Functional Annotation of the Arabidopsis Genome Using Controlled Vocabularies1

PubMed Central

Berardini, Tanya Z.; Mundodi, Suparna; Reiser, Leonore; Huala, Eva; Garcia-Hernandez, Margarita; Zhang, Peifen; Mueller, Lukas A.; Yoon, Jungwoon; Doyle, Aisling; Lander, Gabriel; Moseyko, Nick; Yoo, Danny; Xu, Iris; Zoeckler, Brandon; Montoya, Mary; Miller, Neil; Weems, Dan; Rhee, Seung Y.

2004-01-01

Controlled vocabularies are increasingly used by databases to describe genes and gene products because they facilitate identification of similar genes within an organism or among different organisms. One of The Arabidopsis Information Resource's goals is to associate all Arabidopsis genes with terms developed by the Gene Ontology Consortium that describe the molecular function, biological process, and subcellular location of a gene product. We have also developed terms describing Arabidopsis anatomy and developmental stages and use these to annotate published gene expression data. As of March 2004, we used computational and manual annotation methods to make 85,666 annotations representing 26,624 unique loci. We focus on associating genes to controlled vocabulary terms based on experimental data from the literature and use The Arabidopsis Information Resource-developed PubSearch software to facilitate this process. Each annotation is tagged with a combination of evidence codes, evidence descriptions, and references that provide a robust means to assess data quality. Annotation of all Arabidopsis genes will allow quantitative comparisons between sets of genes derived from sources such as microarray experiments. The Arabidopsis annotation data will also facilitate annotation of newly sequenced plant genomes by using sequence similarity to transfer annotations to homologous genes. In addition, complete and up-to-date annotations will make unknown genes easy to identify and target for experimentation. Here, we describe the process of Arabidopsis functional annotation using a variety of data sources and illustrate several ways in which this information can be accessed and used to infer knowledge about Arabidopsis and other plant species. PMID:15173566

A guide to best practices for Gene Ontology (GO) manual annotation

PubMed Central

Balakrishnan, Rama; Harris, Midori A.; Huntley, Rachael; Van Auken, Kimberly; Cherry, J. Michael

2013-01-01

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374 000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all. Database URL: http://www.geneontology.org PMID:23842463

Metagenomic gene annotation by a homology-independent approach

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

Froula, Jeff; Zhang, Tao; Salmeen, Annette

2011-06-02

Fully understanding the genetic potential of a microbial community requires functional annotation of all the genes it encodes. The recently developed deep metagenome sequencing approach has enabled rapid identification of millions of genes from a complex microbial community without cultivation. Current homology-based gene annotation fails to detect distantly-related or structural homologs. Furthermore, homology searches with millions of genes are very computational intensive. To overcome these limitations, we developed rhModeller, a homology-independent software pipeline to efficiently annotate genes from metagenomic sequencing projects. Using cellulases and carbonic anhydrases as two independent test cases, we demonstrated that rhModeller is much faster than HMMERmore » but with comparable accuracy, at 94.5percent and 99.9percent accuracy, respectively. More importantly, rhModeller has the ability to detect novel proteins that do not share significant homology to any known protein families. As {approx}50percent of the 2 million genes derived from the cow rumen metagenome failed to be annotated based on sequence homology, we tested whether rhModeller could be used to annotate these genes. Preliminary results suggest that rhModeller is robust in the presence of missense and frameshift mutations, two common errors in metagenomic genes. Applying the pipeline to the cow rumen genes identified 4,990 novel cellulases candidates and 8,196 novel carbonic anhydrase candidates.In summary, we expect rhModeller to dramatically increase the speed and quality of metagnomic gene annotation.« less

Gene Ontology annotations at SGD: new data sources and annotation methods

PubMed Central

Hong, Eurie L.; Balakrishnan, Rama; Dong, Qing; Christie, Karen R.; Park, Julie; Binkley, Gail; Costanzo, Maria C.; Dwight, Selina S.; Engel, Stacia R.; Fisk, Dianna G.; Hirschman, Jodi E.; Hitz, Benjamin C.; Krieger, Cynthia J.; Livstone, Michael S.; Miyasato, Stuart R.; Nash, Robert S.; Oughtred, Rose; Skrzypek, Marek S.; Weng, Shuai; Wong, Edith D.; Zhu, Kathy K.; Dolinski, Kara; Botstein, David; Cherry, J. Michael

2008-01-01

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current. PMID:17982175

JGI Plant Genomics Gene Annotation Pipeline

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

Shu, Shengqiang; Rokhsar, Dan; Goodstein, David

2014-07-14

Plant genomes vary in size and are highly complex with a high amount of repeats, genome duplication and tandem duplication. Gene encodes a wealth of information useful in studying organism and it is critical to have high quality and stable gene annotation. Thanks to advancement of sequencing technology, many plant species genomes have been sequenced and transcriptomes are also sequenced. To use these vastly large amounts of sequence data to make gene annotation or re-annotation in a timely fashion, an automatic pipeline is needed. JGI plant genomics gene annotation pipeline, called integrated gene call (IGC), is our effort toward thismore » aim with aid of a RNA-seq transcriptome assembly pipeline. It utilizes several gene predictors based on homolog peptides and transcript ORFs. See Methods for detail. Here we present genome annotation of JGI flagship green plants produced by this pipeline plus Arabidopsis and rice except for chlamy which is done by a third party. The genome annotations of these species and others are used in our gene family build pipeline and accessible via JGI Phytozome portal whose URL and front page snapshot are shown below.« less

dbWFA: a web-based database for functional annotation of Triticum aestivum transcripts

PubMed Central

Vincent, Jonathan; Dai, Zhanwu; Ravel, Catherine; Choulet, Frédéric; Mouzeyar, Said; Bouzidi, M. Fouad; Agier, Marie; Martre, Pierre

2013-01-01

The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L.). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L.) Heynh. and Oryza sativa L., was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families). The use of dbWFA is illustrated here with several query examples. We were able to assign a putative function to 45% of the UniGenes and 81% of the full-length coding sequences from TriFLDB. Moreover, comparison of the annotation of the whole T. aestivum UniGene set along with curated annotations of the two model species assessed the accuracy of the annotation provided by dbWFA. To further illustrate the use of dbWFA, genes specifically expressed during the early cell division or late storage polymer accumulation phases of T. aestivum grain development were identified using a clustering analysis and then annotated using dbWFA. The annotation of these two sets of genes was consistent with previous analyses of T. aestivum grain transcriptomes and proteomes. Database URL: urgi.versailles.inra.fr/dbWFA/ PMID:23660284

Discovering gene annotations in biomedical text databases.

PubMed

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-03-06

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively

Considerations to improve functional annotations in biological databases.

PubMed

Benítez-Páez, Alfonso

2009-12-01

Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community.

Gene Ontology annotation of the rice blast fungus, Magnaporthe oryzae

PubMed Central

Meng, Shaowu; Brown, Douglas E; Ebbole, Daniel J; Torto-Alalibo, Trudy; Oh, Yeon Yee; Deng, Jixin; Mitchell, Thomas K; Dean, Ralph A

2009-01-01

Background Magnaporthe oryzae, the causal agent of blast disease of rice, is the most destructive disease of rice worldwide. The genome of this fungal pathogen has been sequenced and an automated annotation has recently been updated to Version 6 . However, a comprehensive manual curation remains to be performed. Gene Ontology (GO) annotation is a valuable means of assigning functional information using standardized vocabulary. We report an overview of the GO annotation for Version 5 of M. oryzae genome assembly. Methods A similarity-based (i.e., computational) GO annotation with manual review was conducted, which was then integrated with a literature-based GO annotation with computational assistance. For similarity-based GO annotation a stringent reciprocal best hits method was used to identify similarity between predicted proteins of M. oryzae and GO proteins from multiple organisms with published associations to GO terms. Significant alignment pairs were manually reviewed. Functional assignments were further cross-validated with manually reviewed data, conserved domains, or data determined by wet lab experiments. Additionally, biological appropriateness of the functional assignments was manually checked. Results In total, 6,286 proteins received GO term assignment via the homology-based annotation, including 2,870 hypothetical proteins. Literature-based experimental evidence, such as microarray, MPSS, T-DNA insertion mutation, or gene knockout mutation, resulted in 2,810 proteins being annotated with GO terms. Of these, 1,673 proteins were annotated with new terms developed for Plant-Associated Microbe Gene Ontology (PAMGO). In addition, 67 experiment-determined secreted proteins were annotated with PAMGO terms. Integration of the two data sets resulted in 7,412 proteins (57%) being annotated with 1,957 distinct and specific GO terms. Unannotated proteins were assigned to the 3 root terms. The Version 5 GO annotation is publically queryable via the GO site

Discovering gene annotations in biomedical text databases

PubMed Central

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-01-01

Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with

Evidence-based gene models for structural and functional annotations of the oil palm genome.

PubMed

Chan, Kuang-Lim; Tatarinova, Tatiana V; Rosli, Rozana; Amiruddin, Nadzirah; Azizi, Norazah; Halim, Mohd Amin Ab; Sanusi, Nik Shazana Nik Mohd; Jayanthi, Nagappan; Ponomarenko, Petr; Triska, Martin; Solovyev, Victor; Firdaus-Raih, Mohd; Sambanthamurthi, Ravigadevi; Murphy, Denis; Low, Eng-Ti Leslie

2017-09-08

Oil palm is an important source of edible oil. The importance of the crop, as well as its long breeding cycle (10-12 years) has led to the sequencing of its genome in 2013 to pave the way for genomics-guided breeding. Nevertheless, the first set of gene predictions, although useful, had many fragmented genes. Classification and characterization of genes associated with traits of interest, such as those for fatty acid biosynthesis and disease resistance, were also limited. Lipid-, especially fatty acid (FA)-related genes are of particular interest for the oil palm as they specify oil yields and quality. This paper presents the characterization of the oil palm genome using different gene prediction methods and comparative genomics analysis, identification of FA biosynthesis and disease resistance genes, and the development of an annotation database and bioinformatics tools. Using two independent gene-prediction pipelines, Fgenesh++ and Seqping, 26,059 oil palm genes with transcriptome and RefSeq support were identified from the oil palm genome. These coding regions of the genome have a characteristic broad distribution of GC 3 (fraction of cytosine and guanine in the third position of a codon) with over half the GC 3 -rich genes (GC 3  ≥ 0.75286) being intronless. In comparison, only one-seventh of the oil palm genes identified are intronless. Using comparative genomics analysis, characterization of conserved domains and active sites, and expression analysis, 42 key genes involved in FA biosynthesis in oil palm were identified. For three of them, namely EgFABF, EgFABH and EgFAD3, segmental duplication events were detected. Our analysis also identified 210 candidate resistance genes in six classes, grouped by their protein domain structures. We present an accurate and comprehensive annotation of the oil palm genome, focusing on analysis of important categories of genes (GC 3 -rich and intronless), as well as those associated with important functions, such as FA

PANNZER2: a rapid functional annotation web server.

PubMed

Törönen, Petri; Medlar, Alan; Holm, Liisa

2018-05-08

The unprecedented growth of high-throughput sequencing has led to an ever-widening annotation gap in protein databases. While computational prediction methods are available to make up the shortfall, a majority of public web servers are hindered by practical limitations and poor performance. Here, we introduce PANNZER2 (Protein ANNotation with Z-scoRE), a fast functional annotation web server that provides both Gene Ontology (GO) annotations and free text description predictions. PANNZER2 uses SANSparallel to perform high-performance homology searches, making bulk annotation based on sequence similarity practical. PANNZER2 can output GO annotations from multiple scoring functions, enabling users to see which predictions are robust across predictors. Finally, PANNZER2 predictions scored within the top 10 methods for molecular function and biological process in the CAFA2 NK-full benchmark. The PANNZER2 web server is updated on a monthly schedule and is accessible at http://ekhidna2.biocenter.helsinki.fi/sanspanz/. The source code is available under the GNU Public Licence v3.

GARNET--gene set analysis with exploration of annotation relations.

PubMed

Rho, Kyoohyoung; Kim, Bumjin; Jang, Youngjun; Lee, Sanghyun; Bae, Taejeong; Seo, Jihae; Seo, Chaehwa; Lee, Jihyun; Kang, Hyunjung; Yu, Ungsik; Kim, Sunghoon; Lee, Sanghyuk; Kim, Wan Kyu

2011-02-15

Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).

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

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

Computational annotation of genes differentially expressed along olive fruit development

PubMed Central

Galla, Giulio; Barcaccia, Gianni; Ramina, Angelo; Collani, Silvio; Alagna, Fiammetta; Baldoni, Luciana; Cultrera, Nicolò GM; Martinelli, Federico; Sebastiani, Luca; Tonutti, Pietro

2009-01-01

Background Olea europaea L. is a traditional tree crop of the Mediterranean basin with a worldwide economical high impact. Differently from other fruit tree species, little is known about the physiological and molecular basis of the olive fruit development and a few sequences of genes and gene products are available for olive in public databases. This study deals with the identification of large sets of differentially expressed genes in developing olive fruits and the subsequent computational annotation by means of different software. Results mRNA from fruits of the cv. Leccino sampled at three different stages [i.e., initial fruit set (stage 1), completed pit hardening (stage 2) and veraison (stage 3)] was used for the identification of differentially expressed genes putatively involved in main processes along fruit development. Four subtractive hybridization libraries were constructed: forward and reverse between stage 1 and 2 (libraries A and B), and 2 and 3 (libraries C and D). All sequenced clones (1,132 in total) were analyzed through BlastX against non-redundant NCBI databases and about 60% of them showed similarity to known proteins. A total of 89 out of 642 differentially expressed unique sequences was further investigated by Real-Time PCR, showing a validation of the SSH results as high as 69%. Library-specific cDNA repertories were annotated according to the three main vocabularies of the gene ontology (GO): cellular component, biological process and molecular function. BlastX analysis, GO terms mapping and annotation analysis were performed using the Blast2GO software, a research tool designed with the main purpose of enabling GO based data mining on sequence sets for which no GO annotation is yet available. Bioinformatic analysis pointed out a significantly different distribution of the annotated sequences for each GO category, when comparing the three fruit developmental stages. The olive fruit-specific transcriptome dataset was used to query all

Saccharomyces cerevisiae: gene annotation and genome variability, state of the art through comparative genomics.

PubMed

Louis, Ed

2011-01-01

In the early days of the yeast genome sequencing project, gene annotation was in its infancy and suffered the problem of many false positive annotations as well as missed genes. The lack of other sequences for comparison also prevented the annotation of conserved, functional sequences that were not coding. We are now in an era of comparative genomics where many closely related as well as more distantly related genomes are available for direct sequence and synteny comparisons allowing for more probable predictions of genes and other functional sequences due to conservation. We also have a plethora of functional genomics data which helps inform gene annotation for previously uncharacterised open reading frames (ORFs)/genes. For Saccharomyces cerevisiae this has resulted in a continuous updating of the gene and functional sequence annotations in the reference genome helping it retain its position as the best characterized eukaryotic organism's genome. A single reference genome for a species does not accurately describe the species and this is quite clear in the case of S. cerevisiae where the reference strain is not ideal for brewing or baking due to missing genes. Recent surveys of numerous isolates, from a variety of sources, using a variety of technologies have revealed a great deal of variation amongst isolates with genome sequence surveys providing information on novel genes, undetectable by other means. We now have a better understanding of the extant variation in S. cerevisiae as a species as well as some idea of how much we are missing from this understanding. As with gene annotation, comparative genomics enhances the discovery and description of genome variation and is providing us with the tools for understanding genome evolution, adaptation and selection, and underlying genetics of complex traits.

Genome-wide profiling of 24 hr diel rhythmicity in the water flea, Daphnia pulex: network analysis reveals rhythmic gene expression and enhances functional gene annotation.

PubMed

Rund, Samuel S C; Yoo, Boyoung; Alam, Camille; Green, Taryn; Stephens, Melissa T; Zeng, Erliang; George, Gary F; Sheppard, Aaron D; Duffield, Giles E; Milenković, Tijana; Pfrender, Michael E

2016-08-18

Marine and freshwater zooplankton exhibit daily rhythmic patterns of behavior and physiology which may be regulated directly by the light:dark (LD) cycle and/or a molecular circadian clock. One of the best-studied zooplankton taxa, the freshwater crustacean Daphnia, has a 24 h diel vertical migration (DVM) behavior whereby the organism travels up and down through the water column daily. DVM plays a critical role in resource tracking and the behavioral avoidance of predators and damaging ultraviolet radiation. However, there is little information at the transcriptional level linking the expression patterns of genes to the rhythmic physiology/behavior of Daphnia. Here we analyzed genome-wide temporal transcriptional patterns from Daphnia pulex collected over a 44 h time period under a 12:12 LD cycle (diel) conditions using a cosine-fitting algorithm. We used a comprehensive network modeling and analysis approach to identify novel co-regulated rhythmic genes that have similar network topological properties and functional annotations as rhythmic genes identified by the cosine-fitting analyses. Furthermore, we used the network approach to predict with high accuracy novel gene-function associations, thus enhancing current functional annotations available for genes in this ecologically relevant model species. Our results reveal that genes in many functional groupings exhibit 24 h rhythms in their expression patterns under diel conditions. We highlight the rhythmic expression of immunity, oxidative detoxification, and sensory process genes. We discuss differences in the chronobiology of D. pulex from other well-characterized terrestrial arthropods. This research adds to a growing body of literature suggesting the genetic mechanisms governing rhythmicity in crustaceans may be divergent from other arthropod lineages including insects. Lastly, these results highlight the power of using a network analysis approach to identify differential gene expression and provide novel

AutoFACT: An Automatic Functional Annotation and Classification Tool

PubMed Central

Koski, Liisa B; Gray, Michael W; Lang, B Franz; Burger, Gertraud

2005-01-01

Background Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. Results We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. Conclusion AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at . PMID:15960857

Transcriptome Assembly, Gene Annotation and Tissue Gene Expression Atlas of the Rainbow Trout

PubMed Central

Salem, Mohamed; Paneru, Bam; Al-Tobasei, Rafet; Abdouni, Fatima; Thorgaard, Gary H.; Rexroad, Caird E.; Yao, Jianbo

2015-01-01

Efforts to obtain a comprehensive genome sequence for rainbow trout are ongoing and will be complemented by transcriptome information that will enhance genome assembly and annotation. Previously, transcriptome reference sequences were reported using data from different sources. Although the previous work added a great wealth of sequences, a complete and well-annotated transcriptome is still needed. In addition, gene expression in different tissues was not completely addressed in the previous studies. In this study, non-normalized cDNA libraries were sequenced from 13 different tissues of a single doubled haploid rainbow trout from the same source used for the rainbow trout genome sequence. A total of ~1.167 billion paired-end reads were de novo assembled using the Trinity RNA-Seq assembler yielding 474,524 contigs > 500 base-pairs. Of them, 287,593 had homologies to the NCBI non-redundant protein database. The longest contig of each cluster was selected as a reference, yielding 44,990 representative contigs. A total of 4,146 contigs (9.2%), including 710 full-length sequences, did not match any mRNA sequences in the current rainbow trout genome reference. Mapping reads to the reference genome identified an additional 11,843 transcripts not annotated in the genome. A digital gene expression atlas revealed 7,678 housekeeping and 4,021 tissue-specific genes. Expression of about 16,000–32,000 genes (35–71% of the identified genes) accounted for basic and specialized functions of each tissue. White muscle and stomach had the least complex transcriptomes, with high percentages of their total mRNA contributed by a small number of genes. Brain, testis and intestine, in contrast, had complex transcriptomes, with a large numbers of genes involved in their expression patterns. This study provides comprehensive de novo transcriptome information that is suitable for functional and comparative genomics studies in rainbow trout, including annotation of the genome. PMID

ADGO: analysis of differentially expressed gene sets using composite GO annotation.

PubMed

Nam, Dougu; Kim, Sang-Bae; Kim, Seon-Kyu; Yang, Sungjin; Kim, Seon-Young; Chu, In-Sun

2006-09-15

Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. chu@kribb.re.kr http://array.kobic.re.kr/ADGO.

GFam: a platform for automatic annotation of gene families.

PubMed

Sasidharan, Rajkumar; Nepusz, Tamás; Swarbreck, David; Huala, Eva; Paccanaro, Alberto

2012-10-01

We have developed GFam, a platform for automatic annotation of gene/protein families. GFam provides a framework for genome initiatives and model organism resources to build domain-based families, derive meaningful functional labels and offers a seamless approach to propagate functional annotation across periodic genome updates. GFam is a hybrid approach that uses a greedy algorithm to chain component domains from InterPro annotation provided by its 12 member resources followed by a sequence-based connected component analysis of un-annotated sequence regions to derive consensus domain architecture for each sequence and subsequently generate families based on common architectures. Our integrated approach increases sequence coverage by 7.2 percentage points and residue coverage by 14.6 percentage points higher than the coverage relative to the best single-constituent database within InterPro for the proteome of Arabidopsis. The true power of GFam lies in maximizing annotation provided by the different InterPro data sources that offer resource-specific coverage for different regions of a sequence. GFam's capability to capture higher sequence and residue coverage can be useful for genome annotation, comparative genomics and functional studies. GFam is a general-purpose software and can be used for any collection of protein sequences. The software is open source and can be obtained from http://www.paccanarolab.org/software/gfam/.

Cross-organism learning method to discover new gene functionalities.

PubMed

Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

2016-04-01

Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones

De novo RNA-seq and functional annotation of Ornithonyssus bacoti.

PubMed

Niu, DongLing; Wang, RuiLing; Zhao, YaE; Yang, Rui; Hu, Li

2018-06-01

Ornithonyssus bacoti (Hirst) (Acari: Macronyssidae) is a vector and reservoir of pathogens causing serious infectious diseases, such as epidemic hemorrhagic fever, endemic typhus, tularemia, and leptospirosis. Its genome and transcriptome data are lacking in public databases. In this study, total RNA was extracted from live O. bacoti to conduct RNA-seq, functional annotation, coding domain sequence (CDS) prediction and simple sequence repeats (SSRs) detection. The results showed that 65.8 million clean reads were generated and assembled into 72,185 unigenes, of which 49.4% were annotated by seven functional databases. 23,121 unigenes were annotated and assigned to 457 species by non-redundant protein sequence database. The BLAST top-two hit species were Metaseiulus occidentalis and Ixodes scapularis. The procedure detected 12,426 SSRs, of which tri- and di-nucleotides were the most abundant types and the representative motifs were AAT/ATT and AC/GT. 26,936 CDS were predicted with a mean length of 711 bp. 87 unigenes of 30 functional genes, which are usually involved in stress responses, drug resistance, movement, metabolism and allergy, were further identified by bioinformatics methods. The unigenes putatively encoding cytochrome P450 proteins were further analyzed phylogenetically. In conclusion, this study completed the RNA-seq and functional annotation of O. bacoti successfully, which provides reliable molecular data for its future studies of gene function and molecular markers.

