential and reveals patterns of functional diversity in relation with environmental stressors in a chronically polluted, high-latitude coastal environment. In addition, alkane biodegradation genes are described for the first time in members of Planctomycetes.« less
High Genetic Diversity and Novelty in Eukaryotic Plankton Assemblages Inhabiting Saline Lakes in the Qaidam Basin

PubMed Central

Wang, Jiali; Wang, Fang; Chu, Limin; Wang, Hao; Zhong, Zhiping; Liu, Zhipei; Gao, Jianyong; Duan, Hairong

2014-01-01

Saline lakes are intriguing ecosystems harboring extremely productive microbial communities in spite of their extreme environmental conditions. We performed a comprehensive analysis of the genetic diversity (18S rRNA gene) of the planktonic microbial eukaryotes (nano- and picoeukaryotes) in six different inland saline lakes located in the Qaidam Basin. The novelty level are high, with about 11.23% of the whole dataset showing <90% identity to any previously reported sequence in GenBank. At least 4 operational taxonomic units (OTUs) in mesosaline lakes, while up to eighteen OTUs in hypersaline lakes show very low CCM and CEM scores, indicating that these sequences are highly distantly related to any existing sequence. Most of the 18S rRNA gene sequence reads obtained in investigated mesosaline lakes is closely related to Holozoa group (48.13%), whereas Stramenopiles (26.65%) and Alveolates (10.84%) are the next most common groups. Hypersaline lakes in the Qaidam Basin are also dominated by Holozoa group, accounting for 26.65% of the total number of sequence reads. Notably, Chlorophyta group are only found in high abundance in Lake Gasikule (28.00%), whereas less represented in other hypersaline lakes such as Gahai (0.50%) and Xiaochaidan (1.15%). Further analysis show that the compositions of planktonic eukaryotic assemblages are also most variable between different sampling sites in the same lake. Out of the parameters, four show significant correlation to this CCA: altitude, calcium, sodium and potassium concentrations. Overall, this study shows important gaps in the current knowledge about planktonic microbial eukaryotes inhabiting Qaidam Basin (hyper) saline water bodies. The identified diversity and novelty patterns among eukaryotic plankton assemblages in saline lake are of great importance for understanding and interpreting their ecology and evolution. PMID:25401703
Sequence-structure relationships in RNA loops: establishing the basis for loop homology modeling.

PubMed

Schudoma, Christian; May, Patrick; Nikiforova, Viktoria; Walther, Dirk

2010-01-01

The specific function of RNA molecules frequently resides in their seemingly unstructured loop regions. We performed a systematic analysis of RNA loops extracted from experimentally determined three-dimensional structures of RNA molecules. A comprehensive loop-structure data set was created and organized into distinct clusters based on structural and sequence similarity. We detected clear evidence of the hallmark of homology present in the sequence-structure relationships in loops. Loops differing by <25% in sequence identity fold into very similar structures. Thus, our results support the application of homology modeling for RNA loop model building. We established a threshold that may guide the sequence divergence-based selection of template structures for RNA loop homology modeling. Of all possible sequences that are, under the assumption of isosteric relationships, theoretically compatible with actual sequences observed in RNA structures, only a small fraction is contained in the Rfam database of RNA sequences and classes implying that the actual RNA loop space may consist of a limited number of unique loop structures and conserved sequences. The loop-structure data sets are made available via an online database, RLooM. RLooM also offers functionalities for the modeling of RNA loop structures in support of RNA engineering and design efforts.
Integrative transcriptome analysis identifies deregulated microRNA-transcription factor networks in lung adenocarcinoma

PubMed Central

Cinegaglia, Naiara C.; Andrade, Sonia Cristina S.; Tokar, Tomas; Pinheiro, Maísa; Severino, Fábio E.; Oliveira, Rogério A.; Hasimoto, Erica N.; Cataneo, Daniele C.; Cataneo, Antônio J.M.; Defaveri, Júlio; Souza, Cristiano P.; Marques, Márcia M.C.; Carvalho, Robson F.; Coutinho, Luiz L.; Gross, Jefferson L.; Rogatto, Silvia R.; Lam, Wan L.; Jurisica, Igor; Reis, Patricia P.

2016-01-01

Herein, we aimed at identifying global transcriptome microRNA (miRNA) changes and miRNA target genes in lung adenocarcinoma. Samples were selected as training (N = 24) and independent validation (N = 34) sets. Tissues were microdissected to obtain >90% tumor or normal lung cells, subjected to miRNA transcriptome sequencing and TaqMan quantitative PCR validation. We further integrated our data with published miRNA and mRNA expression datasets across 1,491 lung adenocarcinoma and 455 normal lung samples. We identified known and novel, significantly over- and under-expressed (p ≤ 0.01 and FDR≤0.1) miRNAs in lung adenocarcinoma compared to normal lung tissue: let-7a, miR-10a, miR-15b, miR-23b, miR-26a, miR-26b, miR-29a, miR-30e, miR-99a, miR-146b, miR-181b, miR-181c, miR-421, miR-181a, miR-574 and miR-1247. Validated miRNAs included let-7a-2, let-7a-3, miR-15b, miR-21, miR-155 and miR-200b; higher levels of miR-21 expression were associated with lower patient survival (p = 0.042). We identified a regulatory network including miR-15b and miR-155, and transcription factors with prognostic value in lung cancer. Our findings may contribute to the development of treatment strategies in lung adenocarcinoma. PMID:27081085
Integrated analysis of miRNAs and transcriptomes in Aedes albopictus midgut reveals the differential expression profiles of immune-related genes during dengue virus serotype-2 infection.

PubMed

Liu, Yan-Xia; Li, Fen-Xiang; Liu, Zhuan-Zhuan; Jia, Zhi-Rong; Zhou, Yan-He; Zhang, Hao; Yan, Hui; Zhou, Xian-Qiang; Chen, Xiao-Guang

2016-06-01

Mosquito microRNAs (miRNAs) are involved in host-virus interaction, and have been reported to be altered by dengue virus (DENV) infection in Aedes albopictus (Diptera: Culicidae). However, little is known about the molecular mechanisms of Aedes albopictus midgut-the first organ to interact with DENV-involved in its resistance to DENV. Here we used high-throughput sequencing to characterize miRNA and messenger RNA (mRNA) expression patterns in Aedes albopictus midgut in response to dengue virus serotype 2. A total of three miRNAs and 777 mRNAs were identified to be differentially expressed upon DENV infection. For the mRNAs, we identified 198 immune-related genes and 31 of them were differentially expressed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses also showed that the differentially expressed immune-related genes were involved in immune response. Then the differential expression patterns of six immune-related genes and three miRNAs were confirmed by real-time reverse transcription polymerase chain reaction. Furthermore, seven known miRNA-mRNA interaction pairs were identified by aligning our two datasets. These analyses of miRNA and mRNA transcriptomes provide valuable information for uncovering the DENV response genes and provide a basis for future study of the resistance mechanisms in Aedes albopictus midgut. © 2016 Institute of Zoology, Chinese Academy of Sciences.
Primer-independent RNA sequencing with bacteriophage phi6 RNA polymerase and chain terminators.

PubMed

Makeyev, E V; Bamford, D H

2001-05-01

Here we propose a new general method for directly determining RNA sequence based on the use of the RNA-dependent RNA polymerase from bacteriophage phi6 and the chain terminators (RdRP sequencing). The following properties of the polymerase render it appropriate for this application: (1) the phi6 polymerase can replicate a number of single-stranded RNA templates in vitro. (2) In contrast to the primer-dependent DNA polymerases utilized in the sequencing procedure by Sanger et al. (Proc Natl Acad Sci USA, 1977, 74:5463-5467), it initiates nascent strand synthesis without a primer, starting the polymerization on the very 3'-terminus of the template. (3) The polymerase can incorporate chain-terminating nucleotide analogs into the nascent RNA chain to produce a set of base-specific termination products. Consequently, 3' proximal or even complete sequence of many target RNA molecules can be rapidly deduced without prior sequence information. The new technique proved useful for sequencing several synthetic ssRNA templates. Furthermore, using genomic segments of the bluetongue virus we show that RdRP sequencing can also be applied to naturally occurring dsRNA templates. This suggests possible uses of the method in the RNA virus research and diagnostics.

Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.

PubMed

Liu, Zhenqiu; Hsiao, William; Cantarel, Brandi L; Drábek, Elliott Franco; Fraser-Liggett, Claire

2011-12-01

Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. zliu@umm.edu Supplementary data are available at Bioinformatics online.
Simrank: Rapid and sensitive general-purpose k-mer search tool

PubMed Central

2011-01-01

Background Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project http://nihroadmap.nih.gov/hmp. Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available. Results Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset. Conclusions Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity. PMID:21524302
Correcting for batch effects in case-control microbiome studies

PubMed Central

Gibbons, Sean M.; Duvallet, Claire

2018-01-01

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses. PMID:29684016
De novo Transcriptome Analysis of Portunus trituberculatus Ovary and Testis by RNA-Seq: Identification of Genes Involved in Gonadal Development

PubMed Central

Meng, Xian-liang; Liu, Ping; Jia, Fu-long; Li, Jian; Gao, Bao-Quan

2015-01-01

The swimming crab Portunus trituberculatus is a commercially important crab species in East Asia countries. Gonadal development is a physiological process of great significance to the reproduction as well as commercial seed production for P. trituberculatus. However, little is currently known about the molecular mechanisms governing the developmental processes of gonads in this species. To open avenues of molecular research on P. trituberculatus gonadal development, Illumina paired-end sequencing technology was employed to develop deep-coverage transcriptome sequencing data for its gonads. Illumina sequencing generated 58,429,148 and 70,474,978 high-quality reads from the ovary and testis cDNA library, respectively. All these reads were assembled into 54,960 unigenes with an average sequence length of 879 bp, of which 12,340 unigenes (22.45% of the total) matched sequences in GenBank non-redundant database. Based on our transcriptome analysis as well as published literature, a number of candidate genes potentially involved in the regulation of gonadal development of P. trituberculatus were identified, such as FAOMeT, mPRγ, PGMRC1, PGDS, PGER4, 3β-HSD and 17β-HSDs. Differential expression analysis generated 5,919 differentially expressed genes between ovary and testis, among which many genes related to gametogenesis and several genes previously reported to be critical in differentiation and development of gonads were found, including Foxl2, Wnt4, Fst, Fem-1 and Sox9. Furthermore, 28,534 SSRs and 111,646 high-quality SNPs were identified in this transcriptome dataset. This work represents the first transcriptome analysis of P. trituberculatus gonads using the next generation sequencing technology and provides a valuable dataset for understanding molecular mechanisms controlling development of gonads and facilitating future investigation of reproductive biology in this species. The molecular markers obtained in this study will provide a fundamental basis for population genetics and functional genomics in P. trituberculatus and other closely related species. PMID:26042806
Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization.

PubMed

Bauer, Markus; Klau, Gunnar W; Reinert, Knut

2007-07-27

The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account. We present a graph-based representation for sequence-structure alignments, which we model as an integer linear program (ILP). We sketch how we compute an optimal or near-optimal solution to the ILP using methods from combinatorial optimization, and present results on a recently published benchmark set for RNA alignments. The implementation of our algorithm yields better alignments in terms of two published scores than the other programs that we tested: This is especially the case with an increasing number of input sequences. Our program LARA is freely available for academic purposes from http://www.planet-lisa.net.
Integrated sequencing of exome and mRNA of large-sized single cells.

PubMed

Wang, Lily Yan; Guo, Jiajie; Cao, Wei; Zhang, Meng; He, Jiankui; Li, Zhoufang

2018-01-10

Current approaches of single cell DNA-RNA integrated sequencing are difficult to call SNPs, because a large amount of DNA and RNA is lost during DNA-RNA separation. Here, we performed simultaneous single-cell exome and transcriptome sequencing on individual mouse oocytes. Using microinjection, we kept the nuclei intact to avoid DNA loss, while retaining the cytoplasm inside the cell membrane, to maximize the amount of DNA and RNA captured from the single cell. We then conducted exome-sequencing on the isolated nuclei and mRNA-sequencing on the enucleated cytoplasm. For single oocytes, exome-seq can cover up to 92% of exome region with an average sequencing depth of 10+, while mRNA-sequencing reveals more than 10,000 expressed genes in enucleated cytoplasm, with similar performance for intact oocytes. This approach provides unprecedented opportunities to study DNA-RNA regulation, such as RNA editing at single nucleotide level in oocytes. In future, this method can also be applied to other large cells, including neurons, large dendritic cells and large tumour cells for integrated exome and transcriptome sequencing.
A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data.

PubMed

Zou, Jinfeng; Xiang, Daoquan; Datla, Raju; Wang, Edwin

2018-01-01

Genomic imprinting is an epigenetic regulatory mechanism that operates through expression of certain genes from maternal or paternal in a parent-of-origin-specific manner. Imprinted genes have been identified in diverse biological systems that are implicated in some human diseases and in embryonic and seed developmental programs in plants. The molecular underpinning programs and mechanisms involved in imprinting are yet to be explored in depth in plants. The recent advances in RNA-Seq-based methods and technologies offer an opportunity to systematically analyze epigenetic imprinting that operates at the whole genome level in the model and crop plants. We are interested using Arabidopsis model system, to investigate gene expression patterns associated with parent of origin and their implications to imprinting during embryo and seed development. Toward this, we have generated early embryo development RNA-Seq-based transcriptome datasets in F1s from a genetic cross between two diverse Arabidopsis thaliana ecotypes Col-0 and Tsu-1. With the data, we developed a protocol for evaluating the maternal and paternal contributions of genes during the early stages of embryo development after fertilization. This protocol is also designed to consider the contamination from other potential seed tissues, sequencing quality, proper processing of sequenced reads and variant calling, and appropriate inference of the parental contributions based on the parent-of-origin-specific single-nucleotide polymorphisms within the expressed genes. The approach, methods and the protocol developed in this study can be used for evaluating the effects of epigenetic imprinting in plants.
Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits: From RNA Integrity to Network Topology

PubMed Central

O'Brien, M.A.; Costin, B.N.; Miles, M.F.