Genome-wide annotation of the soybean WRKY family and functional characterization of genes involved in response to Phakopsora pachyrhizi infection.

PubMed

Bencke-Malato, Marta; Cabreira, Caroline; Wiebke-Strohm, Beatriz; Bücker-Neto, Lauro; Mancini, Estefania; Osorio, Marina B; Homrich, Milena S; Turchetto-Zolet, Andreia Carina; De Carvalho, Mayra C C G; Stolf, Renata; Weber, Ricardo L M; Westergaard, Gastón; Castagnaro, Atílio P; Abdelnoor, Ricardo V; Marcelino-Guimarães, Francismar C; Margis-Pinheiro, Márcia; Bodanese-Zanettini, Maria Helena

2014-09-10

Many previous studies have shown that soybean WRKY transcription factors are involved in the plant response to biotic and abiotic stresses. Phakopsora pachyrhizi is the causal agent of Asian Soybean Rust, one of the most important soybean diseases. There are evidences that WRKYs are involved in the resistance of some soybean genotypes against that fungus. The number of WRKY genes already annotated in soybean genome was underrepresented. In the present study, a genome-wide annotation of the soybean WRKY family was carried out and members involved in the response to P. pachyrhizi were identified. As a result of a soybean genomic databases search, 182 WRKY-encoding genes were annotated and 33 putative pseudogenes identified. Genes involved in the response to P. pachyrhizi infection were identified using superSAGE, RNA-Seq of microdissected lesions and microarray experiments. Seventy-five genes were differentially expressed during fungal infection. The expression of eight WRKY genes was validated by RT-qPCR. The expression of these genes in a resistant genotype was earlier and/or stronger compared with a susceptible genotype in response to P. pachyrhizi infection. Soybean somatic embryos were transformed in order to overexpress or silence WRKY genes. Embryos overexpressing a WRKY gene were obtained, but they were unable to convert into plants. When infected with P. pachyrhizi, the leaves of the silenced transgenic line showed a higher number of lesions than the wild-type plants. The present study reports a genome-wide annotation of soybean WRKY family. The participation of some members in response to P. pachyrhizi infection was demonstrated. The results contribute to the elucidation of gene function and suggest the manipulation of WRKYs as a strategy to increase fungal resistance in soybean plants.

Mutant phenotypes for thousands of bacterial genes of unknown function

DOE PAGES

Price, Morgan N.; Wetmore, Kelly M.; Waters, R. Jordan; ...

2018-05-16

One-third of all protein-coding genes from bacterial genomes cannot be annotated with a function. Here, to investigate the functions of these genes, we present genome-wide mutant fitness data from 32 diverse bacteria across dozens of growth conditions. We identified mutant phenotypes for 11,779 protein-coding genes that had not been annotated with a specific function. Many genes could be associated with a specific condition because the gene affected fitness only in that condition, or with another gene in the same bacterium because they had similar mutant phenotypes. Of the poorly annotated genes, 2,316 had associations that have high confidence because theymore » are conserved in other bacteria. By combining these conserved associations with comparative genomics, we identified putative DNA repair proteins; in addition, we propose specific functions for poorly annotated enzymes and transporters and for uncharacterized protein families. Lastly, our study demonstrates the scalability of microbial genetics and its utility for improving gene annotations.« less

Mutant phenotypes for thousands of bacterial genes of unknown function

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

Price, Morgan N.; Wetmore, Kelly M.; Waters, R. Jordan

One-third of all protein-coding genes from bacterial genomes cannot be annotated with a function. Here, to investigate the functions of these genes, we present genome-wide mutant fitness data from 32 diverse bacteria across dozens of growth conditions. We identified mutant phenotypes for 11,779 protein-coding genes that had not been annotated with a specific function. Many genes could be associated with a specific condition because the gene affected fitness only in that condition, or with another gene in the same bacterium because they had similar mutant phenotypes. Of the poorly annotated genes, 2,316 had associations that have high confidence because theymore » are conserved in other bacteria. By combining these conserved associations with comparative genomics, we identified putative DNA repair proteins; in addition, we propose specific functions for poorly annotated enzymes and transporters and for uncharacterized protein families. Lastly, our study demonstrates the scalability of microbial genetics and its utility for improving gene annotations.« less

Gene calling and bacterial genome annotation with BG7.

PubMed

Tobes, Raquel; Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Kovach, Evdokim; Alekhin, Alexey; Pareja, Eduardo

2015-01-01

New massive sequencing technologies are providing many bacterial genome sequences from diverse taxa but a refined annotation of these genomes is crucial for obtaining scientific findings and new knowledge. Thus, bacterial genome annotation has emerged as a key point to investigate in bacteria. Any efficient tool designed specifically to annotate bacterial genomes sequenced with massively parallel technologies has to consider the specific features of bacterial genomes (absence of introns and scarcity of nonprotein-coding sequence) and of next-generation sequencing (NGS) technologies (presence of errors and not perfectly assembled genomes). These features make it convenient to focus on coding regions and, hence, on protein sequences that are the elements directly related with biological functions. In this chapter we describe how to annotate bacterial genomes with BG7, an open-source tool based on a protein-centered gene calling/annotation paradigm. BG7 is specifically designed for the annotation of bacterial genomes sequenced with NGS. This tool is sequence error tolerant maintaining their capabilities for the annotation of highly fragmented genomes or for annotating mixed sequences coming from several genomes (as those obtained through metagenomics samples). BG7 has been designed with scalability as a requirement, with a computing infrastructure completely based on cloud computing (Amazon Web Services).

The Co-regulation Data Harvester: Automating gene annotation starting from a transcriptome database

NASA Astrophysics Data System (ADS)

Tsypin, Lev M.; Turkewitz, Aaron P.

Identifying co-regulated genes provides a useful approach for defining pathway-specific machinery in an organism. To be efficient, this approach relies on thorough genome annotation, a process much slower than genome sequencing per se. Tetrahymena thermophila, a unicellular eukaryote, has been a useful model organism and has a fully sequenced but sparsely annotated genome. One important resource for studying this organism has been an online transcriptomic database. We have developed an automated approach to gene annotation in the context of transcriptome data in T. thermophila, called the Co-regulation Data Harvester (CDH). Beginning with a gene of interest, the CDH identifies co-regulated genes by accessing the Tetrahymena transcriptome database. It then identifies their closely related genes (orthologs) in other organisms by using reciprocal BLAST searches. Finally, it collates the annotations of those orthologs' functions, which provides the user with information to help predict the cellular role of the initial query. The CDH, which is freely available, represents a powerful new tool for analyzing cell biological pathways in Tetrahymena. Moreover, to the extent that genes and pathways are conserved between organisms, the inferences obtained via the CDH should be relevant, and can be explored, in many other systems.

Evaluation and integration of functional annotation pipelines for newly sequenced organisms: the potato genome as a test case.

PubMed

Amar, David; Frades, Itziar; Danek, Agnieszka; Goldberg, Tatyana; Sharma, Sanjeev K; Hedley, Pete E; Proux-Wera, Estelle; Andreasson, Erik; Shamir, Ron; Tzfadia, Oren; Alexandersson, Erik

2014-12-05

For most organisms, even if their genome sequence is available, little functional information about individual genes or proteins exists. Several annotation pipelines have been developed for functional analysis based on sequence, 'omics', and literature data. However, researchers encounter little guidance on how well they perform. Here, we used the recently sequenced potato genome as a case study. The potato genome was selected since its genome is newly sequenced and it is a non-model plant even if there is relatively ample information on individual potato genes, and multiple gene expression profiles are available. We show that the automatic gene annotations of potato have low accuracy when compared to a "gold standard" based on experimentally validated potato genes. Furthermore, we evaluate six state-of-the-art annotation pipelines and show that their predictions are markedly dissimilar (Jaccard similarity coefficient of 0.27 between pipelines on average). To overcome this discrepancy, we introduce a simple GO structure-based algorithm that reconciles the predictions of the different pipelines. We show that the integrated annotation covers more genes, increases by over 50% the number of highly co-expressed GO processes, and obtains much higher agreement with the gold standard. We find that different annotation pipelines produce different results, and show how to integrate them into a unified annotation that is of higher quality than each single pipeline. We offer an improved functional annotation of both PGSC and ITAG potato gene models, as well as tools that can be applied to additional pipelines and improve annotation in other organisms. This will greatly aid future functional analysis of '-omics' datasets from potato and other organisms with newly sequenced genomes. The new potato annotations are available with this paper.

Dizeez: An Online Game for Human Gene-Disease Annotation

PubMed Central

Loguercio, Salvatore; Good, Benjamin M.; Su, Andrew I.

2013-01-01

Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org. PMID:23951102

Lynx web services for annotations and systems analysis of multi-gene disorders.

PubMed

Sulakhe, Dinanath; Taylor, Andrew; Balasubramanian, Sandhya; Feng, Bo; Xie, Bingqing; Börnigen, Daniela; Dave, Utpal J; Foster, Ian T; Gilliam, T Conrad; Maltsev, Natalia

2014-07-01

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes.

PubMed

Mende, Daniel R; Letunic, Ivica; Huerta-Cepas, Jaime; Li, Simone S; Forslund, Kristoffer; Sunagawa, Shinichi; Bork, Peer

2017-01-04

The availability of microbial genomes has opened many new avenues of research within microbiology. This has been driven primarily by comparative genomics approaches, which rely on accurate and consistent characterization of genomic sequences. It is nevertheless difficult to obtain consistent taxonomic and integrated functional annotations for defined prokaryotic clades. Thus, we developed proGenomes, a resource that provides user-friendly access to currently 25 038 high-quality genomes whose sequences and consistent annotations can be retrieved individually or by taxonomic clade. These genomes are assigned to 5306 consistent and accurate taxonomic species clusters based on previously established methodology. proGenomes also contains functional information for almost 80 million protein-coding genes, including a comprehensive set of general annotations and more focused annotations for carbohydrate-active enzymes and antibiotic resistance genes. Additionally, broad habitat information is provided for many genomes. All genomes and associated information can be downloaded by user-selected clade or multiple habitat-specific sets of representative genomes. We expect that the availability of high-quality genomes with comprehensive functional annotations will promote advances in clinical microbial genomics, functional evolution and other subfields of microbiology. proGenomes is available at http://progenomes.embl.de. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

PubMed Central

Seaver, Samuel M. D.; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M. T.; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D.; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D.; Henry, Christopher S.

2014-01-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today’s annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed. PMID:24927599

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource.

PubMed

Seaver, Samuel M D; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M T; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D; Henry, Christopher S

2014-07-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.

Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression data

PubMed Central

Bryan, Kenneth; Cunningham, Pádraig

2008-01-01

Background Microarrays have the capacity to measure the expressions of thousands of genes in parallel over many experimental samples. The unsupervised classification technique of bicluster analysis has been employed previously to uncover gene expression correlations over subsets of samples with the aim of providing a more accurate model of the natural gene functional classes. This approach also has the potential to aid functional annotation of unclassified open reading frames (ORFs). Until now this aspect of biclustering has been under-explored. In this work we illustrate how bicluster analysis may be extended into a 'semi-supervised' ORF annotation approach referred to as BALBOA. Results The efficacy of the BALBOA ORF classification technique is first assessed via cross validation and compared to a multi-class k-Nearest Neighbour (kNN) benchmark across three independent gene expression datasets. BALBOA is then used to assign putative functional annotations to unclassified yeast ORFs. These predictions are evaluated using existing experimental and protein sequence information. Lastly, we employ a related semi-supervised method to predict the presence of novel functional modules within yeast. Conclusion In this paper we demonstrate how unsupervised classification methods, such as bicluster analysis, may be extended using of available annotations to form semi-supervised approaches within the gene expression analysis domain. We show that such methods have the potential to improve upon supervised approaches and shed new light on the functions of unclassified ORFs and their co-regulation. PMID:18831786

Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae

PubMed Central

2013-01-01

Background Secondary metabolite production, a hallmark of filamentous fungi, is an expanding area of research for the Aspergilli. These compounds are potent chemicals, ranging from deadly toxins to therapeutic antibiotics to potential anti-cancer drugs. The genome sequences for multiple Aspergilli have been determined, and provide a wealth of predictive information about secondary metabolite production. Sequence analysis and gene overexpression strategies have enabled the discovery of novel secondary metabolites and the genes involved in their biosynthesis. The Aspergillus Genome Database (AspGD) provides a central repository for gene annotation and protein information for Aspergillus species. These annotations include Gene Ontology (GO) terms, phenotype data, gene names and descriptions and they are crucial for interpreting both small- and large-scale data and for aiding in the design of new experiments that further Aspergillus research. Results We have manually curated Biological Process GO annotations for all genes in AspGD with recorded functions in secondary metabolite production, adding new GO terms that specifically describe each secondary metabolite. We then leveraged these new annotations to predict roles in secondary metabolism for genes lacking experimental characterization. As a starting point for manually annotating Aspergillus secondary metabolite gene clusters, we used antiSMASH (antibiotics and Secondary Metabolite Analysis SHell) and SMURF (Secondary Metabolite Unknown Regions Finder) algorithms to identify potential clusters in A. nidulans, A. fumigatus, A. niger and A. oryzae, which we subsequently refined through manual curation. Conclusions This set of 266 manually curated secondary metabolite gene clusters will facilitate the investigation of novel Aspergillus secondary metabolites. PMID:23617571

Improved methods and resources for paramecium genomics: transcription units, gene annotation and gene expression.

PubMed

Arnaiz, Olivier; Van Dijk, Erwin; Bétermier, Mireille; Lhuillier-Akakpo, Maoussi; de Vanssay, Augustin; Duharcourt, Sandra; Sallet, Erika; Gouzy, Jérôme; Sperling, Linda

2017-06-26

The 15 sibling species of the Paramecium aurelia cryptic species complex emerged after a whole genome duplication that occurred tens of millions of years ago. Given extensive knowledge of the genetics and epigenetics of Paramecium acquired over the last century, this species complex offers a uniquely powerful system to investigate the consequences of whole genome duplication in a unicellular eukaryote as well as the genetic and epigenetic mechanisms that drive speciation. High quality Paramecium gene models are important for research using this system. The major aim of the work reported here was to build an improved gene annotation pipeline for the Paramecium lineage. We generated oriented RNA-Seq transcriptome data across the sexual process of autogamy for the model species Paramecium tetraurelia. We determined, for the first time in a ciliate, candidate P. tetraurelia transcription start sites using an adapted Cap-Seq protocol. We developed TrUC, multi-threaded Perl software that in conjunction with TopHat mapping of RNA-Seq data to a reference genome, predicts transcription units for the annotation pipeline. We used EuGene software to combine annotation evidence. The high quality gene structural annotations obtained for P. tetraurelia were used as evidence to improve published annotations for 3 other Paramecium species. The RNA-Seq data were also used for differential gene expression analysis, providing a gene expression atlas that is more sensitive than the previously established microarray resource. We have developed a gene annotation pipeline tailored for the compact genomes and tiny introns of Paramecium species. A novel component of this pipeline, TrUC, predicts transcription units using Cap-Seq and oriented RNA-Seq data. TrUC could prove useful beyond Paramecium, especially in the case of high gene density. Accurate predictions of 3' and 5' UTR will be particularly valuable for studies of gene expression (e.g. nucleosome positioning, identification of cis

microRNAs Databases: Developmental Methodologies, Structural and Functional Annotations.

PubMed

Singh, Nagendra Kumar

2017-09-01

microRNA (miRNA) is an endogenous and evolutionary conserved non-coding RNA, involved in post-transcriptional process as gene repressor and mRNA cleavage through RNA-induced silencing complex (RISC) formation. In RISC, miRNA binds in complementary base pair with targeted mRNA along with Argonaut proteins complex, causes gene repression or endonucleolytic cleavage of mRNAs and results in many diseases and syndromes. After the discovery of miRNA lin-4 and let-7, subsequently large numbers of miRNAs were discovered by low-throughput and high-throughput experimental techniques along with computational process in various biological and metabolic processes. The miRNAs are important non-coding RNA for understanding the complex biological phenomena of organism because it controls the gene regulation. This paper reviews miRNA databases with structural and functional annotations developed by various researchers. These databases contain structural and functional information of animal, plant and virus miRNAs including miRNAs-associated diseases, stress resistance in plant, miRNAs take part in various biological processes, effect of miRNAs interaction on drugs and environment, effect of variance on miRNAs, miRNAs gene expression analysis, sequence of miRNAs, structure of miRNAs. This review focuses on the developmental methodology of miRNA databases such as computational tools and methods used for extraction of miRNAs annotation from different resources or through experiment. This study also discusses the efficiency of user interface design of every database along with current entry and annotations of miRNA (pathways, gene ontology, disease ontology, etc.). Here, an integrated schematic diagram of construction process for databases is also drawn along with tabular and graphical comparison of various types of entries in different databases. Aim of this paper is to present the importance of miRNAs-related resources at a single place.

Functional sequencing read annotation for high precision microbiome analysis

PubMed Central

Zhu, Chengsheng; Miller, Maximilian; Marpaka, Srinayani; Vaysberg, Pavel; Rühlemann, Malte C; Wu, Guojun; Heinsen, Femke-Anouska; Tempel, Marie; Zhao, Liping; Lieb, Wolfgang; Franke, Andre; Bromberg, Yana

2018-01-01

Abstract The vast majority of microorganisms on Earth reside in often-inseparable environment-specific communities—microbiomes. Meta-genomic/-transcriptomic sequencing could reveal the otherwise inaccessible functionality of microbiomes. However, existing analytical approaches focus on attributing sequencing reads to known genes/genomes, often failing to make maximal use of available data. We created faser (functional annotation of sequencing reads), an algorithm that is optimized to map reads to molecular functions encoded by the read-correspondent genes. The mi-faser microbiome analysis pipeline, combining faser with our manually curated reference database of protein functions, accurately annotates microbiome molecular functionality. mi-faser’s minutes-per-microbiome processing speed is significantly faster than that of other methods, allowing for large scale comparisons. Microbiome function vectors can be compared between different conditions to highlight environment-specific and/or time-dependent changes in functionality. Here, we identified previously unseen oil degradation-specific functions in BP oil-spill data, as well as functional signatures of individual-specific gut microbiome responses to a dietary intervention in children with Prader–Willi syndrome. Our method also revealed variability in Crohn's Disease patient microbiomes and clearly distinguished them from those of related healthy individuals. Our analysis highlighted the microbiome role in CD pathogenicity, demonstrating enrichment of patient microbiomes in functions that promote inflammation and that help bacteria survive it. PMID:29194524

BEACON: automated tool for Bacterial GEnome Annotation ComparisON.

PubMed

Kalkatawi, Manal; Alam, Intikhab; Bajic, Vladimir B

2015-08-18

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs). The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON's utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27%, while the number of genes without any function assignment is reduced. We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/ .

Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function.

PubMed

Busk, P K; Pilgaard, B; Lezyk, M J; Meyer, A S; Lange, L

2017-04-12

Carbohydrate-active enzymes are found in all organisms and participate in key biological processes. These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fast and reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interest as demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach not only assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy. We identified conserved peptides for all enzyme families in the CAZy database with Peptide Pattern Recognition. The conserved peptides were matched to protein sequence for de novo annotation and functional prediction of carbohydrate-active enzymes with the Hotpep method. Annotation of protein sequences from 12 bacterial and 16 fungal genomes to families with Hotpep had an accuracy of 0.84 (measured as F1-score) compared to semiautomatic annotation by the CAZy database whereas the dbCAN HMM-based method had an accuracy of 0.77 with optimized parameters. Furthermore, Hotpep provided a functional prediction with 86% accuracy for the annotated genes. Hotpep is available as a stand-alone application for MS Windows. Hotpep is a state-of-the-art method for automatic annotation and functional prediction of carbohydrate-active enzymes.

High-performance web services for querying gene and variant annotation.

PubMed

Xin, Jiwen; Mark, Adam; Afrasiabi, Cyrus; Tsueng, Ginger; Juchler, Moritz; Gopal, Nikhil; Stupp, Gregory S; Putman, Timothy E; Ainscough, Benjamin J; Griffith, Obi L; Torkamani, Ali; Whetzel, Patricia L; Mungall, Christopher J; Mooney, Sean D; Su, Andrew I; Wu, Chunlei

2016-05-06

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times permonth. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info . Both are offered free of charge to the research community.

Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator.

PubMed

Tchechmedjiev, Andon; Abdaoui, Amine; Emonet, Vincent; Melzi, Soumia; Jonnagaddala, Jitendra; Jonquet, Clement

2018-06-01

Second use of clinical data commonly involves annotating biomedical text with terminologies and ontologies. The National Center for Biomedical Ontology Annotator is a frequently used annotation service, originally designed for biomedical data, but not very suitable for clinical text annotation. In order to add new functionalities to the NCBO Annotator without hosting or modifying the original Web service, we have designed a proxy architecture that enables seamless extensions by pre-processing of the input text and parameters, and post processing of the annotations. We have then implemented enhanced functionalities for annotating and indexing free text such as: scoring, detection of context (negation, experiencer, temporality), new output formats and coarse-grained concept recognition (with UMLS Semantic Groups). In this paper, we present the NCBO Annotator+, a Web service which incorporates these new functionalities as well as a small set of evaluation results for concept recognition and clinical context detection on two standard evaluation tasks (Clef eHealth 2017, SemEval 2014). The Annotator+ has been successfully integrated into the SIFR BioPortal platform-an implementation of NCBO BioPortal for French biomedical terminologies and ontologies-to annotate English text. A Web user interface is available for testing and ontology selection (http://bioportal.lirmm.fr/ncbo_annotatorplus); however the Annotator+ is meant to be used through the Web service application programming interface (http://services.bioportal.lirmm.fr/ncbo_annotatorplus). The code is openly available, and we also provide a Docker packaging to enable easy local deployment to process sensitive (e.g. clinical) data in-house (https://github.com/sifrproject). andon.tchechmedjiev@lirmm.fr. Supplementary data are available at Bioinformatics online.

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study

PubMed Central

Raethong, Nachon; Wong-ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa

2016-01-01

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H+-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction. PMID:27274991

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study.

PubMed

Raethong, Nachon; Wong-Ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa

2016-01-01

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H(+)-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database.

PubMed

Drabkin, Harold J; Blake, Judith A

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as 'GO' or 'homology' or 'phenotype'. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as 'papers selected for GO that refer to genes with NO GO annotation'. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations.

PubMed

Agapito, Giuseppe; Milano, Marianna; Guzzi, Pietro Hiram; Cannataro, Mario

2016-01-01

Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

MIPS bacterial genomes functional annotation benchmark dataset.

PubMed

Tetko, Igor V; Brauner, Barbara; Dunger-Kaltenbach, Irmtraud; Frishman, Goar; Montrone, Corinna; Fobo, Gisela; Ruepp, Andreas; Antonov, Alexey V; Surmeli, Dimitrij; Mewes, Hans-Wernen

2005-05-15

Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. BFAB is available at http://mips.gsf.de/proj/bfab

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

PubMed

Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

2018-02-23

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

Functional Annotation, Genome Organization and Phylogeny of the Grapevine (Vitis vinifera) Terpene Synthase Gene Family Based on Genome Assembly, FLcDNA Cloning, and Enzyme Assays

PubMed Central

2010-01-01

Background Terpenoids are among the most important constituents of grape flavour and wine bouquet, and serve as useful metabolite markers in viticulture and enology. Based on the initial 8-fold sequencing of a nearly homozygous Pinot noir inbred line, 89 putative terpenoid synthase genes (VvTPS) were predicted by in silico analysis of the grapevine (Vitis vinifera) genome assembly [1]. The finding of this very large VvTPS family, combined with the importance of terpenoid metabolism for the organoleptic properties of grapevine berries and finished wines, prompted a detailed examination of this gene family at the genomic level as well as an investigation into VvTPS biochemical functions. Results We present findings from the analysis of the up-dated 12-fold sequencing and assembly of the grapevine genome that place the number of predicted VvTPS genes at 69 putatively functional VvTPS, 20 partial VvTPS, and 63 VvTPS probable pseudogenes. Gene discovery and annotation included information about gene architecture and chromosomal location. A dense cluster of 45 VvTPS is localized on chromosome 18. Extensive FLcDNA cloning, gene synthesis, and protein expression enabled functional characterization of 39 VvTPS; this is the largest number of functionally characterized TPS for any species reported to date. Of these enzymes, 23 have unique functions and/or phylogenetic locations within the plant TPS gene family. Phylogenetic analyses of the TPS gene family showed that while most VvTPS form species-specific gene clusters, there are several examples of gene orthology with TPS of other plant species, representing perhaps more ancient VvTPS, which have maintained functions independent of speciation. Conclusions The highly expanded VvTPS gene family underpins the prominence of terpenoid metabolism in grapevine. We provide a detailed experimental functional annotation of 39 members of this important gene family in grapevine and comprehensive information about gene structure and

Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

NASA Astrophysics Data System (ADS)

Yu, Jia-Feng; Sui, Tian-Xiang; Wang, Hong-Mei; Wang, Chun-Ling; Jing, Li; Wang, Ji-Hua

2015-12-01

Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. Project supported by the National Natural Science Foundation of China (Grant Nos. 61302186 and 61271378) and the Funding from the State Key Laboratory of Bioelectronics of Southeast University.

Automated Gene Ontology annotation for anonymous sequence data.

PubMed

Hennig, Steffen; Groth, Detlef; Lehrach, Hans

2003-07-01

Gene Ontology (GO) is the most widely accepted attempt to construct a unified and structured vocabulary for the description of genes and their products in any organism. Annotation by GO terms is performed in most of the current genome projects, which besides generality has the advantage of being very convenient for computer based classification methods. However, direct use of GO in small sequencing projects is not easy, especially for species not commonly represented in public databases. We present a software package (GOblet), which performs annotation based on GO terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. The paper also addresses the reliability of automated GO annotations by using a reference set of more than 6000 human proteins. The GOblet server is accessible at http://goblet.molgen.mpg.de.

Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis

PubMed Central

Tellgren-Roth, Christian; Baudo, Charles D.; Kennell, John C.; Sun, Sheng; Billmyre, R. Blake; Schröder, Markus S.; Andersson, Anna; Holm, Tina; Sigurgeirsson, Benjamin; Wu, Guangxi; Sankaranarayanan, Sundar Ram; Siddharthan, Rahul; Sanyal, Kaustuv; Lundeberg, Joakim; Nystedt, Björn; Boekhout, Teun; Dawson, Thomas L.; Heitman, Joseph

2017-01-01

Abstract Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies. PMID:28100699

NoGOA: predicting noisy GO annotations using evidences and sparse representation.

PubMed

Yu, Guoxian; Lu, Chang; Wang, Jun

2017-07-21

Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem. We introduce a novel approach called NoGOA to predict noisy annotations. NoGOA applies sparse representation on the gene-term association matrix to reduce the impact of noisy annotations, and takes advantage of sparse representation coefficients to measure the semantic similarity between genes. Secondly, it preliminarily predicts noisy annotations of a gene based on aggregated votes from semantic neighborhood genes of that gene. Next, NoGOA estimates the ratio of noisy annotations for each evidence code based on direct annotations in GOA files archived on different periods, and then weights entries of the association matrix via estimated ratios and propagates weights to ancestors of direct annotations using GO hierarchy. Finally, it integrates evidence-weighted association matrix and aggregated votes to predict noisy annotations. Experiments on archived GOA files of six model species (H. sapiens, A. thaliana, S. cerevisiae, G. gallus, B. Taurus and M. musculus) demonstrate that NoGOA achieves significantly better results than other related methods and removing noisy annotations improves the performance of gene function prediction. The comparative study justifies the effectiveness of integrating evidence codes with sparse representation for predicting noisy GO annotations. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NoGOA .

Gene annotation from scientific literature using mappings between keyword systems.

PubMed

Pérez, Antonio J; Perez-Iratxeta, Carolina; Bork, Peer; Thode, Guillermo; Andrade, Miguel A

2004-09-01

The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat

Protein annotation from protein interaction networks and Gene Ontology.

PubMed

Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

2011-10-01

We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

APPRIS: annotation of principal and alternative splice isoforms

PubMed Central

Rodriguez, Jose Manuel; Maietta, Paolo; Ezkurdia, Iakes; Pietrelli, Alessandro; Wesselink, Jan-Jaap; Lopez, Gonzalo; Valencia, Alfonso; Tress, Michael L.

2013-01-01

Here, we present APPRIS (http://appris.bioinfo.cnio.es), a database that houses annotations of human splice isoforms. APPRIS has been designed to provide value to manual annotations of the human genome by adding reliable protein structural and functional data and information from cross-species conservation. The visual representation of the annotations provided by APPRIS for each gene allows annotators and researchers alike to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, APPRIS also selects a single reference sequence for each gene, here termed the principal isoform, based on the annotations of structure, function and conservation for each transcript. APPRIS identifies a principal isoform for 85% of the protein-coding genes in the GENCODE 7 release for ENSEMBL. Analysis of the APPRIS data shows that at least 70% of the alternative (non-principal) variants would lose important functional or structural information relative to the principal isoform. PMID:23161672

Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins

PubMed Central

Delcourt, Vivian; Lucier, Jean-François; Gagnon, Jules; Beaudoin, Maxime C; Vanderperre, Benoît; Breton, Marc-André; Motard, Julie; Jacques, Jean-François; Brunelle, Mylène; Gagnon-Arsenault, Isabelle; Fournier, Isabelle; Ouangraoua, Aida; Hunting, Darel J; Cohen, Alan A; Landry, Christian R; Scott, Michelle S

2017-01-01

Recent functional, proteomic and ribosome profiling studies in eukaryotes have concurrently demonstrated the translation of alternative open-reading frames (altORFs) in addition to annotated protein coding sequences (CDSs). We show that a large number of small proteins could in fact be coded by these altORFs. The putative alternative proteins translated from altORFs have orthologs in many species and contain functional domains. Evolutionary analyses indicate that altORFs often show more extreme conservation patterns than their CDSs. Thousands of alternative proteins are detected in proteomic datasets by reanalysis using a database containing predicted alternative proteins. This is illustrated with specific examples, including altMiD51, a 70 amino acid mitochondrial fission-promoting protein encoded in MiD51/Mief1/SMCR7L, a gene encoding an annotated protein promoting mitochondrial fission. Our results suggest that many genes are multicoding genes and code for a large protein and one or several small proteins. PMID:29083303

Eliciting the Functional Taxonomy from protein annotations and taxa

PubMed Central

Falda, Marco; Lavezzo, Enrico; Fontana, Paolo; Bianco, Luca; Berselli, Michele; Formentin, Elide; Toppo, Stefano

2016-01-01

The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional information for a vast amount of proteins. With these data available, we have developed FunTaxIS, a tool that is the first attempt to infer functional taxonomy (i.e. how functions are distributed over taxa) combining functional and taxonomic information. FunTaxIS is able to define a taxon specific functional space by exploiting annotation frequencies in order to establish if a function can or cannot be used to annotate a certain species. The tool generates constraints between GO terms and taxa and then propagates these relations over the taxonomic tree and the GO graph. Since these constraints nearly cover the whole taxonomy, it is possible to obtain the mapping of a function over the taxonomy. FunTaxIS can be used to make functional comparative analyses among taxa, to detect improper associations between taxa and functions, and to discover how functional knowledge is either distributed or missing. A benchmark test set based on six different model species has been devised to get useful insights on the generated taxonomic rules. PMID:27534507

Increased alignment sensitivity improves the usage of genome alignments for comparative gene annotation.

PubMed

Sharma, Virag; Hiller, Michael

2017-08-21

Genome alignments provide a powerful basis to transfer gene annotations from a well-annotated reference genome to many other aligned genomes. The completeness of these annotations crucially depends on the sensitivity of the underlying genome alignment. Here, we investigated the impact of the genome alignment parameters and found that parameters with a higher sensitivity allow the detection of thousands of novel alignments between orthologous exons that have been missed before. In particular, comparisons between species separated by an evolutionary distance of >0.75 substitutions per neutral site, like human and other non-placental vertebrates, benefit from increased sensitivity. To systematically test if increased sensitivity improves comparative gene annotations, we built a multiple alignment of 144 vertebrate genomes and used this alignment to map human genes to the other 143 vertebrates with CESAR. We found that higher alignment sensitivity substantially improves the completeness of comparative gene annotations by adding on average 2382 and 7440 novel exons and 117 and 317 novel genes for mammalian and non-mammalian species, respectively. Our results suggest a more sensitive alignment strategy that should generally be used for genome alignments between distantly-related species. Our 144-vertebrate genome alignment and the comparative gene annotations (https://bds.mpi-cbg.de/hillerlab/144VertebrateAlignment_CESAR/) are a valuable resource for comparative genomics. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Gene discovery in the hamster: a comparative genomics approach for gene annotation by sequencing of hamster testis cDNAs

PubMed Central

Oduru, Sreedhar; Campbell, Janee L; Karri, SriTulasi; Hendry, William J; Khan, Shafiq A; Williams, Simon C

2003-01-01

Background Complete genome annotation will likely be achieved through a combination of computer-based analysis of available genome sequences combined with direct experimental characterization of expressed regions of individual genomes. We have utilized a comparative genomics approach involving the sequencing of randomly selected hamster testis cDNAs to begin to identify genes not previously annotated on the human, mouse, rat and Fugu (pufferfish) genomes. Results 735 distinct sequences were analyzed for their relatedness to known sequences in public databases. Eight of these sequences were derived from previously unidentified genes and expression of these genes in testis was confirmed by Northern blotting. The genomic locations of each sequence were mapped in human, mouse, rat and pufferfish, where applicable, and the structure of their cognate genes was derived using computer-based predictions, genomic comparisons and analysis of uncharacterized cDNA sequences from human and macaque. Conclusion The use of a comparative genomics approach resulted in the identification of eight cDNAs that correspond to previously uncharacterized genes in the human genome. The proteins encoded by these genes included a new member of the kinesin superfamily, a SET/MYND-domain protein, and six proteins for which no specific function could be predicted. Each gene was expressed primarily in testis, suggesting that they may play roles in the development and/or function of testicular cells. PMID:12783626

Management and analysis of genomic functional and phenotypic controlled annotations to support biomedical investigation and practice.

PubMed

Masseroli, Marco

2007-07-01

The growing available genomic information provides new opportunities for novel research approaches and original biomedical applications that can provide effective data management and analysis support. In fact, integration and comprehensive evaluation of available controlled data can highlight information patterns leading to unveil new biomedical knowledge. Here, we describe Genome Function INtegrated Discover (GFINDer), a Web-accessible three-tier multidatabase system we developed to automatically enrich lists of user-classified genes with several functional and phenotypic controlled annotations, and to statistically evaluate them in order to identify annotation categories significantly over- or underrepresented in each considered gene class. Genomic controlled annotations from Gene Ontology (GO), KEGG, Pfam, InterPro, and Online Mendelian Inheritance in Man (OMIM) were integrated in GFINDer and several categorical tests were implemented for their analysis. A controlled vocabulary of inherited disorder phenotypes was obtained by normalizing and hierarchically structuring disease accompanying signs and symptoms from OMIM Clinical Synopsis sections. GFINDer modular architecture is well suited for further system expansion and for sustaining increasing workload. Testing results showed that GFINDer analyses can highlight gene functional and phenotypic characteristics and differences, demonstrating its value in supporting genomic biomedical approaches aiming at understanding the complex biomolecular mechanisms underlying patho-physiological phenotypes, and in helping the transfer of genomic results to medical practice.

TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes

PubMed Central

Leroy, Philippe; Guilhot, Nicolas; Sakai, Hiroaki; Bernard, Aurélien; Choulet, Frédéric; Theil, Sébastien; Reboux, Sébastien; Amano, Naoki; Flutre, Timothée; Pelegrin, Céline; Ohyanagi, Hajime; Seidel, Michael; Giacomoni, Franck; Reichstadt, Mathieu; Alaux, Michael; Gicquello, Emmanuelle; Legeai, Fabrice; Cerutti, Lorenzo; Numa, Hisataka; Tanaka, Tsuyoshi; Mayer, Klaus; Itoh, Takeshi; Quesneville, Hadi; Feuillet, Catherine

2012-01-01

In support of the international effort to obtain a reference sequence of the bread wheat genome and to provide plant communities dealing with large and complex genomes with a versatile, easy-to-use online automated tool for annotation, we have developed the TriAnnot pipeline. Its modular architecture allows for the annotation and masking of transposable elements, the structural, and functional annotation of protein-coding genes with an evidence-based quality indexing, and the identification of conserved non-coding sequences and molecular markers. The TriAnnot pipeline is parallelized on a 712 CPU computing cluster that can run a 1-Gb sequence annotation in less than 5 days. It is accessible through a web interface for small scale analyses or through a server for large scale annotations. The performance of TriAnnot was evaluated in terms of sensitivity, specificity, and general fitness using curated reference sequence sets from rice and wheat. In less than 8 h, TriAnnot was able to predict more than 83% of the 3,748 CDS from rice chromosome 1 with a fitness of 67.4%. On a set of 12 reference Mb-sized contigs from wheat chromosome 3B, TriAnnot predicted and annotated 93.3% of the genes among which 54% were perfectly identified in accordance with the reference annotation. It also allowed the curation of 12 genes based on new biological evidences, increasing the percentage of perfect gene prediction to 63%. TriAnnot systematically showed a higher fitness than other annotation pipelines that are not improved for wheat. As it is easily adaptable to the annotation of other plant genomes, TriAnnot should become a useful resource for the annotation of large and complex genomes in the future. PMID:22645565

Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis.

PubMed

Zhu, Yafeng; Engström, Pär G; Tellgren-Roth, Christian; Baudo, Charles D; Kennell, John C; Sun, Sheng; Billmyre, R Blake; Schröder, Markus S; Andersson, Anna; Holm, Tina; Sigurgeirsson, Benjamin; Wu, Guangxi; Sankaranarayanan, Sundar Ram; Siddharthan, Rahul; Sanyal, Kaustuv; Lundeberg, Joakim; Nystedt, Björn; Boekhout, Teun; Dawson, Thomas L; Heitman, Joseph; Scheynius, Annika; Lehtiö, Janne

2017-03-17

Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database

PubMed Central

Drabkin, Harold J.; Blake, Judith A.

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as ‘GO’ or ‘homology’ or ‘phenotype’. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as ‘papers selected for GO that refer to genes with NO GO annotation’. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations

Methodology for the inference of gene function from phenotype data.

PubMed

Ascensao, Joao A; Dolan, Mary E; Hill, David P; Blake, Judith A

2014-12-12

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and

An Approach to Function Annotation for Proteins of Unknown Function (PUFs) in the Transcriptome of Indian Mulberry.

PubMed

Dhanyalakshmi, K H; Naika, Mahantesha B N; Sajeevan, R S; Mathew, Oommen K; Shafi, K Mohamed; Sowdhamini, Ramanathan; N Nataraja, Karaba

2016-01-01

The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs). Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS), which also provides a web service API (Application Programming Interface) for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.

Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments

PubMed Central

Haas, Brian J; Salzberg, Steven L; Zhu, Wei; Pertea, Mihaela; Allen, Jonathan E; Orvis, Joshua; White, Owen; Buell, C Robin; Wortman, Jennifer R

2008-01-01

EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation. PMID:18190707

Dense Subgraphs with Restrictions and Applications to Gene Annotation Graphs

NASA Astrophysics Data System (ADS)

Saha, Barna; Hoch, Allison; Khuller, Samir; Raschid, Louiqa; Zhang, Xiao-Ning

In this paper, we focus on finding complex annotation patterns representing novel and interesting hypotheses from gene annotation data. We define a generalization of the densest subgraph problem by adding an additional distance restriction (defined by a separate metric) to the nodes of the subgraph. We show that while this generalization makes the problem NP-hard for arbitrary metrics, when the metric comes from the distance metric of a tree, or an interval graph, the problem can be solved optimally in polynomial time. We also show that the densest subgraph problem with a specified subset of vertices that have to be included in the solution can be solved optimally in polynomial time. In addition, we consider other extensions when not just one solution needs to be found, but we wish to list all subgraphs of almost maximum density as well. We apply this method to a dataset of genes and their annotations obtained from The Arabidopsis Information Resource (TAIR). A user evaluation confirms that the patterns found in the distance restricted densest subgraph for a dataset of photomorphogenesis genes are indeed validated in the literature; a control dataset validates that these are not random patterns. Interestingly, the complex annotation patterns potentially lead to new and as yet unknown hypotheses. We perform experiments to determine the properties of the dense subgraphs, as we vary parameters, including the number of genes and the distance.

AGORA : Organellar genome annotation from the amino acid and nucleotide references.

PubMed

Jung, Jaehee; Kim, Jong Im; Jeong, Young-Sik; Yi, Gangman

2018-03-29

Next-generation sequencing (NGS) technologies have led to the accumulation of highthroughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals.We have developed a web application AGORA for the fast, user-friendly, and improved annotations of organellar genomes. AGORA annotates genes based on a BLAST-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon-intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence, and visualization of gene map by OGDRAW. Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/.The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers. gangman@dongguk.edu.

NCBI prokaryotic genome annotation pipeline.

PubMed

Tatusova, Tatiana; DiCuccio, Michael; Badretdin, Azat; Chetvernin, Vyacheslav; Nawrocki, Eric P; Zaslavsky, Leonid; Lomsadze, Alexandre; Pruitt, Kim D; Borodovsky, Mark; Ostell, James

2016-08-19

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/. Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

OGRO: The Overview of functionally characterized Genes in Rice online database.

PubMed

Yamamoto, Eiji; Yonemaru, Jun-Ichi; Yamamoto, Toshio; Yano, Masahiro

2012-12-01

The high-quality sequence information and rich bioinformatics tools available for rice have contributed to remarkable advances in functional genomics. To facilitate the application of gene function information to the study of natural variation in rice, we comprehensively searched for articles related to rice functional genomics and extracted information on functionally characterized genes. As of 31 March 2012, 702 functionally characterized genes were annotated. This number represents about 1.6% of the predicted loci in the Rice Annotation Project Database. The compiled gene information is organized to facilitate direct comparisons with quantitative trait locus (QTL) information in the Q-TARO database. Comparison of genomic locations between functionally characterized genes and the QTLs revealed that QTL clusters were often co-localized with high-density gene regions, and that the genes associated with the QTLs in these clusters were different genes, suggesting that these QTL clusters are likely to be explained by tightly linked but distinct genes. Information on the functionally characterized genes compiled during this study is now available in the O verview of Functionally Characterized G enes in R ice O nline database (OGRO) on the Q-TARO website ( http://qtaro.abr.affrc.go.jp/ogro ). The database has two interfaces: a table containing gene information, and a genome viewer that allows users to compare the locations of QTLs and functionally characterized genes. OGRO on Q-TARO will facilitate a candidate-gene approach to identifying the genes responsible for QTLs. Because the QTL descriptions in Q-TARO contain information on agronomic traits, such comparisons will also facilitate the annotation of functionally characterized genes in terms of their effects on traits important for rice breeding. The increasing amount of information on rice gene function being generated from mutant panels and other types of studies will make the OGRO database even more valuable in

Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.)

PubMed Central

Kim, Seungill; Kim, Myung-Shin; Kim, Yong-Min; Yeom, Seon-In; Cheong, Kyeongchae; Kim, Ki-Tae; Jeon, Jongbum; Kim, Sunggil; Kim, Do-Sun; Sohn, Seong-Han; Lee, Yong-Hwan; Choi, Doil

2015-01-01

The onion (Allium cepa L.) is one of the most widely cultivated and consumed vegetable crops in the world. Although a considerable amount of onion transcriptome data has been deposited into public databases, the sequences of the protein-coding genes are not accurate enough to be used, owing to non-coding sequences intermixed with the coding sequences. We generated a high-quality, annotated onion transcriptome from de novo sequence assembly and intensive structural annotation using the integrated structural gene annotation pipeline (ISGAP), which identified 54,165 protein-coding genes among 165,179 assembled transcripts totalling 203.0 Mb by eliminating the intron sequences. ISGAP performed reliable annotation, recognizing accurate gene structures based on reference proteins, and ab initio gene models of the assembled transcripts. Integrative functional annotation and gene-based SNP analysis revealed a whole biological repertoire of genes and transcriptomic variation in the onion. The method developed in this study provides a powerful tool for the construction of reference gene sets for organisms based solely on de novo transcriptome data. Furthermore, the reference genes and their variation described here for the onion represent essential tools for molecular breeding and gene cloning in Allium spp. PMID:25362073

COGNATE: comparative gene annotation characterizer.