2014-01-01

Postgenomic studies of the function of genes and their role in disease have now become an area of intense study since efforts to define the raw sequence material of the genome have largely been completed. The use of whole-genome approaches such as microarray expression profiling and, more recently, RNA-sequence analysis of transcript abundance has allowed an unprecedented look at the workings of the genome. However, the accurate derivation of such high-throughput data and their analysis in terms of biological function has been critical to truly leveraging the postgenomic revolution. This chapter will describe an approach that focuses on the use of gene networks to both organize and interpret genomic expression data. Such networks, derived from statistical analysis of large genomic datasets and the application of multiple bioinformatics data resources, poten-tially allow the identification of key control elements for networks associated with human disease, and thus may lead to derivation of novel therapeutic approaches. However, as discussed in this chapter, the leveraging of such networks cannot occur without a thorough understanding of the technical and statistical factors influencing the derivation of genomic expression data. Thus, while the catch phrase may be “it's the network … stupid,” the understanding of factors extending from RNA isolation to genomic profiling technique, multivariate statistics, and bioinformatics are all critical to defining fully useful gene networks for study of complex biology. PMID:23195313
The tmRNA website

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudson, Corey M.; Williams, Kelly P.

We report that the transfer-messenger RNA (tmRNA) and its partner protein SmpB act together in resolving problems arising when translating bacterial ribosomes reach the end of mRNA with no stop codon. Their genes have been found in nearly all bacterial genomes and in some organelles. The tmRNA Website serves tmRNA sequences, alignments and feature annotations, and has recently moved to http: //bioinformatics.sandia.gov/tmrna/. New features include software used to find the sequences, an update raising the number of unique tmRNA sequences from 492 to 1716, and a database of SmpB sequences which are served along with the tmRNA sequence from themore » same organism.« less
The tmRNA website

DOE PAGES

Hudson, Corey M.; Williams, Kelly P.

2014-11-05

We report that the transfer-messenger RNA (tmRNA) and its partner protein SmpB act together in resolving problems arising when translating bacterial ribosomes reach the end of mRNA with no stop codon. Their genes have been found in nearly all bacterial genomes and in some organelles. The tmRNA Website serves tmRNA sequences, alignments and feature annotations, and has recently moved to http: //bioinformatics.sandia.gov/tmrna/. New features include software used to find the sequences, an update raising the number of unique tmRNA sequences from 492 to 1716, and a database of SmpB sequences which are served along with the tmRNA sequence from themore » same organism.« less
First insight into the viral community of the cnidarian model metaorganism Aiptasia using RNA-Seq data

PubMed Central

Brüwer, Jan D.

2018-01-01

Current research posits that all multicellular organisms live in symbioses with associated microorganisms and form so-called metaorganisms or holobionts. Cnidarian metaorganisms are of specific interest given that stony corals provide the foundation of the globally threatened coral reef ecosystems. To gain first insight into viruses associated with the coral model system Aiptasia (sensu Exaiptasia pallida), we analyzed an existing RNA-Seq dataset of aposymbiotic, partially populated, and fully symbiotic Aiptasia CC7 anemones with Symbiodinium. Our approach included the selective removal of anemone host and algal endosymbiont sequences and subsequent microbial sequence annotation. Of a total of 297 million raw sequence reads, 8.6 million (∼3%) remained after host and endosymbiont sequence removal. Of these, 3,293 sequences could be assigned as of viral origin. Taxonomic annotation of these sequences suggests that Aiptasia is associated with a diverse viral community, comprising 116 viral taxa covering 40 families. The viral assemblage was dominated by viruses from the families Herpesviridae (12.00%), Partitiviridae (9.93%), and Picornaviridae (9.87%). Despite an overall stable viral assemblage, we found that some viral taxa exhibited significant changes in their relative abundance when Aiptasia engaged in a symbiotic relationship with Symbiodinium. Elucidation of viral taxa consistently present across all conditions revealed a core virome of 15 viral taxa from 11 viral families, encompassing many viruses previously reported as members of coral viromes. Despite the non-random selection of viral genetic material due to the nature of the sequencing data analyzed, our study provides a first insight into the viral community associated with Aiptasia. Similarities of the Aiptasia viral community with those of corals corroborate the application of Aiptasia as a model system to study coral holobionts. Further, the change in abundance of certain viral taxa across different symbiotic states suggests a role of viruses in the algal endosymbiosis, but the functional significance of this remains to be determined. PMID:29507840
Ribosomal RNA sequence suggest microsporidia are extremely ancient eukaryotes

NASA Technical Reports Server (NTRS)

Vossbrinck, C. R.; Maddox, J. V.; Friedman, S.; Debrunner-Vossbrinck, B. A.; Woese, C. R.

1987-01-01

A comparative sequence analysis of the 18S small subunit ribosomal RNA (rRNA) of the microsporidium Vairimorpha necatrix is presented. The results show that this rRNA sequence is more unlike those of other eukaryotes than any known eukaryote rRNA sequence. It is concluded that the lineage leading to microsporidia branched very early from that leading to other eukaryotes.
Detection of microRNAs in color space.

PubMed

Marco, Antonio; Griffiths-Jones, Sam

2012-02-01

Deep sequencing provides inexpensive opportunities to characterize the transcriptional diversity of known genomes. The AB SOLiD technology generates millions of short sequencing reads in color-space; that is, the raw data is a sequence of colors, where each color represents 2 nt and each nucleotide is represented by two consecutive colors. This strategy is purported to have several advantages, including increased ability to distinguish sequencing errors from polymorphisms. Several programs have been developed to map short reads to genomes in color space. However, a number of previously unexplored technical issues arise when using SOLiD technology to characterize microRNAs. Here we explore these technical difficulties. First, since the sequenced reads are longer than the biological sequences, every read is expected to contain linker fragments. The color-calling error rate increases toward the 3(') end of the read such that recognizing the linker sequence for removal becomes problematic. Second, mapping in color space may lead to the loss of the first nucleotide of each read. We propose a sequential trimming and mapping approach to map small RNAs. Using our strategy, we reanalyze three published insect small RNA deep sequencing datasets and characterize 22 new microRNAs. A bash shell script to perform the sequential trimming and mapping procedure, called SeqTrimMap, is available at: http://www.mirbase.org/tools/seqtrimmap/ antonio.marco@manchester.ac.uk Supplementary data are available at Bioinformatics online.
Equally parsimonious pathways through an RNA sequence space are not equally likely

NASA Technical Reports Server (NTRS)

Lee, Y. H.; DSouza, L. M.; Fox, G. E.

1997-01-01

An experimental system for determining the potential ability of sequences resembling 5S ribosomal RNA (rRNA) to perform as functional 5S rRNAs in vivo in the Escherichia coli cellular environment was devised previously. Presumably, the only 5S rRNA sequences that would have been fixed by ancestral populations are ones that were functionally valid, and hence the actual historical paths taken through RNA sequence space during 5S rRNA evolution would have most likely utilized valid sequences. Herein, we examine the potential validity of all sequence intermediates along alternative equally parsimonious trajectories through RNA sequence space which connect two pairs of sequences that had previously been shown to behave as valid 5S rRNAs in E. coli. The first trajectory requires a total of four changes. The 14 sequence intermediates provide 24 apparently equally parsimonious paths by which the transition could occur. The second trajectory involves three changes, six intermediate sequences, and six potentially equally parsimonious paths. In total, only eight of the 20 sequence intermediates were found to be clearly invalid. As a consequence of the position of these invalid intermediates in the sequence space, seven of the 30 possible paths consisted of exclusively valid sequences. In several cases, the apparent validity/invalidity of the intermediate sequences could not be anticipated on the basis of current knowledge of the 5S rRNA structure. This suggests that the interdependencies in RNA sequence space may be more complex than currently appreciated. If ancestral sequences predicted by parsimony are to be regarded as actual historical sequences, then the present results would suggest that they should also satisfy a validity requirement and that, in at least limited cases, this conjecture can be tested experimentally.
Quantifying the relationship between sequence and three-dimensional structure conservation in RNA

PubMed Central

2010-01-01

Background In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA. Results Here we introduce an all-against-all sequence- and three-dimensional (3D) structure-based comparison of a representative set of RNA structures, which have allowed us to quantitatively confirm that: (i) there is a measurable relationship between sequence and structure conservation that weakens for alignments resulting in below 60% sequence identity, (ii) evolution tends to conserve more RNA structure than sequence, and (iii) there is a twilight zone for RNA homology detection. Discussion The computational analysis here presented quantitatively describes the relationship between sequence and structure for RNA molecules and defines a twilight zone region for detecting RNA homology. Our work could represent the theoretical basis and limitations for future developments in comparative RNA 3D structure prediction. PMID:20550657
Cross-platform normalization of microarray and RNA-seq data for machine learning applications

PubMed Central

Thompson, Jeffrey A.; Tan, Jie

2016-01-01

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language. PMID:26844019
ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets.

PubMed

Brozovic, Matija; Dantec, Christelle; Dardaillon, Justine; Dauga, Delphine; Faure, Emmanuel; Gineste, Mathieu; Louis, Alexandra; Naville, Magali; Nitta, Kazuhiro R; Piette, Jacques; Reeves, Wendy; Scornavacca, Céline; Simion, Paul; Vincentelli, Renaud; Bellec, Maelle; Aicha, Sameh Ben; Fagotto, Marie; Guéroult-Bellone, Marion; Haeussler, Maximilian; Jacox, Edwin; Lowe, Elijah K; Mendez, Mickael; Roberge, Alexis; Stolfi, Alberto; Yokomori, Rui; Brown, C Titus; Cambillau, Christian; Christiaen, Lionel; Delsuc, Frédéric; Douzery, Emmanuel; Dumollard, Rémi; Kusakabe, Takehiro; Nakai, Kenta; Nishida, Hiroki; Satou, Yutaka; Swalla, Billie; Veeman, Michael; Volff, Jean-Nicolas; Lemaire, Patrick

2018-01-04

ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Metabarcoding monitoring analysis: the pros and cons of using co-extracted environmental DNA and RNA data to assess offshore oil production impacts on benthic communities.

PubMed

Laroche, Olivier; Wood, Susanna A; Tremblay, Louis A; Lear, Gavin; Ellis, Joanne I; Pochon, Xavier

2017-01-01

Sequencing environmental DNA (eDNA) is increasingly being used as an alternative to traditional morphological-based identification to characterize biological assemblages and monitor anthropogenic impacts in marine environments. Most studies only assess eDNA which, compared to eRNA, can persist longer in the environment after cell death. Therefore, eRNA may provide a more immediate census of the environment due to its relatively weaker stability, leading some researchers to advocate for the use of eRNA as an additional, or perhaps superior proxy for portraying ecological changes. A variety of pre-treatment techniques for screening eDNA and eRNA derived operational taxonomic units (OTUs) have been employed prior to statistical analyses, including removing singleton taxa (i.e., OTUs found only once) and discarding those not present in both eDNA and eRNA datasets. In this study, we used bacterial (16S ribosomal RNA gene) and eukaryotic (18S ribosomal RNA gene) eDNA- and eRNA-derived data from benthic communities collected at increasing distances along a transect from an oil production platform (Taranaki, New Zealand). Macro-infauna (visual classification of benthic invertebrates) and physico-chemical data were analyzed in parallel. We tested the effect of removing singleton taxa, and removing taxa not present in the eDNA and eRNA libraries from the same environmental sample (trimmed by shared OTUs), by comparing the impact of the oil production platform on alpha- and beta-diversity of the eDNA/eRNA-based biological assemblages, and by correlating these to the morphologically identified macro-faunal communities and the physico-chemical data. When trimmed by singletons, presence/absence information from eRNA data represented the best proxy to detect changes on species diversity for both bacteria and eukaryotes. However, assessment of quantitative beta-diversity from read abundance information of bacteria eRNA did not, contrary to eDNA, reveal any impact from the oil production activity. Overall, the data appeared more robust when trimmed by shared OTUs, showing a greater effect of the platform on alpha- and beta-diversity. Trimming by shared OTUs likely removes taxa derived from legacy DNA and technical artefacts introduced through reverse transcriptase, polymerase-chain-reaction and sequencing. Findings from our scoping study suggest that metabarcoding-based biomonitoring surveys should, if funds, time and expertise allow, be assessed using both eDNA and eRNA products.
Metabarcoding monitoring analysis: the pros and cons of using co-extracted environmental DNA and RNA data to assess offshore oil production impacts on benthic communities

PubMed Central

Wood, Susanna A.; Tremblay, Louis A.; Lear, Gavin; Ellis, Joanne I.; Pochon, Xavier

2017-01-01

Sequencing environmental DNA (eDNA) is increasingly being used as an alternative to traditional morphological-based identification to characterize biological assemblages and monitor anthropogenic impacts in marine environments. Most studies only assess eDNA which, compared to eRNA, can persist longer in the environment after cell death. Therefore, eRNA may provide a more immediate census of the environment due to its relatively weaker stability, leading some researchers to advocate for the use of eRNA as an additional, or perhaps superior proxy for portraying ecological changes. A variety of pre-treatment techniques for screening eDNA and eRNA derived operational taxonomic units (OTUs) have been employed prior to statistical analyses, including removing singleton taxa (i.e., OTUs found only once) and discarding those not present in both eDNA and eRNA datasets. In this study, we used bacterial (16S ribosomal RNA gene) and eukaryotic (18S ribosomal RNA gene) eDNA- and eRNA-derived data from benthic communities collected at increasing distances along a transect from an oil production platform (Taranaki, New Zealand). Macro-infauna (visual classification of benthic invertebrates) and physico-chemical data were analyzed in parallel. We tested the effect of removing singleton taxa, and removing taxa not present in the eDNA and eRNA libraries from the same environmental sample (trimmed by shared OTUs), by comparing the impact of the oil production platform on alpha- and beta-diversity of the eDNA/eRNA-based biological assemblages, and by correlating these to the morphologically identified macro-faunal communities and the physico-chemical data. When trimmed by singletons, presence/absence information from eRNA data represented the best proxy to detect changes on species diversity for both bacteria and eukaryotes. However, assessment of quantitative beta-diversity from read abundance information of bacteria eRNA did not, contrary to eDNA, reveal any impact from the oil production activity. Overall, the data appeared more robust when trimmed by shared OTUs, showing a greater effect of the platform on alpha- and beta-diversity. Trimming by shared OTUs likely removes taxa derived from legacy DNA and technical artefacts introduced through reverse transcriptase, polymerase-chain-reaction and sequencing. Findings from our scoping study suggest that metabarcoding-based biomonitoring surveys should, if funds, time and expertise allow, be assessed using both eDNA and eRNA products. PMID:28533985
Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells.