PubMed

Wilbrandt, Jeanne; Misof, Bernhard; Niehuis, Oliver

2017-07-17

The comparison of gene and genome structures across species has the potential to reveal major trends of genome evolution. However, such a comparative approach is currently hampered by a lack of standardization (e.g., Elliott TA, Gregory TR, Philos Trans Royal Soc B: Biol Sci 370:20140331, 2015). For example, testing the hypothesis that the total amount of coding sequences is a reliable measure of potential proteome diversity (Wang M, Kurland CG, Caetano-Anollés G, PNAS 108:11954, 2011) requires the application of standardized definitions of coding sequence and genes to create both comparable and comprehensive data sets and corresponding summary statistics. However, such standard definitions either do not exist or are not consistently applied. These circumstances call for a standard at the descriptive level using a minimum of parameters as well as an undeviating use of standardized terms, and for software that infers the required data under these strict definitions. The acquisition of a comprehensive, descriptive, and standardized set of parameters and summary statistics for genome publications and further analyses can thus greatly benefit from the availability of an easy to use standard tool. We developed a new open-source command-line tool, COGNATE (Comparative Gene Annotation Characterizer), which uses a given genome assembly and its annotation of protein-coding genes for a detailed description of the respective gene and genome structure parameters. Additionally, we revised the standard definitions of gene and genome structures and provide the definitions used by COGNATE as a working draft suggestion for further reference. Complete parameter lists and summary statistics are inferred using this set of definitions to allow down-stream analyses and to provide an overview of the genome and gene repertoire characteristics. COGNATE is written in Perl and freely available at the ZFMK homepage ( https://www.zfmk.de/en/COGNATE ) and on github ( https

Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam

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

Chai, Juanjuan; Kora, Guruprasad; Ahn, Tae-Hyuk

2014-10-09

To supply some background, phylogenetic studies have provided detailed knowledge on the evolutionary mechanisms of genes and species in Bacteria and Archaea. However, the evolution of cellular functions, represented by metabolic pathways and biological processes, has not been systematically characterized. Many clades in the prokaryotic tree of life have now been covered by sequenced genomes in GenBank. This enables a large-scale functional phylogenomics study of many computationally inferred cellular functions across all sequenced prokaryotes. Our results show a total of 14,727 GenBank prokaryotic genomes were re-annotated using a new protein family database, UniFam, to obtain consistent functional annotations for accuratemore » comparison. The functional profile of a genome was represented by the biological process Gene Ontology (GO) terms in its annotation. The GO term enrichment analysis differentiated the functional profiles between selected archaeal taxa. 706 prokaryotic metabolic pathways were inferred from these genomes using Pathway Tools and MetaCyc. The consistency between the distribution of metabolic pathways in the genomes and the phylogenetic tree of the genomes was measured using parsimony scores and retention indices. The ancestral functional profiles at the internal nodes of the phylogenetic tree were reconstructed to track the gains and losses of metabolic pathways in evolutionary history. In conclusion, our functional phylogenomics analysis shows divergent functional profiles of taxa and clades. Such function-phylogeny correlation stems from a set of clade-specific cellular functions with low parsimony scores. On the other hand, many cellular functions are sparsely dispersed across many clades with high parsimony scores. These different types of cellular functions have distinct evolutionary patterns reconstructed from the prokaryotic tree.« less

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

EuCAP, a Eukaryotic Community Annotation Package, and its application to the rice genome

PubMed Central

Thibaud-Nissen, Françoise; Campbell, Matthew; Hamilton, John P; Zhu, Wei; Buell, C Robin

2007-01-01

Background Despite the improvements of tools for automated annotation of genome sequences, manual curation at the structural and functional level can provide an increased level of refinement to genome annotation. The Institute for Genomic Research Rice Genome Annotation (hereafter named the Osa1 Genome Annotation) is the product of an automated pipeline and, for this reason, will benefit from the input of biologists with expertise in rice and/or particular gene families. Leveraging knowledge from a dispersed community of scientists is a demonstrated way of improving a genome annotation. This requires tools that facilitate 1) the submission of gene annotation to an annotation project, 2) the review of the submitted models by project annotators, and 3) the incorporation of the submitted models in the ongoing annotation effort. Results We have developed the Eukaryotic Community Annotation Package (EuCAP), an annotation tool, and have applied it to the rice genome. The primary level of curation by community annotators (CA) has been the annotation of gene families. Annotation can be submitted by email or through the EuCAP Web Tool. The CA models are aligned to the rice pseudomolecules and the coordinates of these alignments, along with functional annotation, are stored in the MySQL EuCAP Gene Model database. Web pages displaying the alignments of the CA models to the Osa1 Genome models are automatically generated from the EuCAP Gene Model database. The alignments are reviewed by the project annotators (PAs) in the context of experimental evidence. Upon approval by the PAs, the CA models, along with the corresponding functional annotations, are integrated into the Osa1 Genome Annotation. The CA annotations, grouped by family, are displayed on the Community Annotation pages of the project website , as well as in the Community Annotation track of the Genome Browser. Conclusion We have applied EuCAP to rice. As of July 2007, the structural and/or functional annotation of 1

Protannotator: a semiautomated pipeline for chromosome-wise functional annotation of the "missing" human proteome.

PubMed

Islam, Mohammad T; Garg, Gagan; Hancock, William S; Risk, Brian A; Baker, Mark S; Ranganathan, Shoba

2014-01-03

The chromosome-centric human proteome project (C-HPP) aims to define the complete set of proteins encoded in each human chromosome. The neXtProt database (September 2013) lists 20,128 proteins for the human proteome, of which 3831 human proteins (∼19%) are considered "missing" according to the standard metrics table (released September 27, 2013). In support of the C-HPP initiative, we have extended the annotation strategy developed for human chromosome 7 "missing" proteins into a semiautomated pipeline to functionally annotate the "missing" human proteome. This pipeline integrates a suite of bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology, and biochemical pathways. From sequential BLAST searches, we have primarily identified homologues from reviewed nonhuman mammalian proteins with protein evidence for 1271 (33.2%) "missing" proteins, followed by 703 (18.4%) homologues from reviewed nonhuman mammalian proteins and subsequently 564 (14.7%) homologues from reviewed human proteins. Functional annotations for 1945 (50.8%) "missing" proteins were also determined. To accelerate the identification of "missing" proteins from proteomics studies, we generated proteotypic peptides in silico. Matching these proteotypic peptides to ENCODE proteogenomic data resulted in proteomic evidence for 107 (2.8%) of the 3831 "missing proteins, while evidence from a recent membrane proteomic study supported the existence for another 15 "missing" proteins. The chromosome-wise functional annotation of all "missing" proteins is freely available to the scientific community through our web server (http://biolinfo.org/protannotator).

A robust data-driven approach for gene ontology annotation.

PubMed

Li, Yanpeng; Yu, Hong

2014-01-01

Gene ontology (GO) and GO annotation are important resources for biological information management and knowledge discovery, but the speed of manual annotation became a major bottleneck of database curation. BioCreative IV GO annotation task aims to evaluate the performance of system that automatically assigns GO terms to genes based on the narrative sentences in biomedical literature. This article presents our work in this task as well as the experimental results after the competition. For the evidence sentence extraction subtask, we built a binary classifier to identify evidence sentences using reference distance estimator (RDE), a recently proposed semi-supervised learning method that learns new features from around 10 million unlabeled sentences, achieving an F1 of 19.3% in exact match and 32.5% in relaxed match. In the post-submission experiment, we obtained 22.1% and 35.7% F1 performance by incorporating bigram features in RDE learning. In both development and test sets, RDE-based method achieved over 20% relative improvement on F1 and AUC performance against classical supervised learning methods, e.g. support vector machine and logistic regression. For the GO term prediction subtask, we developed an information retrieval-based method to retrieve the GO term most relevant to each evidence sentence using a ranking function that combined cosine similarity and the frequency of GO terms in documents, and a filtering method based on high-level GO classes. The best performance of our submitted runs was 7.8% F1 and 22.2% hierarchy F1. We found that the incorporation of frequency information and hierarchy filtering substantially improved the performance. In the post-submission evaluation, we obtained a 10.6% F1 using a simpler setting. Overall, the experimental analysis showed our approaches were robust in both the two tasks. © The Author(s) 2014. Published by Oxford University Press.

Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.).

PubMed

Kim, Seungill; Kim, Myung-Shin; Kim, Yong-Min; Yeom, Seon-In; Cheong, Kyeongchae; Kim, Ki-Tae; Jeon, Jongbum; Kim, Sunggil; Kim, Do-Sun; Sohn, Seong-Han; Lee, Yong-Hwan; Choi, Doil

2015-02-01

The onion (Allium cepa L.) is one of the most widely cultivated and consumed vegetable crops in the world. Although a considerable amount of onion transcriptome data has been deposited into public databases, the sequences of the protein-coding genes are not accurate enough to be used, owing to non-coding sequences intermixed with the coding sequences. We generated a high-quality, annotated onion transcriptome from de novo sequence assembly and intensive structural annotation using the integrated structural gene annotation pipeline (ISGAP), which identified 54,165 protein-coding genes among 165,179 assembled transcripts totalling 203.0 Mb by eliminating the intron sequences. ISGAP performed reliable annotation, recognizing accurate gene structures based on reference proteins, and ab initio gene models of the assembled transcripts. Integrative functional annotation and gene-based SNP analysis revealed a whole biological repertoire of genes and transcriptomic variation in the onion. The method developed in this study provides a powerful tool for the construction of reference gene sets for organisms based solely on de novo transcriptome data. Furthermore, the reference genes and their variation described here for the onion represent essential tools for molecular breeding and gene cloning in Allium spp. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Annotation of gene function in citrus using gene expression information and co-expression networks

PubMed Central

2014-01-01

Background The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed. Results We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit. Conclusions Integration of citrus gene co-expression networks

Rice DB: an Oryza Information Portal linking annotation, subcellular location, function, expression, regulation, and evolutionary information for rice and Arabidopsis

PubMed Central

Narsai, Reena; Devenish, James; Castleden, Ian; Narsai, Kabir; Xu, Lin; Shou, Huixia; Whelan, James

2013-01-01

Omics research in Oryza sativa (rice) relies on the use of multiple databases to obtain different types of information to define gene function. We present Rice DB, an Oryza information portal that is a functional genomics database, linking gene loci to comprehensive annotations, expression data and the subcellular location of encoded proteins. Rice DB has been designed to integrate the direct comparison of rice with Arabidopsis (Arabidopsis thaliana), based on orthology or ‘expressology’, thus using and combining available information from two pre-eminent plant models. To establish Rice DB, gene identifiers (more than 40 types) and annotations from a variety of sources were compiled, functional information based on large-scale and individual studies was manually collated, hundreds of microarrays were analysed to generate expression annotations, and the occurrences of potential functional regulatory motifs in promoter regions were calculated. A range of computational subcellular localization predictions were also run for all putative proteins encoded in the rice genome, and experimentally confirmed protein localizations have been collated, curated and linked to functional studies in rice. A single search box allows anything from gene identifiers (for rice and/or Arabidopsis), motif sequences, subcellular location, to keyword searches to be entered, with the capability of Boolean searches (such as AND/OR). To demonstrate the utility of Rice DB, several examples are presented including a rice mitochondrial proteome, which draws on a variety of sources for subcellular location data within Rice DB. Comparisons of subcellular location, functional annotations, as well as transcript expression in parallel with Arabidopsis reveals examples of conservation between rice and Arabidopsis, using Rice DB (http://ricedb.plantenergy.uwa.edu.au). PMID:24147765

Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica.

PubMed

Fernandez-Valverde, Selene L; Calcino, Andrew D; Degnan, Bernard M

2015-05-15

The demosponge Amphimedon queenslandica is amongst the few early-branching metazoans with an assembled and annotated draft genome, making it an important species in the study of the origin and early evolution of animals. Current gene models in this species are largely based on in silico predictions and low coverage expressed sequence tag (EST) evidence. Amphimedon queenslandica protein-coding gene models are improved using deep RNA-Seq data from four developmental stages and CEL-Seq data from 82 developmental samples. Over 86% of previously predicted genes are retained in the new gene models, although 24% have additional exons; there is also a marked increase in the total number of annotated 3' and 5' untranslated regions (UTRs). Importantly, these new developmental transcriptome data reveal numerous previously unannotated protein-coding genes in the Amphimedon genome, increasing the total gene number by 25%, from 30,060 to 40,122. In general, Amphimedon genes have introns that are markedly smaller than those in other animals and most of the alternatively spliced genes in Amphimedon undergo intron-retention; exon-skipping is the least common mode of alternative splicing. Finally, in addition to canonical polyadenylation signal sequences, Amphimedon genes are enriched in a number of unique AT-rich motifs in their 3' UTRs. The inclusion of developmental transcriptome data has substantially improved the structure and composition of protein-coding gene models in Amphimedon queenslandica, providing a more accurate and comprehensive set of genes for functional and comparative studies. These improvements reveal the Amphimedon genome is comprised of a remarkably high number of tightly packed genes. These genes have small introns and there is pervasive intron retention amongst alternatively spliced transcripts. These aspects of the sponge genome are more similar unicellular opisthokont genomes than to other animal genomes.

Canine candidate genes for dilated cardiomyopathy: annotation of and polymorphic markers for 14 genes.

PubMed

Wiersma, Anje C; Leegwater, Peter Aj; van Oost, Bernard A; Ollier, William E; Dukes-McEwan, Joanna

2007-10-19

Dilated cardiomyopathy is a myocardial disease occurring in humans and domestic animals and is characterized by dilatation of the left ventricle, reduced systolic function and increased sphericity of the left ventricle. Dilated cardiomyopathy has been observed in several, mostly large and giant, dog breeds, such as the Dobermann and the Great Dane. A number of genes have been identified, which are associated with dilated cardiomyopathy in the human, mouse and hamster. These genes mainly encode structural proteins of the cardiac myocyte. We present the annotation of, and marker development for, 14 of these genes of the dog genome, i.e. alpha-cardiac actin, caveolin 1, cysteine-rich protein 3, desmin, lamin A/C, LIM-domain binding factor 3, myosin heavy polypeptide 7, phospholamban, sarcoglycan delta, titin cap, alpha-tropomyosin, troponin I, troponin T and vinculin. A total of 33 Single Nucleotide Polymorphisms were identified for these canine genes and 11 polymorphic microsatellite repeats were developed. The presented polymorphisms provide a tool to investigate the role of the corresponding genes in canine Dilated Cardiomyopathy by linkage analysis or association studies.

Exogean: a framework for annotating protein-coding genes in eukaryotic genomic DNA

PubMed Central

Djebali, Sarah; Delaplace, Franck; Crollius, Hugues Roest

2006-01-01

Background Accurate and automatic gene identification in eukaryotic genomic DNA is more than ever of crucial importance to efficiently exploit the large volume of assembled genome sequences available to the community. Automatic methods have always been considered less reliable than human expertise. This is illustrated in the EGASP project, where reference annotations against which all automatic methods are measured are generated by human annotators and experimentally verified. We hypothesized that replicating the accuracy of human annotators in an automatic method could be achieved by formalizing the rules and decisions that they use, in a mathematical formalism. Results We have developed Exogean, a flexible framework based on directed acyclic colored multigraphs (DACMs) that can represent biological objects (for example, mRNA, ESTs, protein alignments, exons) and relationships between them. Graphs are analyzed to process the information according to rules that replicate those used by human annotators. Simple individual starting objects given as input to Exogean are thus combined and synthesized into complex objects such as protein coding transcripts. Conclusion We show here, in the context of the EGASP project, that Exogean is currently the method that best reproduces protein coding gene annotations from human experts, in terms of identifying at least one exact coding sequence per gene. We discuss current limitations of the method and several avenues for improvement. PMID:16925841

Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification.

PubMed

Zhang, Jingpu; Zhang, Zuping; Wang, Zixiang; Liu, Yuting; Deng, Lei

2018-05-15

Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. The source code and data are available at http://denglab.org/NeuraNetL2GO/. leideng@csu.edu.cn. Supplementary data are available at Bioinformatics online.

An integrated and comparative approach towards identification, characterization and functional annotation of candidate genes for drought tolerance in sorghum (Sorghum bicolor (L.) Moench).

PubMed

Woldesemayat, Adugna Abdi; Van Heusden, Peter; Ndimba, Bongani K; Christoffels, Alan

2017-12-22

Drought is the most disastrous abiotic stress that severely affects agricultural productivity worldwide. Understanding the biological basis of drought-regulated traits, requires identification and an in-depth characterization of genetic determinants using model organisms and high-throughput technologies. However, studies on drought tolerance have generally been limited to traditional candidate gene approach that targets only a single gene in a pathway that is related to a trait. In this study, we used sorghum, one of the model crops that is well adapted to arid regions, to mine genes and define determinants for drought tolerance using drought expression libraries and RNA-seq data. We provide an integrated and comparative in silico candidate gene identification, characterization and annotation approach, with an emphasis on genes playing a prominent role in conferring drought tolerance in sorghum. A total of 470 non-redundant functionally annotated drought responsive genes (DRGs) were identified using experimental data from drought responses by employing pairwise sequence similarity searches, pathway and interpro-domain analysis, expression profiling and orthology relation. Comparison of the genomic locations between these genes and sorghum quantitative trait loci (QTLs) showed that 40% of these genes were co-localized with QTLs known for drought tolerance. The genome reannotation conducted using the Program to Assemble Spliced Alignment (PASA), resulted in 9.6% of existing single gene models being updated. In addition, 210 putative novel genes were identified using AUGUSTUS and PASA based analysis on expression dataset. Among these, 50% were single exonic, 69.5% represented drought responsive and 5.7% were complete gene structure models. Analysis of biochemical metabolism revealed 14 metabolic pathways that are related to drought tolerance and also had a strong biological network, among categories of genes involved. Identification of these pathways, signifies the

FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer.

PubMed

Panigrahi, Priyabrata; Jere, Abhay; Anamika, Krishanpal

2018-01-01

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

Rice DB: an Oryza Information Portal linking annotation, subcellular location, function, expression, regulation, and evolutionary information for rice and Arabidopsis.

PubMed

Narsai, Reena; Devenish, James; Castleden, Ian; Narsai, Kabir; Xu, Lin; Shou, Huixia; Whelan, James

2013-12-01

Omics research in Oryza sativa (rice) relies on the use of multiple databases to obtain different types of information to define gene function. We present Rice DB, an Oryza information portal that is a functional genomics database, linking gene loci to comprehensive annotations, expression data and the subcellular location of encoded proteins. Rice DB has been designed to integrate the direct comparison of rice with Arabidopsis (Arabidopsis thaliana), based on orthology or 'expressology', thus using and combining available information from two pre-eminent plant models. To establish Rice DB, gene identifiers (more than 40 types) and annotations from a variety of sources were compiled, functional information based on large-scale and individual studies was manually collated, hundreds of microarrays were analysed to generate expression annotations, and the occurrences of potential functional regulatory motifs in promoter regions were calculated. A range of computational subcellular localization predictions were also run for all putative proteins encoded in the rice genome, and experimentally confirmed protein localizations have been collated, curated and linked to functional studies in rice. A single search box allows anything from gene identifiers (for rice and/or Arabidopsis), motif sequences, subcellular location, to keyword searches to be entered, with the capability of Boolean searches (such as AND/OR). To demonstrate the utility of Rice DB, several examples are presented including a rice mitochondrial proteome, which draws on a variety of sources for subcellular location data within Rice DB. Comparisons of subcellular location, functional annotations, as well as transcript expression in parallel with Arabidopsis reveals examples of conservation between rice and Arabidopsis, using Rice DB (http://ricedb.plantenergy.uwa.edu.au). © 2013 The Authors The Plant Journal © 2013 John Wiley & Sons Ltd.

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes.

PubMed

Ono, Hiromasa; Ogasawara, Osamu; Okubo, Kosaku; Bono, Hidemasa

2017-08-29

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/.

Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

PubMed Central

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.

2014-01-01

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function

Sheep genome functional annotation reveals proximal regulatory elements contributed to the evolution of modern breeds.

PubMed

Naval-Sanchez, Marina; Nguyen, Quan; McWilliam, Sean; Porto-Neto, Laercio R; Tellam, Ross; Vuocolo, Tony; Reverter, Antonio; Perez-Enciso, Miguel; Brauning, Rudiger; Clarke, Shannon; McCulloch, Alan; Zamani, Wahid; Naderi, Saeid; Rezaei, Hamid Reza; Pompanon, Francois; Taberlet, Pierre; Worley, Kim C; Gibbs, Richard A; Muzny, Donna M; Jhangiani, Shalini N; Cockett, Noelle; Daetwyler, Hans; Kijas, James

2018-02-28

Domestication fundamentally reshaped animal morphology, physiology and behaviour, offering the opportunity to investigate the molecular processes driving evolutionary change. Here we assess sheep domestication and artificial selection by comparing genome sequence from 43 modern breeds (Ovis aries) and their Asian mouflon ancestor (O. orientalis) to identify selection sweeps. Next, we provide a comparative functional annotation of the sheep genome, validated using experimental ChIP-Seq of sheep tissue. Using these annotations, we evaluate the impact of selection and domestication on regulatory sequences and find that sweeps are significantly enriched for protein coding genes, proximal regulatory elements of genes and genome features associated with active transcription. Finally, we find individual sites displaying strong allele frequency divergence are enriched for the same regulatory features. Our data demonstrate that remodelling of gene expression is likely to have been one of the evolutionary forces that drove phenotypic diversification of this common livestock species.

Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms.