PubMed

Jaiswal, Alok; Peddinti, Gopal; Akimov, Yevhen; Wennerberg, Krister; Kuznetsov, Sergey; Tang, Jing; Aittokallio, Tero

2017-06-01

Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity. We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments. Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment. We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.

Integrated modeling of protein-coding genes in the Manduca sexta genome using RNA-Seq data from the biochemical model insect.

PubMed

Cao, Xiaolong; Jiang, Haobo

2015-07-01

The genome sequence of Manduca sexta was recently determined using 454 technology. Cufflinks and MAKER2 were used to establish gene models in the genome assembly based on the RNA-Seq data and other species' sequences. Aided by the extensive RNA-Seq data from 50 tissue samples at various life stages, annotators over the world (including the present authors) have manually confirmed and improved a small percentage of the models after spending months of effort. While such collaborative efforts are highly commendable, many of the predicted genes still have problems which may hamper future research on this insect species. As a biochemical model representing lepidopteran pests, M. sexta has been used extensively to study insect physiological processes for over five decades. In this work, we assembled Manduca datasets Cufflinks 3.0, Trinity 4.0, and Oases 4.0 to assist the manual annotation efforts and development of Official Gene Set (OGS) 2.0. To further improve annotation quality, we developed methods to evaluate gene models in the MAKER2, Cufflinks, Oases and Trinity assemblies and selected the best ones to constitute MCOT 1.0 after thorough crosschecking. MCOT 1.0 has 18,089 genes encoding 31,666 proteins: 32.8% match OGS 2.0 models perfectly or near perfectly, 11,747 differ considerably, and 29.5% are absent in OGS 2.0. Future automation of this process is anticipated to greatly reduce human efforts in generating comprehensive, reliable models of structural genes in other genome projects where extensive RNA-Seq data are available. Copyright © 2015 Elsevier Ltd. All rights reserved.
Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion.

PubMed

Zhao, Shanrong; Zhang, Ying; Gamini, Ramya; Zhang, Baohong; von Schack, David

2018-03-19

To allow efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNA) are generally removed from total RNA either by positive polyA+ selection or by rRNA depletion (negative selection) before sequencing. Comparisons between the two methods have been carried out by various groups, but the assessments have relied largely on non-clinical samples. In this study, we evaluated these two RNA sequencing approaches using human blood and colon tissue samples. Our analyses showed that rRNA depletion captured more unique transcriptome features, whereas polyA+ selection outperformed rRNA depletion with higher exonic coverage and better accuracy of gene quantification. For blood- and colon-derived RNAs, we found that 220% and 50% more reads, respectively, would have to be sequenced to achieve the same level of exonic coverage in the rRNA depletion method compared with the polyA+ selection method. Therefore, in most cases we strongly recommend polyA+ selection over rRNA depletion for gene quantification in clinical RNA sequencing. Our evaluation revealed that a small number of lncRNAs and small RNAs made up a large fraction of the reads in the rRNA depletion RNA sequencing data. Thus, we recommend that these RNAs are specifically depleted to improve the sequencing depth of the remaining RNAs.
Ariadne: a database search engine for identification and chemical analysis of RNA using tandem mass spectrometry data.

PubMed

Nakayama, Hiroshi; Akiyama, Misaki; Taoka, Masato; Yamauchi, Yoshio; Nobe, Yuko; Ishikawa, Hideaki; Takahashi, Nobuhiro; Isobe, Toshiaki

2009-04-01

We present here a method to correlate tandem mass spectra of sample RNA nucleolytic fragments with an RNA nucleotide sequence in a DNA/RNA sequence database, thereby allowing tandem mass spectrometry (MS/MS)-based identification of RNA in biological samples. Ariadne, a unique web-based database search engine, identifies RNA by two probability-based evaluation steps of MS/MS data. In the first step, the software evaluates the matches between the masses of product ions generated by MS/MS of an RNase digest of sample RNA and those calculated from a candidate nucleotide sequence in a DNA/RNA sequence database, which then predicts the nucleotide sequences of these RNase fragments. In the second step, the candidate sequences are mapped for all RNA entries in the database, and each entry is scored for a function of occurrences of the candidate sequences to identify a particular RNA. Ariadne can also predict post-transcriptional modifications of RNA, such as methylation of nucleotide bases and/or ribose, by estimating mass shifts from the theoretical mass values. The method was validated with MS/MS data of RNase T1 digests of in vitro transcripts. It was applied successfully to identify an unknown RNA component in a tRNA mixture and to analyze post-transcriptional modification in yeast tRNA(Phe-1).
RNA processing in Neurospora crassa mitochondria: use of transfer RNA sequences as signals.

PubMed Central

Breitenberger, C A; Browning, K S; Alzner-DeWeerd, B; RajBhandary, U L

1985-01-01

We have used RNA gel transfer hybridization, S1 nuclease mapping and primer extension to analyze transcripts derived from several genes in Neurospora crassa mitochondria. The transcripts studied include those for cytochrome oxidase subunit III, 17S rRNA and an unidentified open reading frame. In all three cases, initial transcripts are long, include tRNA sequences, and are subsequently processed to generate the mature RNAs. We find that endpoints of the most abundant transcripts generally coincide with those of tRNA sequences. We therefore conclude that tRNA sequences in long transcripts act as primary signals for RNA processing in N. crassa mitochondria. The situation is somewhat analogous to that observed in mammalian mitochondrial systems. The difference, however, is that in mammalian mitochondria, noncoding spacers between tRNA, rRNA and protein genes are very short and in many cases non-existent, allowing no room for intergenic RNA processing signals whereas, in N. crassa mtDNA, intergenic non-coding sequences are usually several hundred nucleotides long and contain highly conserved GC-rich palindromic sequences. Since these GC-rich palindromic sequences are retained in the processed mature RNAs, we conclude that they do not serve as signals for RNA processing. Images Fig. 2. Fig. 3. Fig. 4. Fig. 5. Fig. 6. Fig. 7. PMID:2990893
RNase H-assisted RNA-primed rolling circle amplification for targeted RNA sequence detection.

PubMed

Takahashi, Hirokazu; Ohkawachi, Masahiko; Horio, Kyohei; Kobori, Toshiro; Aki, Tsunehiro; Matsumura, Yukihiko; Nakashimada, Yutaka; Okamura, Yoshiko

2018-05-17

RNA-primed rolling circle amplification (RPRCA) is a useful laboratory method for RNA detection; however, the detection of RNA is limited by the lack of information on 3'-terminal sequences. We uncovered that conventional RPRCA using pre-circularized probes could potentially detect the internal sequence of target RNA molecules in combination with RNase H. However, the specificity for mRNA detection was low, presumably due to non-specific hybridization of non-target RNA with the circular probe. To overcome this technical problem, we developed a method for detecting a sequence of interest in target RNA molecules via RNase H-assisted RPRCA using padlocked probes. When padlock probes are hybridized to the target RNA molecule, they are converted to the circular form by SplintR ligase. Subsequently, RNase H creates nick sites only in the hybridized RNA sequence, and single-stranded DNA is finally synthesized from the nick site by phi29 DNA polymerase. This method could specifically detect at least 10 fmol of the target RNA molecule without reverse transcription. Moreover, this method detected GFP mRNA present in 10 ng of total RNA isolated from Escherichia coli without background DNA amplification. Therefore, this method can potentially detect almost all types of RNA molecules without reverse transcription and reveal full-length sequence information.
Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

PubMed

Grimes, Tyler; Walker, Alejandro R; Datta, Susmita; Datta, Somnath

2018-05-30

Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. Using predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients. This article was reviewed by Subharup Guha and Isabel Nepomuceno.
GC-Content Normalization for RNA-Seq Data

PubMed Central

2011-01-01

Background Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof. Results We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq. Conclusions Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes. PMID:22177264
Expanding the World of Marine Bacterial and Archaeal Clades

PubMed Central

Yilmaz, Pelin; Yarza, Pablo; Rapp, Josephine Z.; Glöckner, Frank O.

2016-01-01

Determining which microbial taxa are out there, where they live, and what they are doing is a driving approach in marine microbial ecology. The importance of these questions is underlined by concerted, large-scale, and global ocean sampling initiatives, for example the International Census of Marine Microbes, Ocean Sampling Day, or Tara Oceans. Given decades of effort, we know that the large majority of marine Bacteria and Archaea belong to about a dozen phyla. In addition to the classically culturable Bacteria and Archaea, at least 50 “clades,” at different taxonomic depths, exist. These account for the majority of marine microbial diversity, but there is still an underexplored and less abundant portion remaining. We refer to these hitherto unrecognized clades as unknown, as their boundaries, names, and classifications are not available. In this work, we were able to characterize up to 92 of these unknown clades found within the bacterial and archaeal phylogenetic diversity currently reported for marine water column environments. We mined the SILVA 16S rRNA gene datasets for sequences originating from the marine water column. Instead of the usual subjective taxa delineation and nomenclature methods, we applied the candidate taxonomic unit (CTU) circumscription system, along with a standardized nomenclature to the sequences in newly constructed phylogenetic trees. With this new phylogenetic and taxonomic framework, we performed an analysis of ICoMM rRNA gene amplicon datasets to gain insights into the global distribution of the new marine clades, their ecology, biogeography, and interaction with oceanographic variables. Most of the new clades we identified were interspersed by known taxa with cultivated members, whose genome sequences are available. This result encouraged us to perform metabolic predictions for the novel marine clades using the PICRUSt approach. Our work also provides an update on the taxonomy of several phyla and widely known marine clades as our CTU approach breaks down these randomly lumped clades into smaller objectively calculated subgroups. Finally, all taxa were classified and named following standards compatible with the Bacteriological Code rules, enhancing their digitization, and comparability with future microbial ecological and taxonomy studies. PMID:26779174
Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trembath-Reichert, Elizabeth; Case, David H.; Orphan, Victoria J.

Methane seep systems along continental margins host diverse and dynamic microbial assemblages, sustained in large part through the microbially mediated process of sulfate-coupled Anaerobic Oxidation of Methane (AOM). This methanotrophic metabolism has been linked to consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). These two groups are the focus of numerous studies; however, less is known about the wide diversity of other seep associated microorganisms. We selected a hierarchical set of FISH probes targeting a range ofDeltaproteobacteriadiversity. Using the Magneto-FISH enrichment technique, we then magnetically captured CARD-FISH hybridized cells and their physically associated microorganisms from a methane seepmore » sediment incubation. DNA from nested Magneto-FISH experiments was analyzed using Illumina tag 16S rRNA gene sequencing (iTag). Enrichment success and potential bias with iTag was evaluated in the context of full-length 16S rRNA gene clone libraries, CARD-FISH, functional gene clone libraries, and iTag mock communities. We determined commonly used Earth Microbiome Project (EMP) iTAG primers introduced bias in some common methane seep microbial taxa that reduced the ability to directly compare OTU relative abundances within a sample, but comparison of relative abundances between samples (in nearly all cases) and whole community-based analyses were robust. The iTag dataset was subjected to statistical co-occurrence measures of the most abundant OTUs to determine which taxa in this dataset were most correlated across all samples. In addition, many non-canonical microbial partnerships were statistically significant in our co-occurrence network analysis, most of which were not recovered with conventional clone library sequencing, demonstrating the utility of combining Magneto-FISH and iTag sequencing methods for hypothesis generation of associations within complex microbial communities. Network analysis pointed to many co-occurrences containing putatively heterotrophic, candidate phyla such as OD1, Atribacteria, MBG-B, and Hyd24-12 and the potential for complex sulfur cycling involving Epsilon-, Delta-, and Gammaproteobacteria in methane seep ecosystems.« less
Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments

DOE PAGES

Trembath-Reichert, Elizabeth; Case, David H.; Orphan, Victoria J.

2016-04-18

Methane seep systems along continental margins host diverse and dynamic microbial assemblages, sustained in large part through the microbially mediated process of sulfate-coupled Anaerobic Oxidation of Methane (AOM). This methanotrophic metabolism has been linked to consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). These two groups are the focus of numerous studies; however, less is known about the wide diversity of other seep associated microorganisms. We selected a hierarchical set of FISH probes targeting a range ofDeltaproteobacteriadiversity. Using the Magneto-FISH enrichment technique, we then magnetically captured CARD-FISH hybridized cells and their physically associated microorganisms from a methane seepmore » sediment incubation. DNA from nested Magneto-FISH experiments was analyzed using Illumina tag 16S rRNA gene sequencing (iTag). Enrichment success and potential bias with iTag was evaluated in the context of full-length 16S rRNA gene clone libraries, CARD-FISH, functional gene clone libraries, and iTag mock communities. We determined commonly used Earth Microbiome Project (EMP) iTAG primers introduced bias in some common methane seep microbial taxa that reduced the ability to directly compare OTU relative abundances within a sample, but comparison of relative abundances between samples (in nearly all cases) and whole community-based analyses were robust. The iTag dataset was subjected to statistical co-occurrence measures of the most abundant OTUs to determine which taxa in this dataset were most correlated across all samples. In addition, many non-canonical microbial partnerships were statistically significant in our co-occurrence network analysis, most of which were not recovered with conventional clone library sequencing, demonstrating the utility of combining Magneto-FISH and iTag sequencing methods for hypothesis generation of associations within complex microbial communities. Network analysis pointed to many co-occurrences containing putatively heterotrophic, candidate phyla such as OD1, Atribacteria, MBG-B, and Hyd24-12 and the potential for complex sulfur cycling involving Epsilon-, Delta-, and Gammaproteobacteria in methane seep ecosystems.« less
Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments.