PubMed

Speiser, Daniel I; Pankey, M Sabrina; Zaharoff, Alexander K; Battelle, Barbara A; Bracken-Grissom, Heather D; Breinholt, Jesse W; Bybee, Seth M; Cronin, Thomas W; Garm, Anders; Lindgren, Annie R; Patel, Nipam H; Porter, Megan L; Protas, Meredith E; Rivera, Ajna S; Serb, Jeanne M; Zigler, Kirk S; Crandall, Keith A; Oakley, Todd H

2014-11-19

Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis to produce a computationally efficient, tree-based approach for annotating transcriptomes or new genomes that we term Phylogenetically-Informed Annotation (PIA), which places uncharacterized genes into pre-calculated phylogenies of gene families. We generated maximum likelihood trees for 109 genes from a Light Interaction Toolkit (LIT), a collection of genes that underlie the function or development of light-interacting structures in metazoans. To do so, we searched protein sequences predicted from 29 fully-sequenced genomes and built trees using tools for phylogenetic analysis in the Osiris package of Galaxy (an open-source workflow management system). Next, to rapidly annotate transcriptomes from organisms that lack sequenced genomes, we repurposed a maximum likelihood-based Evolutionary Placement Algorithm (implemented in RAxML) to place sequences of potential LIT genes on to our pre-calculated gene trees. Finally, we implemented PIA in Galaxy and used it to search for LIT genes in 28 newly-sequenced transcriptomes from the light-interacting tissues of a range of cephalopod mollusks, arthropods, and cubozoan cnidarians. Our new trees for LIT genes are available on the Bitbucket public repository ( http://bitbucket.org/osiris_phylogenetics/pia/ ) and we demonstrate PIA on a publicly-accessible web server ( http://galaxy-dev.cnsi.ucsb.edu/pia/ ). Our new

Canine candidate genes for dilated cardiomyopathy: annotation of and polymorphic markers for 14 genes

PubMed Central

Wiersma, Anje C; Leegwater, Peter AJ; van Oost, Bernard A; Ollier, William E; Dukes-McEwan, Joanna

2007-01-01

Background Dilated cardiomyopathy is a myocardial disease occurring in humans and domestic animals and is characterized by dilatation of the left ventricle, reduced systolic function and increased sphericity of the left ventricle. Dilated cardiomyopathy has been observed in several, mostly large and giant, dog breeds, such as the Dobermann and the Great Dane. A number of genes have been identified, which are associated with dilated cardiomyopathy in the human, mouse and hamster. These genes mainly encode structural proteins of the cardiac myocyte. Results We present the annotation of, and marker development for, 14 of these genes of the dog genome, i.e. α-cardiac actin, caveolin 1, cysteine-rich protein 3, desmin, lamin A/C, LIM-domain binding factor 3, myosin heavy polypeptide 7, phospholamban, sarcoglycan δ, titin cap, α-tropomyosin, troponin I, troponin T and vinculin. A total of 33 Single Nucleotide Polymorphisms were identified for these canine genes and 11 polymorphic microsatellite repeats were developed. Conclusion The presented polymorphisms provide a tool to investigate the role of the corresponding genes in canine Dilated Cardiomyopathy by linkage analysis or association studies. PMID:17949487

Cloning, analysis and functional annotation of expressed sequence tags from the Earthworm Eisenia fetida

PubMed Central

Pirooznia, Mehdi; Gong, Ping; Guan, Xin; Inouye, Laura S; Yang, Kuan; Perkins, Edward J; Deng, Youping

2007-01-01

Background Eisenia fetida, commonly known as red wiggler or compost worm, belongs to the Lumbricidae family of the Annelida phylum. Little is known about its genome sequence although it has been extensively used as a test organism in terrestrial ecotoxicology. In order to understand its gene expression response to environmental contaminants, we cloned 4032 cDNAs or expressed sequence tags (ESTs) from two E. fetida libraries enriched with genes responsive to ten ordnance related compounds using suppressive subtractive hybridization-PCR. Results A total of 3144 good quality ESTs (GenBank dbEST accession number EH669363–EH672369 and EL515444–EL515580) were obtained from the raw clone sequences after cleaning. Clustering analysis yielded 2231 unique sequences including 448 contigs (from 1361 ESTs) and 1783 singletons. Comparative genomic analysis showed that 743 or 33% of the unique sequences shared high similarity with existing genes in the GenBank nr database. Provisional function annotation assigned 830 Gene Ontology terms to 517 unique sequences based on their homology with the annotated genomes of four model organisms Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, and Caenorhabditis elegans. Seven percent of the unique sequences were further mapped to 99 Kyoto Encyclopedia of Genes and Genomes pathways based on their matching Enzyme Commission numbers. All the information is stored and retrievable at a highly performed, web-based and user-friendly relational database called EST model database or ESTMD version 2. Conclusion The ESTMD containing the sequence and annotation information of 4032 E. fetida ESTs is publicly accessible at . PMID:18047730

Mining functionally relevant gene sets for analyzing physiologically novel clinical expression data.

PubMed

Turcan, Sevin; Vetter, Douglas E; Maron, Jill L; Wei, Xintao; Slonim, Donna K

2011-01-01

Gene set analyses have become a standard approach for increasing the sensitivity of transcriptomic studies. However, analytical methods incorporating gene sets require the availability of pre-defined gene sets relevant to the underlying physiology being studied. For novel physiological problems, relevant gene sets may be unavailable or existing gene set databases may bias the results towards only the best-studied of the relevant biological processes. We describe a successful attempt to mine novel functional gene sets for translational projects where the underlying physiology is not necessarily well characterized in existing annotation databases. We choose targeted training data from public expression data repositories and define new criteria for selecting biclusters to serve as candidate gene sets. Many of the discovered gene sets show little or no enrichment for informative Gene Ontology terms or other functional annotation. However, we observe that such gene sets show coherent differential expression in new clinical test data sets, even if derived from different species, tissues, and disease states. We demonstrate the efficacy of this method on a human metabolic data set, where we discover novel, uncharacterized gene sets that are diagnostic of diabetes, and on additional data sets related to neuronal processes and human development. Our results suggest that our approach may be an efficient way to generate a collection of gene sets relevant to the analysis of data for novel clinical applications where existing functional annotation is relatively incomplete.

A Factor Graph Approach to Automated GO Annotation.

PubMed

Spetale, Flavio E; Tapia, Elizabeth; Krsticevic, Flavia; Roda, Fernando; Bulacio, Pilar

2016-01-01

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

PubMed Central

Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim; Krogsgaard, Steen; Nielsen, Jens

2008-01-01

Background Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae. Results Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion A much enhanced annotation of the A

Genome-wide Annotation, Identification, and Global Transcriptomic Analysis of Regulatory or Small RNA Gene Expression in Staphylococcus aureus.

PubMed

Carroll, Ronan K; Weiss, Andy; Broach, William H; Wiemels, Richard E; Mogen, Austin B; Rice, Kelly C; Shaw, Lindsey N

2016-02-09

In Staphylococcus aureus, hundreds of small regulatory or small RNAs (sRNAs) have been identified, yet this class of molecule remains poorly understood and severely understudied. sRNA genes are typically absent from genome annotation files, and as a consequence, their existence is often overlooked, particularly in global transcriptomic studies. To facilitate improved detection and analysis of sRNAs in S. aureus, we generated updated GenBank files for three commonly used S. aureus strains (MRSA252, NCTC 8325, and USA300), in which we added annotations for >260 previously identified sRNAs. These files, the first to include genome-wide annotation of sRNAs in S. aureus, were then used as a foundation to identify novel sRNAs in the community-associated methicillin-resistant strain USA300. This analysis led to the discovery of 39 previously unidentified sRNAs. Investigating the genomic loci of the newly identified sRNAs revealed a surprising degree of inconsistency in genome annotation in S. aureus, which may be hindering the analysis and functional exploration of these elements. Finally, using our newly created annotation files as a reference, we perform a global analysis of sRNA gene expression in S. aureus and demonstrate that the newly identified tsr25 is the most highly upregulated sRNA in human serum. This study provides an invaluable resource to the S. aureus research community in the form of our newly generated annotation files, while at the same time presenting the first examination of differential sRNA expression in pathophysiologically relevant conditions. Despite a large number of studies identifying regulatory or small RNA (sRNA) genes in Staphylococcus aureus, their annotation is notably lacking in available genome files. In addition to this, there has been a considerable lack of cross-referencing in the wealth of studies identifying these elements, often leading to the same sRNA being identified multiple times and bearing multiple names. In this work

GeneMachine: gene prediction and sequence annotation.

PubMed

Makalowska, I; Ryan, J F; Baxevanis, A D

2001-09-01

A number of free-standing programs have been developed in order to help researchers find potential coding regions and deduce gene structure for long stretches of what is essentially 'anonymous DNA'. As these programs apply inherently different criteria to the question of what is and is not a coding region, multiple algorithms should be used in the course of positional cloning and positional candidate projects to assure that all potential coding regions within a previously-identified critical region are identified. We have developed a gene identification tool called GeneMachine which allows users to query multiple exon and gene prediction programs in an automated fashion. BLAST searches are also performed in order to see whether a previously-characterized coding region corresponds to a region in the query sequence. A suite of Perl programs and modules are used to run MZEF, GENSCAN, GRAIL 2, FGENES, RepeatMasker, Sputnik, and BLAST. The results of these runs are then parsed and written into ASN.1 format. Output files can be opened using NCBI Sequin, in essence using Sequin as both a workbench and as a graphical viewer. The main feature of GeneMachine is that the process is fully automated; the user is only required to launch GeneMachine and then open the resulting file with Sequin. Annotations can then be made to these results prior to submission to GenBank, thereby increasing the intrinsic value of these data. GeneMachine is freely-available for download at http://genome.nhgri.nih.gov/genemachine. A public Web interface to the GeneMachine server for academic and not-for-profit users is available at http://genemachine.nhgri.nih.gov. The Web supplement to this paper may be found at http://genome.nhgri.nih.gov/genemachine/supplement/.

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

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

Leung, Elo; Huang, Amy; Cadag, Eithon

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE PAGES

Leung, Elo; Huang, Amy; Cadag, Eithon; ...

2016-01-20

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Integrative Functional Genomics for Systems Genetics in GeneWeaver.org.

PubMed

Bubier, Jason A; Langston, Michael A; Baker, Erich J; Chesler, Elissa J

2017-01-01

The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.

RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes

PubMed Central

Ono, Hiromasa; Ogasawara, Osamu; Okubo, Kosaku; Bono, Hidemasa

2017-01-01

Gene expression data are exponentially accumulating; thus, the functional annotation of such sequence data from metadata is urgently required. However, life scientists have difficulty utilizing the available data due to its sheer magnitude and complicated access. We have developed a web tool for browsing reference gene expression pattern of mammalian tissues and cell lines measured using different methods, which should facilitate the reuse of the precious data archived in several public databases. The web tool is called Reference Expression dataset (RefEx), and RefEx allows users to search by the gene name, various types of IDs, chromosomal regions in genetic maps, gene family based on InterPro, gene expression patterns, or biological categories based on Gene Ontology. RefEx also provides information about genes with tissue-specific expression, and the relative gene expression values are shown as choropleth maps on 3D human body images from BodyParts3D. Combined with the newly incorporated Functional Annotation of Mammals (FANTOM) dataset, RefEx provides insight regarding the functional interpretation of unfamiliar genes. RefEx is publicly available at http://refex.dbcls.jp/. PMID:28850115

The De Novo Transcriptome and Its Functional Annotation in the Seed Beetle Callosobruchus maculatus.

PubMed

Sayadi, Ahmed; Immonen, Elina; Bayram, Helen; Arnqvist, Göran

2016-01-01

Despite their unparalleled biodiversity, the genomic resources available for beetles (Coleoptera) remain relatively scarce. We present an integrative and high quality annotated transcriptome of the beetle Callosobruchus maculatus, an important and cosmopolitan agricultural pest as well as an emerging model species in ecology and evolutionary biology. Using Illumina sequencing technology, we sequenced 492 million read pairs generated from 51 samples of different developmental stages (larvae, pupae and adults) of C. maculatus. Reads were de novo assembled using the Trinity software, into a single combined assembly as well as into three separate assemblies based on data from the different developmental stages. The combined assembly generated 218,192 transcripts and 145,883 putative genes. Putative genes were annotated with the Blast2GO software and the Trinotate pipeline. In total, 33,216 putative genes were successfully annotated using Blastx against the Nr (non-redundant) database and 13,382 were assigned to 34,100 Gene Ontology (GO) terms. We classified 5,475 putative genes into Clusters of Orthologous Groups (COG) and 116 metabolic pathways maps were predicted based on the annotation. Our analyses suggested that the transcriptional specificity increases with ontogeny. For example, out of 33,216 annotated putative genes, 51 were only expressed in larvae, 63 only in pupae and 171 only in adults. Our study illustrates the importance of including samples from several developmental stages when the aim is to provide an integrative and high quality annotated transcriptome. Our results will represent an invaluable resource for those working with the ecology, evolution and pest control of C. maculatus, as well for comparative studies of the transcriptomics and genomics of beetles more generally.

The De Novo Transcriptome and Its Functional Annotation in the Seed Beetle Callosobruchus maculatus

PubMed Central

Sayadi, Ahmed; Immonen, Elina; Bayram, Helen

2016-01-01

Despite their unparalleled biodiversity, the genomic resources available for beetles (Coleoptera) remain relatively scarce. We present an integrative and high quality annotated transcriptome of the beetle Callosobruchus maculatus, an important and cosmopolitan agricultural pest as well as an emerging model species in ecology and evolutionary biology. Using Illumina sequencing technology, we sequenced 492 million read pairs generated from 51 samples of different developmental stages (larvae, pupae and adults) of C. maculatus. Reads were de novo assembled using the Trinity software, into a single combined assembly as well as into three separate assemblies based on data from the different developmental stages. The combined assembly generated 218,192 transcripts and 145,883 putative genes. Putative genes were annotated with the Blast2GO software and the Trinotate pipeline. In total, 33,216 putative genes were successfully annotated using Blastx against the Nr (non-redundant) database and 13,382 were assigned to 34,100 Gene Ontology (GO) terms. We classified 5,475 putative genes into Clusters of Orthologous Groups (COG) and 116 metabolic pathways maps were predicted based on the annotation. Our analyses suggested that the transcriptional specificity increases with ontogeny. For example, out of 33,216 annotated putative genes, 51 were only expressed in larvae, 63 only in pupae and 171 only in adults. Our study illustrates the importance of including samples from several developmental stages when the aim is to provide an integrative and high quality annotated transcriptome. Our results will represent an invaluable resource for those working with the ecology, evolution and pest control of C. maculatus, as well for comparative studies of the transcriptomics and genomics of beetles more generally. PMID:27442123

PanCoreGen - Profiling, detecting, annotating protein-coding genes in microbial genomes.

PubMed

Paul, Sandip; Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V; Chattopadhyay, Sujay

2015-12-01

A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing the pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen - a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for a species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars - Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. Copyright © 2015 Elsevier Inc. All rights reserved.

PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data.

PubMed

Hart, Steven N; Moore, Raymond M; Zimmermann, Michael T; Oliver, Gavin R; Egan, Jan B; Bryce, Alan H; Kocher, Jean-Pierre A

2015-01-01

Objective. Bringing together genomics, transcriptomics, proteomics, and other -omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods. We have developed PANDA (Pathway AND Annotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e., other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results. In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user's own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion. PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website: http://bioinformaticstools.mayo.edu/research/panda-viewer/.

Bioinformatics for spermatogenesis: annotation of male reproduction based on proteomics

PubMed Central

Zhou, Tao; Zhou, Zuo-Min; Guo, Xue-Jiang

2013-01-01

Proteomics strategies have been widely used in the field of male reproduction, both in basic and clinical research. Bioinformatics methods are indispensable in proteomics-based studies and are used for data presentation, database construction and functional annotation. In the present review, we focus on the functional annotation of gene lists obtained through qualitative or quantitative methods, summarizing the common and male reproduction specialized proteomics databases. We introduce several integrated tools used to find the hidden biological significance from the data obtained. We further describe in detail the information on male reproduction derived from Gene Ontology analyses, pathway analyses and biomedical analyses. We provide an overview of bioinformatics annotations in spermatogenesis, from gene function to biological function and from biological function to clinical application. On the basis of recently published proteomics studies and associated data, we show that bioinformatics methods help us to discover drug targets for sperm motility and to scan for cancer-testis genes. In addition, we summarize the online resources relevant to male reproduction research for the exploration of the regulation of spermatogenesis. PMID:23852026

RATT: Rapid Annotation Transfer Tool

PubMed Central

Otto, Thomas D.; Dillon, Gary P.; Degrave, Wim S.; Berriman, Matthew

2011-01-01

Second-generation sequencing technologies have made large-scale sequencing projects commonplace. However, making use of these datasets often requires gene function to be ascribed genome wide. Although tool development has kept pace with the changes in sequence production, for tasks such as mapping, de novo assembly or visualization, genome annotation remains a challenge. We have developed a method to rapidly provide accurate annotation for new genomes using previously annotated genomes as a reference. The method, implemented in a tool called RATT (Rapid Annotation Transfer Tool), transfers annotations from a high-quality reference to a new genome on the basis of conserved synteny. We demonstrate that a Mycobacterium tuberculosis genome or a single 2.5 Mb chromosome from a malaria parasite can be annotated in less than five minutes with only modest computational resources. RATT is available at http://ratt.sourceforge.net. PMID:21306991

Automated update, revision, and quality control of the maize genome annotations using MAKER-P improves the B73 RefGen_v3 gene models and identifies new genes.

PubMed

Law, MeiYee; Childs, Kevin L; Campbell, Michael S; Stein, Joshua C; Olson, Andrew J; Holt, Carson; Panchy, Nicholas; Lei, Jikai; Jiao, Dian; Andorf, Carson M; Lawrence, Carolyn J; Ware, Doreen; Shiu, Shin-Han; Sun, Yanni; Jiang, Ning; Yandell, Mark

2015-01-01

The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-P to update and revise the maize (Zea mays) B73 RefGen_v3 annotation build (5b+) in less than 3 h using the iPlant Cyberinfrastructure. MAKER-P identified and annotated 4,466 additional, well-supported protein-coding genes not present in the 5b+ annotation build, added additional untranslated regions to 1,393 5b+ gene models, identified 2,647 5b+ gene models that lack any supporting evidence (despite the use of large and diverse evidence data sets), identified 104,215 pseudogene fragments, and created an additional 2,522 noncoding gene annotations. We also describe a method for de novo training of MAKER-P for the annotation of newly sequenced grass genomes. Collectively, these results lead to the 6a maize genome annotation and demonstrate the utility of MAKER-P for rapid annotation, management, and quality control of grasses and other difficult-to-annotate plant genomes. © 2015 American Society of Plant Biologists. All Rights Reserved.

Genetic control of functional traits related to photosynthesis and water use efficiency in Pinus pinaster Ait. drought response: integration of genome annotation, allele association and QTL detection for candidate gene identification.

PubMed

de Miguel, Marina; Cabezas, José-Antonio; de María, Nuria; Sánchez-Gómez, David; Guevara, María-Ángeles; Vélez, María-Dolores; Sáez-Laguna, Enrique; Díaz, Luis-Manuel; Mancha, Jose-Antonio; Barbero, María-Carmen; Collada, Carmen; Díaz-Sala, Carmen; Aranda, Ismael; Cervera, María-Teresa

2014-06-12

Understanding molecular mechanisms that control photosynthesis and water use efficiency in response to drought is crucial for plant species from dry areas. This study aimed to identify QTL for these traits in a Mediterranean conifer and tested their stability under drought. High density linkage maps for Pinus pinaster were used in the detection of QTL for photosynthesis and water use efficiency at three water irrigation regimes. A total of 28 significant and 27 suggestive QTL were found. QTL detected for photochemical traits accounted for the higher percentage of phenotypic variance. Functional annotation of genes within the QTL suggested 58 candidate genes for the analyzed traits. Allele association analysis in selected candidate genes showed three SNPs located in a MYB transcription factor that were significantly associated with efficiency of energy capture by open PSII reaction centers and specific leaf area. The integration of QTL mapping of functional traits, genome annotation and allele association yielded several candidate genes involved with molecular control of photosynthesis and water use efficiency in response to drought in a conifer species. The results obtained highlight the importance of maintaining the integrity of the photochemical machinery in P. pinaster drought response.

Leveraging Comparative Genomics to Identify and Functionally Characterize Genes Associated with Sperm Phenotypes in Python bivittatus (Burmese Python).

PubMed

Irizarry, Kristopher J L; Rutllant, Josep

2016-01-01

Comparative genomics approaches provide a means of leveraging functional genomics information from a highly annotated model organism's genome (such as the mouse genome) in order to make physiological inferences about the role of genes and proteins in a less characterized organism's genome (such as the Burmese python). We employed a comparative genomics approach to produce the functional annotation of Python bivittatus genes encoding proteins associated with sperm phenotypes. We identify 129 gene-phenotype relationships in the python which are implicated in 10 specific sperm phenotypes. Results obtained through our systematic analysis identified subsets of python genes exhibiting associations with gene ontology annotation terms. Functional annotation data was represented in a semantic scatter plot. Together, these newly annotated Python bivittatus genome resources provide a high resolution framework from which the biology relating to reptile spermatogenesis, fertility, and reproduction can be further investigated. Applications of our research include (1) production of genetic diagnostics for assessing fertility in domestic and wild reptiles; (2) enhanced assisted reproduction technology for endangered and captive reptiles; and (3) novel molecular targets for biotechnology-based approaches aimed at reducing fertility and reproduction of invasive reptiles. Additional enhancements to reptile genomic resources will further enhance their value.

Literature-based concept profiles for gene annotation: the issue of weighting.

PubMed

Jelier, Rob; Schuemie, Martijn J; Roes, Peter-Jan; van Mulligen, Erik M; Kors, Jan A

2008-05-01

Text-mining has been used to link biomedical concepts, such as genes or biological processes, to each other for annotation purposes or the generation of new hypotheses. To relate two concepts to each other several authors have used the vector space model, as vectors can be compared efficiently and transparently. Using this model, a concept is characterized by a list of associated concepts, together with weights that indicate the strength of the association. The associated concepts in the vectors and their weights are derived from a set of documents linked to the concept of interest. An important issue with this approach is the determination of the weights of the associated concepts. Various schemes have been proposed to determine these weights, but no comparative studies of the different approaches are available. Here we compare several weighting approaches in a large scale classification experiment. Three different techniques were evaluated: (1) weighting based on averaging, an empirical approach; (2) the log likelihood ratio, a test-based measure; (3) the uncertainty coefficient, an information-theory based measure. The weighting schemes were applied in a system that annotates genes with Gene Ontology codes. As the gold standard for our study we used the annotations provided by the Gene Ontology Annotation project. Classification performance was evaluated by means of the receiver operating characteristics (ROC) curve using the area under the curve (AUC) as the measure of performance. All methods performed well with median AUC scores greater than 0.84, and scored considerably higher than a binary approach without any weighting. Especially for the more specific Gene Ontology codes excellent performance was observed. The differences between the methods were small when considering the whole experiment. However, the number of documents that were linked to a concept proved to be an important variable. When larger amounts of texts were available for the generation of

Identification and computational annotation of genes differentially expressed in pulp development of Cocos nucifera L. by suppression subtractive hybridization

PubMed Central

2014-01-01

Background Coconut (Cocos nucifera L.) is one of the world’s most versatile, economically important tropical crops. Little is known about the physiological and molecular basis of coconut pulp (endosperm) development and only a few coconut genes and gene product sequences are available in public databases. This study identified genes that were differentially expressed during development of coconut pulp and functionally annotated these identified genes using bioinformatics analysis. Results Pulp from three different coconut developmental stages was collected. Four suppression subtractive hybridization (SSH) libraries were constructed (forward and reverse libraries A and B between stages 1 and 2, and C and D between stages 2 and 3), and identified sequences were computationally annotated using Blast2GO software. A total of 1272 clones were obtained for analysis from four SSH libraries with 63% showing similarity to known proteins. Pairwise comparing of stage-specific gene ontology ids from libraries B-D, A-C, B-C and A-D showed that 32 genes were continuously upregulated and seven downregulated; 28 were transiently upregulated and 23 downregulated. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis showed that 1-acyl-sn-glycerol-3-phosphate acyltransferase (LPAAT), phospholipase D, acetyl-CoA carboxylase carboxyltransferase beta subunit, 3-hydroxyisobutyryl-CoA hydrolase-like and pyruvate dehydrogenase E1 β subunit were associated with fatty acid biosynthesis or metabolism. Triose phosphate isomerase, cellulose synthase and glucan 1,3-β-glucosidase were related to carbohydrate metabolism, and phosphoenolpyruvate carboxylase was related to both fatty acid and carbohydrate metabolism. Of 737 unigenes, 103 encoded enzymes were involved in fatty acid and carbohydrate biosynthesis and metabolism, and a number of transcription factors and other interesting genes with stage-specific expression were confirmed by real-time PCR, with validation of the SSH results as

Identification and computational annotation of genes differentially expressed in pulp development of Cocos nucifera L. by suppression subtractive hybridization.

PubMed

Liang, Yuanxue; Yuan, Yijun; Liu, Tao; Mao, Wei; Zheng, Yusheng; Li, Dongdong

2014-08-02

Coconut (Cocos nucifera L.) is one of the world's most versatile, economically important tropical crops. Little is known about the physiological and molecular basis of coconut pulp (endosperm) development and only a few coconut genes and gene product sequences are available in public databases. This study identified genes that were differentially expressed during development of coconut pulp and functionally annotated these identified genes using bioinformatics analysis. Pulp from three different coconut developmental stages was collected. Four suppression subtractive hybridization (SSH) libraries were constructed (forward and reverse libraries A and B between stages 1 and 2, and C and D between stages 2 and 3), and identified sequences were computationally annotated using Blast2GO software. A total of 1272 clones were obtained for analysis from four SSH libraries with 63% showing similarity to known proteins. Pairwise comparing of stage-specific gene ontology ids from libraries B-D, A-C, B-C and A-D showed that 32 genes were continuously upregulated and seven downregulated; 28 were transiently upregulated and 23 downregulated. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis showed that 1-acyl-sn-glycerol-3-phosphate acyltransferase (LPAAT), phospholipase D, acetyl-CoA carboxylase carboxyltransferase beta subunit, 3-hydroxyisobutyryl-CoA hydrolase-like and pyruvate dehydrogenase E1 β subunit were associated with fatty acid biosynthesis or metabolism. Triose phosphate isomerase, cellulose synthase and glucan 1,3-β-glucosidase were related to carbohydrate metabolism, and phosphoenolpyruvate carboxylase was related to both fatty acid and carbohydrate metabolism. Of 737 unigenes, 103 encoded enzymes were involved in fatty acid and carbohydrate biosynthesis and metabolism, and a number of transcription factors and other interesting genes with stage-specific expression were confirmed by real-time PCR, with validation of the SSH results as high as 66.6%. Based on

Annotating ebony on the fly.

PubMed

Kohn, Michael H; Wittkopp, Patricia J

2007-07-01

The distinctive black phenotype of ebony mutants has made it one of the most widely used phenotypic markers in Drosophila genetics. Without doubt, ebony showcases the fruits of the fly community's labours to annotate gene function. As of this writing, FlyBase lists 142 references, 1277 fly stocks, 15 phenotypes and 44 alleles. In addition to its namesake pigmentation phenotype, ebony mutants affect other traits, including phototaxis and courtship. With phenotypic consequences of ebony variants readily apparent in the laboratory, does natural selection also see them in the wild? In this issue of Molecular Ecology, Pool & Aquadro investigate this question and found signs of natural selection on the ebony gene that appear to have resulted from selection for darker pigmentation at higher elevations in sub-Saharan populations of Drosophila melanogaster. Such findings from population genomic analysis of wild-derived strains should be included in gene annotations to provide a more holistic view of a gene's function. The evolutionary annotation of ebony added by Pool & Aquadro substantiates that pigmentation can be adaptive and implicates elevation as an important selective factor. This is important progress because the selective factors seem to differ between populations and species. In addition, the study raises issues to consider when extrapolating from selection at the molecular level to selection at the phenotypic level.

A Factor Graph Approach to Automated GO Annotation

PubMed Central

Spetale, Flavio E.; Tapia, Elizabeth; Krsticevic, Flavia; Roda, Fernando; Bulacio, Pilar

2016-01-01

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum. PMID:26771463

Solving the Problem: Genome Annotation Standards before the Data Deluge.

PubMed

Klimke, William; O'Donovan, Claire; White, Owen; Brister, J Rodney; Clark, Karen; Fedorov, Boris; Mizrachi, Ilene; Pruitt, Kim D; Tatusova, Tatiana

2011-10-15

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries.

Genome Wide Re-Annotation of Caldicellulosiruptor saccharolyticus with New Insights into Genes Involved in Biomass Degradation and Hydrogen Production

PubMed Central

Chowdhary, Nupoor; Selvaraj, Ashok; KrishnaKumaar, Lakshmi; Kumar, Gopal Ramesh

2015-01-01

Caldicellulosiruptor saccharolyticus has proven itself to be an excellent candidate for biological hydrogen (H2) production, but still it has major drawbacks like sensitivity to high osmotic pressure and low volumetric H2 productivity, which should be considered before it can be used industrially. A whole genome re-annotation work has been carried out as an attempt to update the incomplete genome information that causes gap in the knowledge especially in the area of metabolic engineering, to improve the H2 producing capabilities of C. saccharolyticus. Whole genome re-annotation was performed through manual means for 2,682 Coding Sequences (CDSs). Bioinformatics tools based on sequence similarity, motif search, phylogenetic analysis and fold recognition were employed for re-annotation. Our methodology could successfully add functions for 409 hypothetical proteins (HPs), 46 proteins previously annotated as putative and assigned more accurate functions for the known protein sequences. Homology based gene annotation has been used as a standard method for assigning function to novel proteins, but over the past few years many non-homology based methods such as genomic context approaches for protein function prediction have been developed. Using non-homology based functional prediction methods, we were able to assign cellular processes or physical complexes for 249 hypothetical sequences. Our re-annotation pipeline highlights the addition of 231 new CDSs generated from MicroScope Platform, to the original genome with functional prediction for 49 of them. The re-annotation of HPs and new CDSs is stored in the relational database that is available on the MicroScope web-based platform. In parallel, a comparative genome analyses were performed among the members of genus Caldicellulosiruptor to understand the function and evolutionary processes. Further, with results from integrated re-annotation studies (homology and genomic context approach), we strongly suggest that Csac

Genome Wide Re-Annotation of Caldicellulosiruptor saccharolyticus with New Insights into Genes Involved in Biomass Degradation and Hydrogen Production.

PubMed

Chowdhary, Nupoor; Selvaraj, Ashok; KrishnaKumaar, Lakshmi; Kumar, Gopal Ramesh

2015-01-01

Caldicellulosiruptor saccharolyticus has proven itself to be an excellent candidate for biological hydrogen (H2) production, but still it has major drawbacks like sensitivity to high osmotic pressure and low volumetric H2 productivity, which should be considered before it can be used industrially. A whole genome re-annotation work has been carried out as an attempt to update the incomplete genome information that causes gap in the knowledge especially in the area of metabolic engineering, to improve the H2 producing capabilities of C. saccharolyticus. Whole genome re-annotation was performed through manual means for 2,682 Coding Sequences (CDSs). Bioinformatics tools based on sequence similarity, motif search, phylogenetic analysis and fold recognition were employed for re-annotation. Our methodology could successfully add functions for 409 hypothetical proteins (HPs), 46 proteins previously annotated as putative and assigned more accurate functions for the known protein sequences. Homology based gene annotation has been used as a standard method for assigning function to novel proteins, but over the past few years many non-homology based methods such as genomic context approaches for protein function prediction have been developed. Using non-homology based functional prediction methods, we were able to assign cellular processes or physical complexes for 249 hypothetical sequences. Our re-annotation pipeline highlights the addition of 231 new CDSs generated from MicroScope Platform, to the original genome with functional prediction for 49 of them. The re-annotation of HPs and new CDSs is stored in the relational database that is available on the MicroScope web-based platform. In parallel, a comparative genome analyses were performed among the members of genus Caldicellulosiruptor to understand the function and evolutionary processes. Further, with results from integrated re-annotation studies (homology and genomic context approach), we strongly suggest that Csac

Creating reference gene annotation for the mouse C57BL6/J genome assembly.

PubMed

Mudge, Jonathan M; Harrow, Jennifer

2015-10-01

Annotation on the reference genome of the C57BL6/J mouse has been an ongoing project ever since the draft genome was first published. Initially, the principle focus was on the identification of all protein-coding genes, although today the importance of describing long non-coding RNAs, small RNAs, and pseudogenes is recognized. Here, we describe the progress of the GENCODE mouse annotation project, which combines manual annotation from the HAVANA group with Ensembl computational annotation, alongside experimental and in silico validation pipelines from other members of the consortium. We discuss the more recent incorporation of next-generation sequencing datasets into this workflow, including the usage of mass-spectrometry data to potentially identify novel protein-coding genes. Finally, we will outline how the C57BL6/J genebuild can be used to gain insights into the variant sites that distinguish different mouse strains and species.

Identification of novel biomass-degrading enzymes from genomic dark matter: Populating genomic sequence space with functional annotation.

PubMed

Piao, Hailan; Froula, Jeff; Du, Changbin; Kim, Tae-Wan; Hawley, Erik R; Bauer, Stefan; Wang, Zhong; Ivanova, Nathalia; Clark, Douglas S; Klenk, Hans-Peter; Hess, Matthias

2014-08-01

Although recent nucleotide sequencing technologies have significantly enhanced our understanding of microbial genomes, the function of ∼35% of genes identified in a genome currently remains unknown. To improve the understanding of microbial genomes and consequently of microbial processes it will be crucial to assign a function to this "genomic dark matter." Due to the urgent need for additional carbohydrate-active enzymes for improved production of transportation fuels from lignocellulosic biomass, we screened the genomes of more than 5,500 microorganisms for hypothetical proteins that are located in the proximity of already known cellulases. We identified, synthesized and expressed a total of 17 putative cellulase genes with insufficient sequence similarity to currently known cellulases to be identified as such using traditional sequence annotation techniques that rely on significant sequence similarity. The recombinant proteins of the newly identified putative cellulases were subjected to enzymatic activity assays to verify their hydrolytic activity towards cellulose and lignocellulosic biomass. Eleven (65%) of the tested enzymes had significant activity towards at least one of the substrates. This high success rate highlights that a gene context-based approach can be used to assign function to genes that are otherwise categorized as "genomic dark matter" and to identify biomass-degrading enzymes that have little sequence similarity to already known cellulases. The ability to assign function to genes that have no related sequence representatives with functional annotation will be important to enhance our understanding of microbial processes and to identify microbial proteins for a wide range of applications. © 2014 Wiley Periodicals, Inc.

Sma3s: A universal tool for easy functional annotation of proteomes and transcriptomes.

PubMed

Casimiro-Soriguer, Carlos S; Muñoz-Mérida, Antonio; Pérez-Pulido, Antonio J

2017-06-01

The current cheapening of next-generation sequencing has led to an enormous growth in the number of sequenced genomes and transcriptomes, allowing wet labs to get the sequences from their organisms of study. To make the most of these data, one of the first things that should be done is the functional annotation of the protein-coding genes. But it used to be a slow and tedious step that can involve the characterization of thousands of sequences. Sma3s is an accurate computational tool for annotating proteins in an unattended way. Now, we have developed a completely new version, which includes functionalities that will be of utility for fundamental and applied science. Currently, the results provide functional categories such as biological processes, which become useful for both characterizing particular sequence datasets and comparing results from different projects. But one of the most important implemented innovations is that it has now low computational requirements, and the complete annotation of a simple proteome or transcriptome usually takes around 24 hours in a personal computer. Sma3s has been tested with a large amount of complete proteomes and transcriptomes, and it has demonstrated its potential in health science and other specific projects. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessment of protein set coherence using functional annotations

PubMed Central

Chagoyen, Monica; Carazo, Jose M; Pascual-Montano, Alberto

2008-01-01

Background Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information (e.g. significantly enriched annotations, pairwise functional similarities), they do not specifically measure the degree of homogeneity of a protein set. Results In this work we present a method that scores the degree of functional homogeneity, or coherence, of a set of proteins on the basis of the global similarity of their functional annotations. The method uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set. As such, it can be used as a first step in the validation of sets expected to be homogeneous prior to further functional interpretation. Conclusion We evaluate our method by analysing known biologically relevant sets as well as random ones. The known relevant sets comprise macromolecular complexes, cellular components and pathways described for Saccharomyces cerevisiae, which are mostly significantly coherent. Finally, we illustrate the usefulness of our approach for validating 'functional modules' obtained from computational analysis of protein-protein interaction networks. Matlab code and supplementary data are available at PMID:18937846

Using comparative genome analysis to identify problems in annotated microbial genomes.

PubMed

Poptsova, Maria S; Gogarten, J Peter

2010-07-01

Genome annotation is a tedious task that is mostly done by automated methods; however, the accuracy of these approaches has been questioned since the beginning of the sequencing era. Genome annotation is a multilevel process, and errors can emerge at different stages: during sequencing, as a result of gene-calling procedures, and in the process of assigning gene functions. Missed or wrongly annotated genes differentially impact different types of analyses. Here we discuss and demonstrate how the methods of comparative genome analysis can refine annotations by locating missing orthologues. We also discuss possible reasons for errors and show that the second-generation annotation systems, which combine multiple gene-calling programs with similarity-based methods, perform much better than the first annotation tools. Since old errors may propagate to the newly sequenced genomes, we emphasize that the problem of continuously updating popular public databases is an urgent and unresolved one. Due to the progress in genome-sequencing technologies, automated annotation techniques will remain the main approach in the future. Researchers need to be aware of the existing errors in the annotation of even well-studied genomes, such as Escherichia coli, and consider additional quality control for their results.

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

PubMed

Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke

2009-02-15

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http

Genome-wide Annotation, Identification, and Global Transcriptomic Analysis of Regulatory or Small RNA Gene Expression in Staphylococcus aureus

PubMed Central

Weiss, Andy; Broach, William H.; Wiemels, Richard E.; Mogen, Austin B.; Rice, Kelly C.

2016-01-01

ABSTRACT In Staphylococcus aureus, hundreds of small regulatory or small RNAs (sRNAs) have been identified, yet this class of molecule remains poorly understood and severely understudied. sRNA genes are typically absent from genome annotation files, and as a consequence, their existence is often overlooked, particularly in global transcriptomic studies. To facilitate improved detection and analysis of sRNAs in S. aureus, we generated updated GenBank files for three commonly used S. aureus strains (MRSA252, NCTC 8325, and USA300), in which we added annotations for >260 previously identified sRNAs. These files, the first to include genome-wide annotation of sRNAs in S. aureus, were then used as a foundation to identify novel sRNAs in the community-associated methicillin-resistant strain USA300. This analysis led to the discovery of 39 previously unidentified sRNAs. Investigating the genomic loci of the newly identified sRNAs revealed a surprising degree of inconsistency in genome annotation in S. aureus, which may be hindering the analysis and functional exploration of these elements. Finally, using our newly created annotation files as a reference, we perform a global analysis of sRNA gene expression in S. aureus and demonstrate that the newly identified tsr25 is the most highly upregulated sRNA in human serum. This study provides an invaluable resource to the S. aureus research community in the form of our newly generated annotation files, while at the same time presenting the first examination of differential sRNA expression in pathophysiologically relevant conditions. PMID:26861020

Translatomics combined with transcriptomics and proteomics reveals novel functional, recently evolved orphan genes in Escherichia coli O157:H7 (EHEC).

PubMed

Neuhaus, Klaus; Landstorfer, Richard; Fellner, Lea; Simon, Svenja; Schafferhans, Andrea; Goldberg, Tatyana; Marx, Harald; Ozoline, Olga N; Rost, Burkhard; Kuster, Bernhard; Keim, Daniel A; Scherer, Siegfried

2016-02-24

Genomes of E. coli, including that of the human pathogen Escherichia coli O157:H7 (EHEC) EDL933, still harbor undetected protein-coding genes which, apparently, have escaped annotation due to their small size and non-essential function. To find such genes, global gene expression of EHEC EDL933 was examined, using strand-specific RNAseq (transcriptome), ribosomal footprinting (translatome) and mass spectrometry (proteome). Using the above methods, 72 short, non-annotated protein-coding genes were detected. All of these showed signals in the ribosomal footprinting assay indicating mRNA translation. Seven were verified by mass spectrometry. Fifty-seven genes are annotated in other enterobacteriaceae, mainly as hypothetical genes; the remaining 15 genes constitute novel discoveries. In addition, protein structure and function were predicted computationally and compared between EHEC-encoded proteins and 100-times randomly shuffled proteins. Based on this comparison, 61 of the 72 novel proteins exhibit predicted structural and functional features similar to those of annotated proteins. Many of the novel genes show differential transcription when grown under eleven diverse growth conditions suggesting environmental regulation. Three genes were found to confer a phenotype in previous studies, e.g., decreased cattle colonization. These findings demonstrate that ribosomal footprinting can be used to detect novel protein coding genes, contributing to the growing body of evidence that hypothetical genes are not annotation artifacts and opening an additional way to study their functionality. All 72 genes are taxonomically restricted and, therefore, appear to have evolved relatively recently de novo.

Functional annotation of the vlinc class of non-coding RNAs using systems biology approach

PubMed Central

Laurent, Georges St.; Vyatkin, Yuri; Antonets, Denis; Ri, Maxim; Qi, Yao; Saik, Olga; Shtokalo, Dmitry; de Hoon, Michiel J.L.; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Arner, Erik; Forrest, Alistair R.R.; Nicolas, Estelle; McCaffrey, Timothy A.; Carninci, Piero; Hayashizaki, Yoshihide; Wahlestedt, Claes; Kapranov, Philipp

2016-01-01

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlincRNAs genes likely function in cis to activate nearby genes. This effect while most pronounced in closely spaced vlincRNA–gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlincRNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs. PMID:27001520

Leveraging Comparative Genomics to Identify and Functionally Characterize Genes Associated with Sperm Phenotypes in Python bivittatus (Burmese Python)

PubMed Central

Rutllant, Josep

2016-01-01

Comparative genomics approaches provide a means of leveraging functional genomics information from a highly annotated model organism's genome (such as the mouse genome) in order to make physiological inferences about the role of genes and proteins in a less characterized organism's genome (such as the Burmese python). We employed a comparative genomics approach to produce the functional annotation of Python bivittatus genes encoding proteins associated with sperm phenotypes. We identify 129 gene-phenotype relationships in the python which are implicated in 10 specific sperm phenotypes. Results obtained through our systematic analysis identified subsets of python genes exhibiting associations with gene ontology annotation terms. Functional annotation data was represented in a semantic scatter plot. Together, these newly annotated Python bivittatus genome resources provide a high resolution framework from which the biology relating to reptile spermatogenesis, fertility, and reproduction can be further investigated. Applications of our research include (1) production of genetic diagnostics for assessing fertility in domestic and wild reptiles; (2) enhanced assisted reproduction technology for endangered and captive reptiles; and (3) novel molecular targets for biotechnology-based approaches aimed at reducing fertility and reproduction of invasive reptiles. Additional enhancements to reptile genomic resources will further enhance their value. PMID:27200191

Automated Update, Revision, and Quality Control of the Maize Genome Annotations Using MAKER-P Improves the B73 RefGen_v3 Gene Models and Identifies New Genes1[OPEN

PubMed Central

Law, MeiYee; Childs, Kevin L.; Campbell, Michael S.; Stein, Joshua C.; Olson, Andrew J.; Holt, Carson; Panchy, Nicholas; Lei, Jikai; Jiao, Dian; Andorf, Carson M.; Lawrence, Carolyn J.; Ware, Doreen; Shiu, Shin-Han; Sun, Yanni; Jiang, Ning; Yandell, Mark

2015-01-01

The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-P to update and revise the maize (Zea mays) B73 RefGen_v3 annotation build (5b+) in less than 3 h using the iPlant Cyberinfrastructure. MAKER-P identified and annotated 4,466 additional, well-supported protein-coding genes not present in the 5b+ annotation build, added additional untranslated regions to 1,393 5b+ gene models, identified 2,647 5b+ gene models that lack any supporting evidence (despite the use of large and diverse evidence data sets), identified 104,215 pseudogene fragments, and created an additional 2,522 noncoding gene annotations. We also describe a method for de novo training of MAKER-P for the annotation of newly sequenced grass genomes. Collectively, these results lead to the 6a maize genome annotation and demonstrate the utility of MAKER-P for rapid annotation, management, and quality control of grasses and other difficult-to-annotate plant genomes. PMID:25384563

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.