PubMed

Trembath-Reichert, Elizabeth; Case, David H; Orphan, Victoria J

2016-01-01

Methane seep systems along continental margins host diverse and dynamic microbial assemblages, sustained in large part through the microbially mediated process of sulfate-coupled Anaerobic Oxidation of Methane (AOM). This methanotrophic metabolism has been linked to consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). These two groups are the focus of numerous studies; however, less is known about the wide diversity of other seep associated microorganisms. We selected a hierarchical set of FISH probes targeting a range of Deltaproteobacteria diversity. Using the Magneto-FISH enrichment technique, we then magnetically captured CARD-FISH hybridized cells and their physically associated microorganisms from a methane seep sediment incubation. DNA from nested Magneto-FISH experiments was analyzed using Illumina tag 16S rRNA gene sequencing (iTag). Enrichment success and potential bias with iTag was evaluated in the context of full-length 16S rRNA gene clone libraries, CARD-FISH, functional gene clone libraries, and iTag mock communities. We determined commonly used Earth Microbiome Project (EMP) iTAG primers introduced bias in some common methane seep microbial taxa that reduced the ability to directly compare OTU relative abundances within a sample, but comparison of relative abundances between samples (in nearly all cases) and whole community-based analyses were robust. The iTag dataset was subjected to statistical co-occurrence measures of the most abundant OTUs to determine which taxa in this dataset were most correlated across all samples. Many non-canonical microbial partnerships were statistically significant in our co-occurrence network analysis, most of which were not recovered with conventional clone library sequencing, demonstrating the utility of combining Magneto-FISH and iTag sequencing methods for hypothesis generation of associations within complex microbial communities. Network analysis pointed to many co-occurrences containing putatively heterotrophic, candidate phyla such as OD1, Atribacteria, MBG-B, and Hyd24-12 and the potential for complex sulfur cycling involving Epsilon-, Delta-, and Gammaproteobacteria in methane seep ecosystems.
Capturing microRNA targets using an RNA-induced silencing complex (RISC)-trap approach.

PubMed

Cambronne, Xiaolu A; Shen, Rongkun; Auer, Paul L; Goodman, Richard H

2012-12-11

Identifying targets is critical for understanding the biological effects of microRNA (miRNA) expression. The challenge lies in characterizing the cohort of targets for a specific miRNA, especially when targets are being actively down-regulated in miRNA- RNA-induced silencing complex (RISC)-messengerRNA (mRNA) complexes. We have developed a robust and versatile strategy called RISCtrap to stabilize and purify targets from this transient interaction. Its utility was demonstrated by determining specific high-confidence target datasets for miR-124, miR-132, and miR-181 that contained known and previously unknown transcripts. Two previously unknown miR-132 targets identified with RISCtrap, adaptor protein CT10 regulator of kinase 1 (CRK1) and tight junction-associated protein 1 (TJAP1), were shown to be endogenously regulated by miR-132 in adult mouse forebrain. The datasets, moreover, differed in the number of targets and in the types and frequency of microRNA recognition element (MRE) motifs, thus revealing a previously underappreciated level of specificity in the target sets regulated by individual miRNAs.
Molecular Signatures of Microbial Metabolism in an Actively Growing, Silicified, Microbial Structure from Yellowstone National Park

NASA Astrophysics Data System (ADS)

Ferreira, M.; Creveling, J.; Hilburn, I.; Karlsson, E.; Pepe-Ranney, C.; Spear, J.; Dawson, S.; Geobio2008, I.

2008-12-01

Silicified structures that exhibit a putative biologic component in their formation permeate the rock record as stromatolites. We have studied a silicified microbial structure from a hot spring in Yellowstone National Park using phenotypic, phylogenetic, and metagenomic analyses to determine microbial carbon metabolic pathways and the phylogenetic affiliations of microbes present in this unique structure. In this multi-faceted approach, dominant physiologies, specifically with regards to anaerobic and aerobic metabolisms, were inferred from 16S rRNA gene sequences and 454 sequencing data from bulk DNA samples of the structure. Carbon utilization as indicated by ECO Biolog plates showed abundant heterotrophy and heterotrophic diversity throughout the microbial structure. Microbes within the structure are able to utilize all tested sources of carbohydrates, lipids/fatty acids, and protein/amino acids as carbon sources. ECO plate testing of the hot spring water yielded considerable less carbohydrate consumption (only 4 out of 13 tested carbohydrates) and similar lipids/fatty acids and protein/amino acids consumption (2 out of 3 and 5 out of 5 tested sources respectively). Full length 16S rRNA gene sequences and metagenomic 454 pyrosequencing of community DNA showed limited diversity among primary producers. From the 16S data, the majority of the autotrophs are inferred to utilize the Calvin cycle for CO2 fixation, followed by 3-hydroxypropionate/4- hydroxybutyrate CO2 fixation. However, an analysis of the metagenomic data compared to the KEGG database does not show genes directly involved with Calvin cycle carbon fixation. Further BLAST searches of our data failed to find significant matches within our 6514 metagenomic sequences to known RuBisCo sequences taken from the NCBI database. This is likely due to a far under-sampled dataset of metagenomic sequences, and the low number (958) that had matches to the KEGG pathways database. Anaerobic versus aerobic physiology also can be estimated from the 16S clone libraries. Phylogenetic analysis of recovered 16S sequences suggests that 15% of the 16S sequences can be attributed to anaerobic microbes while 42% likely come from aerobes. The remaining 43% of 16S rRNA gene sequences belong to metabolically unassigned phyla both known and novel. This preliminary study demonstrates that the small spatially stratified silicified microbial structure present on the margins of a hot spring contains a rich and complex microbial community with different trophic levels and enzymatic pathways.
RNAcentral: A comprehensive database of non-coding RNA sequences

DOE PAGES

Williams, Kelly Porter; Lau, Britney Yan

2016-10-28

RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. Furthermore, the website has been subject to continuous improvements focusing on text and sequence similaritymore » searches as well as genome browsing functionality.« less
RNAcentral: A comprehensive database of non-coding RNA sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Kelly Porter; Lau, Britney Yan

RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. Furthermore, the website has been subject to continuous improvements focusing on text and sequence similaritymore » searches as well as genome browsing functionality.« less
Protein Interaction Profile Sequencing (PIP-seq).

PubMed

Foley, Shawn W; Gregory, Brian D

2016-10-10

Every eukaryotic RNA transcript undergoes extensive post-transcriptional processing from the moment of transcription up through degradation. This regulation is performed by a distinct cohort of RNA-binding proteins which recognize their target transcript by both its primary sequence and secondary structure. Here, we describe protein interaction profile sequencing (PIP-seq), a technique that uses ribonuclease-based footprinting followed by high-throughput sequencing to globally assess both protein-bound RNA sequences and RNA secondary structure. PIP-seq utilizes single- and double-stranded RNA-specific nucleases in the absence of proteins to infer RNA secondary structure. These libraries are also compared to samples that undergo nuclease digestion in the presence of proteins in order to find enriched protein-bound sequences. Combined, these four libraries provide a comprehensive, transcriptome-wide view of RNA secondary structure and RNA protein interaction sites from a single experimental technique. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.
Characterization of the complete mitochondrial genomes of two whipworms Trichuris ovis and Trichuris discolor (Nematoda: Trichuridae).