PubMed

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-02-04

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

PubMed Central

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-01-01

Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method. PMID:19193216

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

DOE PAGES

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; ...

2014-10-16

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary

Functional Annotation of Ion Channel Structures by Molecular Simulation.

PubMed

Trick, Jemma L; Chelvaniththilan, Sivapalan; Klesse, Gianni; Aryal, Prafulla; Wallace, E Jayne; Tucker, Stephen J; Sansom, Mark S P

2016-12-06

Ion channels play key roles in cell membranes, and recent advances are yielding an increasing number of structures. However, their functional relevance is often unclear and better tools are required for their functional annotation. In sub-nanometer pores such as ion channels, hydrophobic gating has been shown to promote dewetting to produce a functionally closed (i.e., non-conductive) state. Using the serotonin receptor (5-HT 3 R) structure as an example, we demonstrate the use of molecular dynamics to aid the functional annotation of channel structures via simulation of the behavior of water within the pore. Three increasingly complex simulation analyses are described: water equilibrium densities; single-ion free-energy profiles; and computational electrophysiology. All three approaches correctly predict the 5-HT 3 R crystal structure to represent a functionally closed (i.e., non-conductive) state. We also illustrate the application of water equilibrium density simulations to annotate different conformational states of a glycine receptor. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Solving the Problem: Genome Annotation Standards before the Data Deluge

PubMed Central

Klimke, William; O'Donovan, Claire; White, Owen; Brister, J. Rodney; Clark, Karen; Fedorov, Boris; Mizrachi, Ilene; Pruitt, Kim D.; Tatusova, Tatiana

2011-01-01

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries. PMID:22180819

IMG ER: a system for microbial genome annotation expert review and curation.

PubMed

Markowitz, Victor M; Mavromatis, Konstantinos; Ivanova, Natalia N; Chen, I-Min A; Chu, Ken; Kyrpides, Nikos C

2009-09-01

A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct. We have developed an Expert Review (ER) version of the Integrated Microbial Genomes (IMG) system, with the goal of supporting systematic and efficient revision of microbial genome annotations. IMG ER provides tools for the review and curation of annotations of both new and publicly available microbial genomes within IMG's rich integrated genome framework. New genome datasets are included into IMG ER prior to their public release either with their native annotations or with annotations generated by IMG ER's annotation pipeline. IMG ER tools allow addressing annotation problems detected with IMG's comparative analysis tools, such as genes missed by gene prediction pipelines or genes without an associated function. Over the past year, IMG ER was used for improving the annotations of about 150 microbial genomes.

A draft annotation and overview of the human genome

PubMed Central

Wright, Fred A; Lemon, William J; Zhao, Wei D; Sears, Russell; Zhuo, Degen; Wang, Jian-Ping; Yang, Hee-Yung; Baer, Troy; Stredney, Don; Spitzner, Joe; Stutz, Al; Krahe, Ralf; Yuan, Bo

2001-01-01

Background The recent draft assembly of the human genome provides a unified basis for describing genomic structure and function. The draft is sufficiently accurate to provide useful annotation, enabling direct observations of previously inferred biological phenomena. Results We report here a functionally annotated human gene index placed directly on the genome. The index is based on the integration of public transcript, protein, and mapping information, supplemented with computational prediction. We describe numerous global features of the genome and examine the relationship of various genetic maps with the assembly. In addition, initial sequence analysis reveals highly ordered chromosomal landscapes associated with paralogous gene clusters and distinct functional compartments. Finally, these annotation data were synthesized to produce observations of gene density and number that accord well with historical estimates. Such a global approach had previously been described only for chromosomes 21 and 22, which together account for 2.2% of the genome. Conclusions We estimate that the genome contains 65,000-75,000 transcriptional units, with exon sequences comprising 4%. The creation of a comprehensive gene index requires the synthesis of all available computational and experimental evidence. PMID:11516338

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

2014-01-01

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

Large-scale gene function analysis with the PANTHER classification system.

PubMed

Mi, Huaiyu; Muruganujan, Anushya; Casagrande, John T; Thomas, Paul D

2013-08-01

The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs). Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways. The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests. It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. In the 2013 release of PANTHER (v.8.0), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability. This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system.

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.

PubMed

Peng, Jiajie; Zhang, Xuanshuo; Hui, Weiwei; Lu, Junya; Li, Qianqian; Liu, Shuhui; Shang, Xuequn

2018-03-19

Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

Functional annotation of the vlinc class of non-coding RNAs using systems biology approach.

PubMed

St Laurent, Georges; Vyatkin, Yuri; Antonets, Denis; Ri, Maxim; Qi, Yao; Saik, Olga; Shtokalo, Dmitry; de Hoon, Michiel J L; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Arner, Erik; Forrest, Alistair R R; Nicolas, Estelle; McCaffrey, Timothy A; Carninci, Piero; Hayashizaki, Yoshihide; Wahlestedt, Claes; Kapranov, Philipp

2016-04-20

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlinc RNAs genes likely function in cisto activate nearby genes. This effect while most pronounced in closely spaced vlinc RNA-gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlinc RNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

ORCAN-a web-based meta-server for real-time detection and functional annotation of orthologs.

PubMed

Zielezinski, Andrzej; Dziubek, Michal; Sliski, Jan; Karlowski, Wojciech M

2017-04-15

ORCAN (ORtholog sCANner) is a web-based meta-server for one-click evolutionary and functional annotation of protein sequences. The server combines information from the most popular orthology-prediction resources, including four tools and four online databases. Functional annotation utilizes five additional comparisons between the query and identified homologs, including: sequence similarity, protein domain architectures, functional motifs, Gene Ontology term assignments and a list of associated articles. Furthermore, the server uses a plurality-based rating system to evaluate the orthology relationships and to rank the reference proteins by their evolutionary and functional relevance to the query. Using a dataset of ∼1 million true yeast orthologs as a sample reference set, we show that combining multiple orthology-prediction tools in ORCAN increases the sensitivity and precision by 1-2 percent points. The service is available for free at http://www.combio.pl/orcan/ . wmk@amu.edu.pl. 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

Finding a common path: predicting gene function using inferred evolutionary trees.

PubMed

Reynolds, Kimberly A

2014-07-14

Reporting in Cell, Li and colleagues (2014) describe an innovative method to functionally classify genes using evolutionary information. This approach demonstrates broad utility for eukaryotic gene annotation and suggests an intriguing new decomposition of pathways and complexes into evolutionarily conserved modules. Copyright © 2014 Elsevier Inc. All rights reserved.

Biases in the Experimental Annotations of Protein Function and Their Effect on Our Understanding of Protein Function Space

PubMed Central

Schnoes, Alexandra M.; Ream, David C.; Thorman, Alexander W.; Babbitt, Patricia C.; Friedberg, Iddo

2013-01-01

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments. PMID:23737737

Large-scale identification and characterization of alternative splicing variants of human gene transcripts using 56 419 completely sequenced and manually annotated full-length cDNAs

PubMed Central

Takeda, Jun-ichi; Suzuki, Yutaka; Nakao, Mitsuteru; Barrero, Roberto A.; Koyanagi, Kanako O.; Jin, Lihua; Motono, Chie; Hata, Hiroko; Isogai, Takao; Nagai, Keiichi; Otsuki, Tetsuji; Kuryshev, Vladimir; Shionyu, Masafumi; Yura, Kei; Go, Mitiko; Thierry-Mieg, Jean; Thierry-Mieg, Danielle; Wiemann, Stefan; Nomura, Nobuo; Sugano, Sumio; Gojobori, Takashi; Imanishi, Tadashi

2006-01-01

We report the first genome-wide identification and characterization of alternative splicing in human gene transcripts based on analysis of the full-length cDNAs. Applying both manual and computational analyses for 56 419 completely sequenced and precisely annotated full-length cDNAs selected for the H-Invitational human transcriptome annotation meetings, we identified 6877 alternative splicing genes with 18 297 different alternative splicing variants. A total of 37 670 exons were involved in these alternative splicing events. The encoded protein sequences were affected in 6005 of the 6877 genes. Notably, alternative splicing affected protein motifs in 3015 genes, subcellular localizations in 2982 genes and transmembrane domains in 1348 genes. We also identified interesting patterns of alternative splicing, in which two distinct genes seemed to be bridged, nested or having overlapping protein coding sequences (CDSs) of different reading frames (multiple CDS). In these cases, completely unrelated proteins are encoded by a single locus. Genome-wide annotations of alternative splicing, relying on full-length cDNAs, should lay firm groundwork for exploring in detail the diversification of protein function, which is mediated by the fast expanding universe of alternative splicing variants. PMID:16914452

Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis.

PubMed

Grossmann, Steffen; Bauer, Sebastian; Robinson, Peter N; Vingron, Martin

2007-11-15

High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.

Reduce Manual Curation by Combining Gene Predictions from Multiple Annotation Engines, a Case Study of Start Codon Prediction

PubMed Central

Ederveen, Thomas H. A.; Overmars, Lex; van Hijum, Sacha A. F. T.

2013-01-01

Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35–52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity. PMID:23675487

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

PubMed

Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

2017-03-20

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

Variation analysis and gene annotation of eight MHC haplotypes: The MHC Haplotype Project

PubMed Central

Horton, Roger; Gibson, Richard; Coggill, Penny; Miretti, Marcos; Allcock, Richard J.; Almeida, Jeff; Forbes, Simon; Gilbert, James G. R.; Halls, Karen; Harrow, Jennifer L.; Hart, Elizabeth; Howe, Kevin; Jackson, David K.; Palmer, Sophie; Roberts, Anne N.; Sims, Sarah; Stewart, C. Andrew; Traherne, James A.; Trevanion, Steve; Wilming, Laurens; Rogers, Jane; de Jong, Pieter J.; Elliott, John F.; Sawcer, Stephen; Todd, John A.; Trowsdale, John

2008-01-01

The human major histocompatibility complex (MHC) is contained within about 4 Mb on the short arm of chromosome 6 and is recognised as the most variable region in the human genome. The primary aim of the MHC Haplotype Project was to provide a comprehensively annotated reference sequence of a single, human leukocyte antigen-homozygous MHC haplotype and to use it as a basis against which variations could be assessed from seven other similarly homozygous cell lines, representative of the most common MHC haplotypes in the European population. Comparison of the haplotype sequences, including four haplotypes not previously analysed, resulted in the identification of >44,000 variations, both substitutions and indels (insertions and deletions), which have been submitted to the dbSNP database. The gene annotation uncovered haplotype-specific differences and confirmed the presence of more than 300 loci, including over 160 protein-coding genes. Combined analysis of the variation and annotation datasets revealed 122 gene loci with coding substitutions of which 97 were non-synonymous. The haplotype (A3-B7-DR15; PGF cell line) designated as the new MHC reference sequence, has been incorporated into the human genome assembly (NCBI35 and subsequent builds), and constitutes the largest single-haplotype sequence of the human genome to date. The extensive variation and annotation data derived from the analysis of seven further haplotypes have been made publicly available and provide a framework and resource for future association studies of all MHC-associated diseases and transplant medicine. PMID:18193213

RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

DOE PAGES

Brettin, Thomas; Davis, James J.; Disz, Terry; ...

2015-02-10

The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offersmore » a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.« less

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

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

Ansong, Charles; Tolic, Nikola; Purvine, Samuel O.

Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. For example systems biology-oriented genome scale modeling efforts greatly benefit from accurate annotation of protein-coding genes to develop proper functioning models. However, determining protein-coding genes for most new genomes is almost completely performed by inference, using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. With the ability to directly measure peptides arising from expressed proteins, mass spectrometry-based proteomics approaches can be used to augment and verify codingmore » regions of a genomic sequence and importantly detect post-translational processing events. In this study we utilized “shotgun” proteomics to guide accurate primary genome annotation of the bacterial pathogen Salmonella Typhimurium 14028 to facilitate a systems-level understanding of Salmonella biology. The data provides protein-level experimental confirmation for 44% of predicted protein-coding genes, suggests revisions to 48 genes assigned incorrect translational start sites, and uncovers 13 non-annotated genes missed by gene prediction programs. We also present a comprehensive analysis of post-translational processing events in Salmonella, revealing a wide range of complex chemical modifications (70 distinct modifications) and confirming more than 130 signal peptide and N-terminal methionine cleavage events in Salmonella. This study highlights several ways in which proteomics data applied during the primary stages of annotation can improve the quality of genome annotations, especially with regards to the annotation of mature protein products.« less

Enriching the annotation of Mycobacterium tuberculosis H37Rv proteome using remote homology detection approaches: insights into structure and function.

PubMed

Ramakrishnan, Gayatri; Ochoa-Montaño, Bernardo; Raghavender, Upadhyayula S; Mudgal, Richa; Joshi, Adwait G; Chandra, Nagasuma R; Sowdhamini, Ramanathan; Blundell, Tom L; Srinivasan, Narayanaswamy

2015-01-01

The availability of the genome sequence of Mycobacterium tuberculosis H37Rv has encouraged determination of large numbers of protein structures and detailed definition of the biological information encoded therein; yet, the functions of many proteins in M. tuberculosis remain unknown. The emergence of multidrug resistant strains makes it a priority to exploit recent advances in homology recognition and structure prediction to re-analyse its gene products. Here we report the structural and functional characterization of gene products encoded in the M. tuberculosis genome, with the help of sensitive profile-based remote homology search and fold recognition algorithms resulting in an enhanced annotation of the proteome where 95% of the M. tuberculosis proteins were identified wholly or partly with information on structure or function. New information includes association of 244 proteins with 205 domain families and a separate set of new association of folds to 64 proteins. Extending structural information across uncharacterized protein families represented in the M. tuberculosis proteome, by determining superfamily relationships between families of known and unknown structures, has contributed to an enhancement in the knowledge of structural content. In retrospect, such superfamily relationships have facilitated recognition of probable structure and/or function for several uncharacterized protein families, eventually aiding recognition of probable functions for homologous proteins corresponding to such families. Gene products unique to mycobacteria for which no functions could be identified are 183. Of these 18 were determined to be M. tuberculosis specific. Such pathogen-specific proteins are speculated to harbour virulence factors required for pathogenesis. A re-annotated proteome of M. tuberculosis, with greater completeness of annotated proteins and domain assigned regions, provides a valuable basis for experimental endeavours designed to obtain a better

VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment.

PubMed

Habegger, Lukas; Balasubramanian, Suganthi; Chen, David Z; Khurana, Ekta; Sboner, Andrea; Harmanci, Arif; Rozowsky, Joel; Clarke, Declan; Snyder, Michael; Gerstein, Mark

2012-09-01

The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment. VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org.

A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis

PubMed Central

2011-01-01

Background Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches - the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known. Results The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods. Conclusions The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR. Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius

Escherichia coli K-12: a cooperatively developed annotation snapshot—2005

PubMed Central

Riley, Monica; Abe, Takashi; Arnaud, Martha B.; Berlyn, Mary K.B.; Blattner, Frederick R.; Chaudhuri, Roy R.; Glasner, Jeremy D.; Horiuchi, Takashi; Keseler, Ingrid M.; Kosuge, Takehide; Mori, Hirotada; Perna, Nicole T.; Plunkett, Guy; Rudd, Kenneth E.; Serres, Margrethe H.; Thomas, Gavin H.; Thomson, Nicholas R.; Wishart, David; Wanner, Barry L.

2006-01-01

The goal of this group project has been to coordinate and bring up-to-date information on all genes of Escherichia coli K-12. Annotation of the genome of an organism entails identification of genes, the boundaries of genes in terms of precise start and end sites, and description of the gene products. Known and predicted functions were assigned to each gene product on the basis of experimental evidence or sequence analysis. Since both kinds of evidence are constantly expanding, no annotation is complete at any moment in time. This is a snapshot analysis based on the most recent genome sequences of two E.coli K-12 bacteria. An accurate and up-to-date description of E.coli K-12 genes is of particular importance to the scientific community because experimentally determined properties of its gene products provide fundamental information for annotation of innumerable genes of other organisms. Availability of the complete genome sequence of two K-12 strains allows comparison of their genotypes and mutant status of alleles. PMID:16397293

The genome sequence of Leishmania (Leishmania) amazonensis: functional annotation and extended analysis of gene models.

PubMed

Real, Fernando; Vidal, Ramon Oliveira; Carazzolle, Marcelo Falsarella; Mondego, Jorge Maurício Costa; Costa, Gustavo Gilson Lacerda; Herai, Roberto Hirochi; Würtele, Martin; de Carvalho, Lucas Miguel; Carmona e Ferreira, Renata; Mortara, Renato Arruda; Barbiéri, Clara Lucia; Mieczkowski, Piotr; da Silveira, José Franco; Briones, Marcelo Ribeiro da Silva; Pereira, Gonçalo Amarante Guimarães; Bahia, Diana

2013-12-01

We present the sequencing and annotation of the Leishmania (Leishmania) amazonensis genome, an etiological agent of human cutaneous leishmaniasis in the Amazon region of Brazil. L. (L.) amazonensis shares features with Leishmania (L.) mexicana but also exhibits unique characteristics regarding geographical distribution and clinical manifestations of cutaneous lesions (e.g. borderline disseminated cutaneous leishmaniasis). Predicted genes were scored for orthologous gene families and conserved domains in comparison with other human pathogenic Leishmania spp. Carboxypeptidase, aminotransferase, and 3'-nucleotidase genes and ATPase, thioredoxin, and chaperone-related domains were represented more abundantly in L. (L.) amazonensis and L. (L.) mexicana species. Phylogenetic analysis revealed that these two species share groups of amastin surface proteins unique to the genus that could be related to specific features of disease outcomes and host cell interactions. Additionally, we describe a hypothetical hybrid interactome of potentially secreted L. (L.) amazonensis proteins and host proteins under the assumption that parasite factors mimic their mammalian counterparts. The model predicts an interaction between an L. (L.) amazonensis heat-shock protein and mammalian Toll-like receptor 9, which is implicated in important immune responses such as cytokine and nitric oxide production. The analysis presented here represents valuable information for future studies of leishmaniasis pathogenicity and treatment.

The Genome Sequence of Leishmania (Leishmania) amazonensis: Functional Annotation and Extended Analysis of Gene Models

PubMed Central

Real, Fernando; Vidal, Ramon Oliveira; Carazzolle, Marcelo Falsarella; Mondego, Jorge Maurício Costa; Costa, Gustavo Gilson Lacerda; Herai, Roberto Hirochi; Würtele, Martin; de Carvalho, Lucas Miguel; e Ferreira, Renata Carmona; Mortara, Renato Arruda; Barbiéri, Clara Lucia; Mieczkowski, Piotr; da Silveira, José Franco; Briones, Marcelo Ribeiro da Silva; Pereira, Gonçalo Amarante Guimarães; Bahia, Diana

2013-01-01

We present the sequencing and annotation of the Leishmania (Leishmania) amazonensis genome, an etiological agent of human cutaneous leishmaniasis in the Amazon region of Brazil. L. (L.) amazonensis shares features with Leishmania (L.) mexicana but also exhibits unique characteristics regarding geographical distribution and clinical manifestations of cutaneous lesions (e.g. borderline disseminated cutaneous leishmaniasis). Predicted genes were scored for orthologous gene families and conserved domains in comparison with other human pathogenic Leishmania spp. Carboxypeptidase, aminotransferase, and 3′-nucleotidase genes and ATPase, thioredoxin, and chaperone-related domains were represented more abundantly in L. (L.) amazonensis and L. (L.) mexicana species. Phylogenetic analysis revealed that these two species share groups of amastin surface proteins unique to the genus that could be related to specific features of disease outcomes and host cell interactions. Additionally, we describe a hypothetical hybrid interactome of potentially secreted L. (L.) amazonensis proteins and host proteins under the assumption that parasite factors mimic their mammalian counterparts. The model predicts an interaction between an L. (L.) amazonensis heat-shock protein and mammalian Toll-like receptor 9, which is implicated in important immune responses such as cytokine and nitric oxide production. The analysis presented here represents valuable information for future studies of leishmaniasis pathogenicity and treatment. PMID:23857904

Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana

PubMed Central

Itoh, Takeshi; Tanaka, Tsuyoshi; Barrero, Roberto A.; Yamasaki, Chisato; Fujii, Yasuyuki; Hilton, Phillip B.; Antonio, Baltazar A.; Aono, Hideo; Apweiler, Rolf; Bruskiewich, Richard; Bureau, Thomas; Burr, Frances; Costa de Oliveira, Antonio; Fuks, Galina; Habara, Takuya; Haberer, Georg; Han, Bin; Harada, Erimi; Hiraki, Aiko T.; Hirochika, Hirohiko; Hoen, Douglas; Hokari, Hiroki; Hosokawa, Satomi; Hsing, Yue; Ikawa, Hiroshi; Ikeo, Kazuho; Imanishi, Tadashi; Ito, Yukiyo; Jaiswal, Pankaj; Kanno, Masako; Kawahara, Yoshihiro; Kawamura, Toshiyuki; Kawashima, Hiroaki; Khurana, Jitendra P.; Kikuchi, Shoshi; Komatsu, Setsuko; Koyanagi, Kanako O.; Kubooka, Hiromi; Lieberherr, Damien; Lin, Yao-Cheng; Lonsdale, David; Matsumoto, Takashi; Matsuya, Akihiro; McCombie, W. Richard; Messing, Joachim; Miyao, Akio; Mulder, Nicola; Nagamura, Yoshiaki; Nam, Jongmin; Namiki, Nobukazu; Numa, Hisataka; Nurimoto, Shin; O’Donovan, Claire; Ohyanagi, Hajime; Okido, Toshihisa; OOta, Satoshi; Osato, Naoki; Palmer, Lance E.; Quetier, Francis; Raghuvanshi, Saurabh; Saichi, Naomi; Sakai, Hiroaki; Sakai, Yasumichi; Sakata, Katsumi; Sakurai, Tetsuya; Sato, Fumihiko; Sato, Yoshiharu; Schoof, Heiko; Seki, Motoaki; Shibata, Michie; Shimizu, Yuji; Shinozaki, Kazuo; Shinso, Yuji; Singh, Nagendra K.; Smith-White, Brian; Takeda, Jun-ichi; Tanino, Motohiko; Tatusova, Tatiana; Thongjuea, Supat; Todokoro, Fusano; Tsugane, Mika; Tyagi, Akhilesh K.; Vanavichit, Apichart; Wang, Aihui; Wing, Rod A.; Yamaguchi, Kaori; Yamamoto, Mayu; Yamamoto, Naoyuki; Yu, Yeisoo; Zhang, Hao; Zhao, Qiang; Higo, Kenichi; Burr, Benjamin; Gojobori, Takashi; Sasaki, Takuji

2007-01-01

We present here the annotation of the complete genome of rice Oryza sativa L. ssp. japonica cultivar Nipponbare. All functional annotations for proteins and non-protein-coding RNA (npRNA) candidates were manually curated. Functions were identified or inferred in 19,969 (70%) of the proteins, and 131 possible npRNAs (including 58 antisense transcripts) were found. Almost 5000 annotated protein-coding genes were found to be disrupted in insertional mutant lines, which will accelerate future experimental validation of the annotations. The rice loci were determined by using cDNA sequences obtained from rice and other representative cereals. Our conservative estimate based on these loci and an extrapolation suggested that the gene number of rice is ∼32,000, which is smaller than previous estimates. We conducted comparative analyses between rice and Arabidopsis thaliana and found that both genomes possessed several lineage-specific genes, which might account for the observed differences between these species, while they had similar sets of predicted functional domains among the protein sequences. A system to control translational efficiency seems to be conserved across large evolutionary distances. Moreover, the evolutionary process of protein-coding genes was examined. Our results suggest that natural selection may have played a role for duplicated genes in both species, so that duplication was suppressed or favored in a manner that depended on the function of a gene. PMID:17210932

Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis

PubMed Central

Neerincx, Pieter BT; Casel, Pierrot; Prickett, Dennis; Nie, Haisheng; Watson, Michael; Leunissen, Jack AM; Groenen, Martien AM; Klopp, Christophe

2009-01-01

Background Reliable annotation linking oligonucleotide probes to target genes is essential for functional biological analysis of microarray experiments. We used the IMAD, OligoRAP and sigReannot pipelines to update the annotation for the ARK-Genomics Chicken 20 K array as part of a joined EADGENE/SABRE workshop. In this manuscript we compare their annotation strategies and results. Furthermore, we analyse the effect of differences in updated annotation on functional analysis for an experiment involving Eimeria infected chickens and finally we propose guidelines for optimal annotation strategies. Results IMAD, OligoRAP and sigReannot update both annotation and estimated target specificity. The 3 pipelines can assign oligos to target specificity categories although with varying degrees of resolution. Target specificity is judged based on the amount and type of oligo versus target-gene alignments (hits), which are determined by filter thresholds that users can adjust based on their experimental conditions. Linking oligos to annotation on the other hand is based on rigid rules, which differ between pipelines. For 52.7% of the oligos from a subset selected for in depth comparison all pipelines linked to one or more Ensembl genes with consensus on 44.0%. In 31.0% of the cases none of the pipelines could assign an Ensembl gene to an oligo and for the remaining 16.3% the coverage differed between pipelines. Differences in updated annotation were mainly due to different thresholds for hybridisation potential filtering of oligo versus target-gene alignments and different policies for expanding annotation using indirect links. The differences in updated annotation packages had a significant effect on GO term enrichment analysis with consensus on only 67.2% of the enriched terms. Conclusion In addition to flexible thresholds to determine target specificity, annotation tools should provide metadata describing the relationships between oligos and the annotation assigned to them

Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis.