PubMed

Liu, Guo-Hua; Wang, Yan; Xu, Min-Jun; Zhou, Dong-Hui; Ye, Yong-Gang; Li, Jia-Yuan; Song, Hui-Qun; Lin, Rui-Qing; Zhu, Xing-Quan

2012-12-01

For many years, whipworms (Trichuris spp.) have been described with a relatively narrow range of both morphological and biometrical features. Moreover, there has been insufficient discrimination between congeners (or closely related species). In the present study, we determined the complete mitochondrial (mt) genomes of two whipworms Trichuris ovis and Trichuris discolor, compared them and then tested the hypothesis that T. ovis and T. discolor are distinct species by phylogenetic analyses using Bayesian inference, maximum likelihood and maximum parsimony) based on the deduced amino acid sequences of the mt protein-coding genes. The complete mt genomes of T. ovis and T. discolor were 13,946 bp and 13,904 bp in size, respectively. Both mt genomes are circular, and consist of 37 genes, including 13 genes coding for proteins, 2 genes for rRNA, and 22 genes for tRNA. The gene content and arrangement are identical to that of human and pig whipworms Trichuris trichiura and Trichuris suis. Taken together, these analyses showed genetic distinctiveness and strongly supported the recent proposal that T. ovis and T. discolor are distinct species using nuclear ribosomal DNA and a portion of the mtDNA sequence dataset. The availability of the complete mtDNA sequences of T. ovis and T. discolor provides novel genetic markers for studying the population genetics, diagnostics and molecular epidemiology of T. ovis and T. discolor. Copyright © 2012 Elsevier B.V. All rights reserved.
The king cobra genome reveals dynamic gene evolution and adaptation in the snake venom system.

PubMed

Vonk, Freek J; Casewell, Nicholas R; Henkel, Christiaan V; Heimberg, Alysha M; Jansen, Hans J; McCleary, Ryan J R; Kerkkamp, Harald M E; Vos, Rutger A; Guerreiro, Isabel; Calvete, Juan J; Wüster, Wolfgang; Woods, Anthony E; Logan, Jessica M; Harrison, Robert A; Castoe, Todd A; de Koning, A P Jason; Pollock, David D; Yandell, Mark; Calderon, Diego; Renjifo, Camila; Currier, Rachel B; Salgado, David; Pla, Davinia; Sanz, Libia; Hyder, Asad S; Ribeiro, José M C; Arntzen, Jan W; van den Thillart, Guido E E J M; Boetzer, Marten; Pirovano, Walter; Dirks, Ron P; Spaink, Herman P; Duboule, Denis; McGlinn, Edwina; Kini, R Manjunatha; Richardson, Michael K

2013-12-17

Snakes are limbless predators, and many species use venom to help overpower relatively large, agile prey. Snake venoms are complex protein mixtures encoded by several multilocus gene families that function synergistically to cause incapacitation. To examine venom evolution, we sequenced and interrogated the genome of a venomous snake, the king cobra (Ophiophagus hannah), and compared it, together with our unique transcriptome, microRNA, and proteome datasets from this species, with data from other vertebrates. In contrast to the platypus, the only other venomous vertebrate with a sequenced genome, we find that snake toxin genes evolve through several distinct co-option mechanisms and exhibit surprisingly variable levels of gene duplication and directional selection that correlate with their functional importance in prey capture. The enigmatic accessory venom gland shows a very different pattern of toxin gene expression from the main venom gland and seems to have recruited toxin-like lectin genes repeatedly for new nontoxic functions. In addition, tissue-specific microRNA analyses suggested the co-option of core genetic regulatory components of the venom secretory system from a pancreatic origin. Although the king cobra is limbless, we recovered coding sequences for all Hox genes involved in amniote limb development, with the exception of Hoxd12. Our results provide a unique view of the origin and evolution of snake venom and reveal multiple genome-level adaptive responses to natural selection in this complex biological weapon system. More generally, they provide insight into mechanisms of protein evolution under strong selection.
A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.

PubMed

Li, Xiaohong; Brock, Guy N; Rouchka, Eric C; Cooper, Nigel G F; Wu, Dongfeng; O'Toole, Timothy E; Gill, Ryan S; Eteleeb, Abdallah M; O'Brien, Liz; Rai, Shesh N

2017-01-01

Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods (DESeq, TMM-edgeR, FPKM-CuffDiff, TC, Med UQ and FQ) and two new methods we propose: Med-pgQ2 and UQ-pgQ2 (per-gene normalization after per-sample median or upper-quartile global scaling). Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (<70%) and a slightly higher actual FDR than our proposed methods. In addition, the results from an analysis based on the qualitative characteristics of sample distribution for MAQC2 and human breast cancer datasets show that only our gene-wise normalization methods corrected data skewed towards lower read counts. However, when we evaluated MAQC3 with less variation in five replicates, all methods performed similarly. Thus, our proposed Med-pgQ2 and UQ-pgQ2 methods perform slightly better for differential gene analysis of RNA-seq data skewed towards lowly expressed read counts with high variation by improving specificity while maintaining a good detection power with a control of the nominal FDR level.
A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data

PubMed Central

Li, Xiaohong; Brock, Guy N.; Rouchka, Eric C.; Cooper, Nigel G. F.; Wu, Dongfeng; O’Toole, Timothy E.; Gill, Ryan S.; Eteleeb, Abdallah M.; O’Brien, Liz

2017-01-01

Normalization is an essential step with considerable impact on high-throughput RNA sequencing (RNA-seq) data analysis. Although there are numerous methods for read count normalization, it remains a challenge to choose an optimal method due to multiple factors contributing to read count variability that affects the overall sensitivity and specificity. In order to properly determine the most appropriate normalization methods, it is critical to compare the performance and shortcomings of a representative set of normalization routines based on different dataset characteristics. Therefore, we set out to evaluate the performance of the commonly used methods (DESeq, TMM-edgeR, FPKM-CuffDiff, TC, Med UQ and FQ) and two new methods we propose: Med-pgQ2 and UQ-pgQ2 (per-gene normalization after per-sample median or upper-quartile global scaling). Our per-gene normalization approach allows for comparisons between conditions based on similar count levels. Using the benchmark Microarray Quality Control Project (MAQC) and simulated datasets, we performed differential gene expression analysis to evaluate these methods. When evaluating MAQC2 with two replicates, we observed that Med-pgQ2 and UQ-pgQ2 achieved a slightly higher area under the Receiver Operating Characteristic Curve (AUC), a specificity rate > 85%, the detection power > 92% and an actual false discovery rate (FDR) under 0.06 given the nominal FDR (≤0.05). Although the top commonly used methods (DESeq and TMM-edgeR) yield a higher power (>93%) for MAQC2 data, they trade off with a reduced specificity (<70%) and a slightly higher actual FDR than our proposed methods. In addition, the results from an analysis based on the qualitative characteristics of sample distribution for MAQC2 and human breast cancer datasets show that only our gene-wise normalization methods corrected data skewed towards lower read counts. However, when we evaluated MAQC3 with less variation in five replicates, all methods performed similarly. Thus, our proposed Med-pgQ2 and UQ-pgQ2 methods perform slightly better for differential gene analysis of RNA-seq data skewed towards lowly expressed read counts with high variation by improving specificity while maintaining a good detection power with a control of the nominal FDR level. PMID:28459823

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