PubMed

Neerincx, Pieter Bt; Casel, Pierrot; Prickett, Dennis; Nie, Haisheng; Watson, Michael; Leunissen, Jack Am; Groenen, Martien Am; Klopp, Christophe

2009-07-16

Reliable annotation linking oligonucleotide probes to target genes is essential for functional biological analysis of microarray experiments. We used the IMAD, OligoRAP and sigReannot pipelines to update the annotation for the ARK-Genomics Chicken 20 K array as part of a joined EADGENE/SABRE workshop. In this manuscript we compare their annotation strategies and results. Furthermore, we analyse the effect of differences in updated annotation on functional analysis for an experiment involving Eimeria infected chickens and finally we propose guidelines for optimal annotation strategies. IMAD, OligoRAP and sigReannot update both annotation and estimated target specificity. The 3 pipelines can assign oligos to target specificity categories although with varying degrees of resolution. Target specificity is judged based on the amount and type of oligo versus target-gene alignments (hits), which are determined by filter thresholds that users can adjust based on their experimental conditions. Linking oligos to annotation on the other hand is based on rigid rules, which differ between pipelines.For 52.7% of the oligos from a subset selected for in depth comparison all pipelines linked to one or more Ensembl genes with consensus on 44.0%. In 31.0% of the cases none of the pipelines could assign an Ensembl gene to an oligo and for the remaining 16.3% the coverage differed between pipelines. Differences in updated annotation were mainly due to different thresholds for hybridisation potential filtering of oligo versus target-gene alignments and different policies for expanding annotation using indirect links. The differences in updated annotation packages had a significant effect on GO term enrichment analysis with consensus on only 67.2% of the enriched terms. In addition to flexible thresholds to determine target specificity, annotation tools should provide metadata describing the relationships between oligos and the annotation assigned to them. These relationships can then

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

PubMed

Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

2016-10-20

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

MetaStorm: A Public Resource for Customizable Metagenomics Annotation

PubMed Central

Arango-Argoty, Gustavo; Singh, Gargi; Heath, Lenwood S.; Pruden, Amy; Xiao, Weidong; Zhang, Liqing

2016-01-01

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution. PMID:27632579

MetaStorm: A Public Resource for Customizable Metagenomics Annotation.

PubMed

Arango-Argoty, Gustavo; Singh, Gargi; Heath, Lenwood S; Pruden, Amy; Xiao, Weidong; Zhang, Liqing

2016-01-01

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution.

Approaches to Fungal Genome Annotation

PubMed Central

Haas, Brian J.; Zeng, Qiandong; Pearson, Matthew D.; Cuomo, Christina A.; Wortman, Jennifer R.

2011-01-01

Fungal genome annotation is the starting point for analysis of genome content. This generally involves the application of diverse methods to identify features on a genome assembly such as protein-coding and non-coding genes, repeats and transposable elements, and pseudogenes. Here we describe tools and methods leveraged for eukaryotic genome annotation with a focus on the annotation of fungal nuclear and mitochondrial genomes. We highlight the application of the latest technologies and tools to improve the quality of predicted gene sets. The Broad Institute eukaryotic genome annotation pipeline is described as one example of how such methods and tools are integrated into a sequencing center’s production genome annotation environment. PMID:22059117

Discovering Functions of Unannotated Genes from a Transcriptome Survey of Wild Fungal Isolates

PubMed Central

Ellison, Christopher E.; Kowbel, David; Glass, N. Louise; Taylor, John W.

2014-01-01

ABSTRACT Most fungal genomes are poorly annotated, and many fungal traits of industrial and biomedical relevance are not well suited to classical genetic screens. Assigning genes to phenotypes on a genomic scale thus remains an urgent need in the field. We developed an approach to infer gene function from expression profiles of wild fungal isolates, and we applied our strategy to the filamentous fungus Neurospora crassa. Using transcriptome measurements in 70 strains from two well-defined clades of this microbe, we first identified 2,247 cases in which the expression of an unannotated gene rose and fell across N. crassa strains in parallel with the expression of well-characterized genes. We then used image analysis of hyphal morphologies, quantitative growth assays, and expression profiling to test the functions of four genes predicted from our population analyses. The results revealed two factors that influenced regulation of metabolism of nonpreferred carbon and nitrogen sources, a gene that governed hyphal architecture, and a gene that mediated amino acid starvation resistance. These findings validate the power of our population-transcriptomic approach for inference of novel gene function, and we suggest that this strategy will be of broad utility for genome-scale annotation in many fungal systems. PMID:24692637

Determining Semantically Related Significant Genes.

PubMed

Taha, Kamal

2014-01-01

GO relation embodies some aspects of existence dependency. If GO term xis existence-dependent on GO term y, the presence of y implies the presence of x. Therefore, the genes annotated with the function of the GO term y are usually functionally and semantically related to the genes annotated with the function of the GO term x. A large number of gene set enrichment analysis methods have been developed in recent years for analyzing gene sets enrichment. However, most of these methods overlook the structural dependencies between GO terms in GO graph by not considering the concept of existence dependency. We propose in this paper a biological search engine called RSGSearch that identifies enriched sets of genes annotated with different functions using the concept of existence dependency. We observe that GO term xcannot be existence-dependent on GO term y, if x- and y- have the same specificity (biological characteristics). After encoding into a numeric format the contributions of GO terms annotating target genes to the semantics of their lowest common ancestors (LCAs), RSGSearch uses microarray experiment to identify the most significant LCA that annotates the result genes. We evaluated RSGSearch experimentally and compared it with five gene set enrichment systems. Results showed marked improvement.

The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation

PubMed Central

Casadio, Rita

2017-01-01

Abstract BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3. PMID:28453653

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis

PubMed Central

Du, Yushen; Wu, Nicholas C.; Jiang, Lin; Zhang, Tianhao; Gong, Danyang; Shu, Sara; Wu, Ting-Ting

2016-01-01

ABSTRACT Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available. PMID:27803181

Improved systematic tRNA gene annotation allows new insights into the evolution of mitochondrial tRNA structures and into the mechanisms of mitochondrial genome rearrangements

PubMed Central

Jühling, Frank; Pütz, Joern; Bernt, Matthias; Donath, Alexander; Middendorf, Martin; Florentz, Catherine; Stadler, Peter F.

2012-01-01

Transfer RNAs (tRNAs) are present in all types of cells as well as in organelles. tRNAs of animal mitochondria show a low level of primary sequence conservation and exhibit ‘bizarre’ secondary structures, lacking complete domains of the common cloverleaf. Such sequences are hard to detect and hence frequently missed in computational analyses and mitochondrial genome annotation. Here, we introduce an automatic annotation procedure for mitochondrial tRNA genes in Metazoa based on sequence and structural information in manually curated covariance models. The method, applied to re-annotate 1876 available metazoan mitochondrial RefSeq genomes, allows to distinguish between remaining functional genes and degrading ‘pseudogenes’, even at early stages of divergence. The subsequent analysis of a comprehensive set of mitochondrial tRNA genes gives new insights into the evolution of structures of mitochondrial tRNA sequences as well as into the mechanisms of genome rearrangements. We find frequent losses of tRNA genes concentrated in basal Metazoa, frequent independent losses of individual parts of tRNA genes, particularly in Arthropoda, and wide-spread conserved overlaps of tRNAs in opposite reading direction. Direct evidence for several recent Tandem Duplication-Random Loss events is gained, demonstrating that this mechanism has an impact on the appearance of new mitochondrial gene orders. PMID:22139921

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

PubMed Central

Overbeek, Ross; Olson, Robert; Pusch, Gordon D.; Olsen, Gary J.; Davis, James J.; Disz, Terry; Edwards, Robert A.; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R.; Xia, Fangfang; Stevens, Rick

2014-01-01

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources. PMID:24293654

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST).

PubMed

Overbeek, Ross; Olson, Robert; Pusch, Gordon D; Olsen, Gary J; Davis, James J; Disz, Terry; Edwards, Robert A; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R; Xia, Fangfang; Stevens, Rick

2014-01-01

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

New Genes and Functional Innovation in Mammals

PubMed Central

Luis Villanueva-Cañas, José; Ruiz-Orera, Jorge; Agea, M. Isabel; Gallo, Maria; Andreu, David

2017-01-01

Abstract The birth of genes that encode new protein sequences is a major source of evolutionary innovation. However, we still understand relatively little about how these genes come into being and which functions they are selected for. To address these questions, we have obtained a large collection of mammalian-specific gene families that lack homologues in other eukaryotic groups. We have combined gene annotations and de novo transcript assemblies from 30 different mammalian species, obtaining ∼6,000 gene families. In general, the proteins in mammalian-specific gene families tend to be short and depleted in aromatic and negatively charged residues. Proteins which arose early in mammalian evolution include milk and skin polypeptides, immune response components, and proteins involved in reproduction. In contrast, the functions of proteins which have a more recent origin remain largely unknown, despite the fact that these proteins also have extensive proteomics support. We identify several previously described cases of genes originated de novo from noncoding genomic regions, supporting the idea that this mechanism frequently underlies the evolution of new protein-coding genes in mammals. Finally, we show that most young mammalian genes are preferentially expressed in testis, suggesting that sexual selection plays an important role in the emergence of new functional genes. PMID:28854603

GO-FAANG meeting: A gathering on functional annotation of animal genomes

USDA-ARS?s Scientific Manuscript database

The FAANG (Functional Annotation of Animal Genomes) Consortium recently held a Gathering On FAANG (GO-FAANG) Workshop in Washington, DC on October 7-8, 2015. This consortium is a grass-roots organization formed to advance the annotation of newly assembled genomes of non-model organisms (www.faang.or...

Fuzzy measures on the Gene Ontology for gene product similarity.

PubMed

Popescu, Mihail; Keller, James M; Mitchell, Joyce A

2006-01-01

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.

A curated catalog of canine and equine keratin genes

PubMed Central

Pujar, Shashikant; McGarvey, Kelly M.; Welle, Monika; Galichet, Arnaud; Müller, Eliane J.; Pruitt, Kim D.; Leeb, Tosso

2017-01-01

Keratins represent a large protein family with essential structural and functional roles in epithelial cells of skin, hair follicles, and other organs. During evolution the genes encoding keratins have undergone multiple rounds of duplication and humans have two clusters with a total of 55 functional keratin genes in their genomes. Due to the high similarity between different keratin paralogs and species-specific differences in gene content, the currently available keratin gene annotation in species with draft genome assemblies such as dog and horse is still imperfect. We compared the National Center for Biotechnology Information (NCBI) (dog annotation release 103, horse annotation release 101) and Ensembl (release 87) gene predictions for the canine and equine keratin gene clusters to RNA-seq data that were generated from adult skin of five dogs and two horses and from adult hair follicle tissue of one dog. Taking into consideration the knowledge on the conserved exon/intron structure of keratin genes, we annotated 61 putatively functional keratin genes in both the dog and horse, respectively. Subsequently, curators in the RefSeq group at NCBI reviewed their annotation of keratin genes in the dog and horse genomes (Annotation Release 104 and Annotation Release 102, respectively) and updated annotation and gene nomenclature of several keratin genes. The updates are now available in the NCBI Gene database (https://www.ncbi.nlm.nih.gov/gene). PMID:28846680

Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins.

PubMed

Das, Sreetama; Bhadra, Pratiti; Ramakumar, Suryanarayanarao; Pal, Debnath

2017-08-04

cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.

Gene Model Annotations for Drosophila melanogaster: The Rule-Benders

PubMed Central

Crosby, Madeline A.; Gramates, L. Sian; dos Santos, Gilberto; Matthews, Beverley B.; St. Pierre, Susan E.; Zhou, Pinglei; Schroeder, Andrew J.; Falls, Kathleen; Emmert, David B.; Russo, Susan M.; Gelbart, William M.

2015-01-01

In the context of the FlyBase annotated gene models in Drosophila melanogaster, we describe the many exceptional cases we have curated from the literature or identified in the course of FlyBase analysis. These range from atypical but common examples such as dicistronic and polycistronic transcripts, noncanonical splices, trans-spliced transcripts, noncanonical translation starts, and stop-codon readthroughs, to single exceptional cases such as ribosomal frameshifting and HAC1-type intron processing. In FlyBase, exceptional genes and transcripts are flagged with Sequence Ontology terms and/or standardized comments. Because some of the rule-benders create problems for handlers of high-throughput data, we discuss plans for flagging these cases in bulk data downloads. PMID:26109356

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

USDA-ARS?s Scientific Manuscript database

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic mode...

Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.

PubMed

Zitnik, Marinka; Zupan, Blaž

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.

VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment

PubMed Central

Habegger, Lukas; Balasubramanian, Suganthi; Chen, David Z.; Khurana, Ekta; Sboner, Andrea; Harmanci, Arif; Rozowsky, Joel; Clarke, Declan; Snyder, Michael; Gerstein, Mark

2012-01-01

Summary: The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment. Availability and Implementation: VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org. Contact: lukas.habegger@yale.edu or mark.gerstein@yale.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22743228

Analysis of mammalian gene function through broad based phenotypic screens across a consortium of mouse clinics

PubMed Central

Adams, David J; Adams, Niels C; Adler, Thure; Aguilar-Pimentel, Antonio; Ali-Hadji, Dalila; Amann, Gregory; André, Philippe; Atkins, Sarah; Auburtin, Aurelie; Ayadi, Abdel; Becker, Julien; Becker, Lore; Bedu, Elodie; Bekeredjian, Raffi; Birling, Marie-Christine; Blake, Andrew; Bottomley, Joanna; Bowl, Mike; Brault, Véronique; Busch, Dirk H; Bussell, James N; Calzada-Wack, Julia; Cater, Heather; Champy, Marie-France; Charles, Philippe; Chevalier, Claire; Chiani, Francesco; Codner, Gemma F; Combe, Roy; Cox, Roger; Dalloneau, Emilie; Dierich, André; Di Fenza, Armida; Doe, Brendan; Duchon, Arnaud; Eickelberg, Oliver; Esapa, Chris T; El Fertak, Lahcen; Feigel, Tanja; Emelyanova, Irina; Estabel, Jeanne; Favor, Jack; Flenniken, Ann; Gambadoro, Alessia; Garrett, Lilian; Gates, Hilary; Gerdin, Anna-Karin; Gkoutos, George; Greenaway, Simon; Glasl, Lisa; Goetz, Patrice; Da Cruz, Isabelle Goncalves; Götz, Alexander; Graw, Jochen; Guimond, Alain; Hans, Wolfgang; Hicks, Geoff; Hölter, Sabine M; Höfler, Heinz; Hancock, John M; Hoehndorf, Robert; Hough, Tertius; Houghton, Richard; Hurt, Anja; Ivandic, Boris; Jacobs, Hughes; Jacquot, Sylvie; Jones, Nora; Karp, Natasha A; Katus, Hugo A; Kitchen, Sharon; Klein-Rodewald, Tanja; Klingenspor, Martin; Klopstock, Thomas; Lalanne, Valerie; Leblanc, Sophie; Lengger, Christoph; le Marchand, Elise; Ludwig, Tonia; Lux, Aline; McKerlie, Colin; Maier, Holger; Mandel, Jean-Louis; Marschall, Susan; Mark, Manuel; Melvin, David G; Meziane, Hamid; Micklich, Kateryna; Mittelhauser, Christophe; Monassier, Laurent; Moulaert, David; Muller, Stéphanie; Naton, Beatrix; Neff, Frauke; Nolan, Patrick M; Nutter, Lauryl MJ; Ollert, Markus; Pavlovic, Guillaume; Pellegata, Natalia S; Peter, Emilie; Petit-Demoulière, Benoit; Pickard, Amanda; Podrini, Christine; Potter, Paul; Pouilly, Laurent; Puk, Oliver; Richardson, David; Rousseau, Stephane; Quintanilla-Fend, Leticia; Quwailid, Mohamed M; Racz, Ildiko; Rathkolb, Birgit; Riet, Fabrice; Rossant, Janet; Roux, Michel; Rozman, Jan; Ryder, Ed; Salisbury, Jennifer; Santos, Luis; Schäble, Karl-Heinz; Schiller, Evelyn; Schrewe, Anja; Schulz, Holger; Steinkamp, Ralf; Simon, Michelle; Stewart, Michelle; Stöger, Claudia; Stöger, Tobias; Sun, Minxuan; Sunter, David; Teboul, Lydia; Tilly, Isabelle; Tocchini-Valentini, Glauco P; Tost, Monica; Treise, Irina; Vasseur, Laurent; Velot, Emilie; Vogt-Weisenhorn, Daniela; Wagner, Christelle; Walling, Alison; Weber, Bruno; Wendling, Olivia; Westerberg, Henrik; Willershäuser, Monja; Wolf, Eckhard; Wolter, Anne; Wood, Joe; Wurst, Wolfgang; Yildirim, Ali Önder; Zeh, Ramona; Zimmer, Andreas; Zimprich, Annemarie

2015-01-01

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse ES cell knockout resource provides a basis for characterisation of relationships between gene and phenotype. The EUMODIC consortium developed and validated robust methodologies for broad-based phenotyping of knockouts through a pipeline comprising 20 disease-orientated platforms. We developed novel statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no prior functional annotation. We captured data from over 27,000 mice finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. Novel phenotypes were uncovered for many genes with unknown function providing a powerful basis for hypothesis generation and further investigation in diverse systems. PMID:26214591

Proteomics and transcriptomics of the BABA-induced resistance response in potato using a novel functional annotation approach

PubMed Central

2014-01-01

Background Induced resistance (IR) can be part of a sustainable plant protection strategy against important plant diseases. β-aminobutyric acid (BABA) can induce resistance in a wide range of plants against several types of pathogens, including potato infected with Phytophthora infestans. However, the molecular mechanisms behind this are unclear and seem to be dependent on the system studied. To elucidate the defence responses activated by BABA in potato, a genome-wide transcript microarray analysis in combination with label-free quantitative proteomics analysis of the apoplast secretome were performed two days after treatment of the leaf canopy with BABA at two concentrations, 1 and 10 mM. Results Over 5000 transcripts were differentially expressed and over 90 secretome proteins changed in abundance indicating a massive activation of defence mechanisms with 10 mM BABA, the concentration effective against late blight disease. To aid analysis, we present a more comprehensive functional annotation of the microarray probes and gene models by retrieving information from orthologous gene families across 26 sequenced plant genomes. The new annotation provided GO terms to 8616 previously un-annotated probes. Conclusions BABA at 10 mM affected several processes related to plant hormones and amino acid metabolism. A major accumulation of PR proteins was also evident, and in the mevalonate pathway, genes involved in sterol biosynthesis were down-regulated, whereas several enzymes involved in the sesquiterpene phytoalexin biosynthesis were up-regulated. Interestingly, abscisic acid (ABA) responsive genes were not as clearly regulated by BABA in potato as previously reported in Arabidopsis. Together these findings provide candidates and markers for improved resistance in potato, one of the most important crops in the world. PMID:24773703

The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation.

PubMed

Profiti, Giuseppe; Martelli, Pier Luigi; Casadio, Rita

2017-07-03

BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

PubMed

Du, Yushen; Wu, Nicholas C; Jiang, Lin; Zhang, Tianhao; Gong, Danyang; Shu, Sara; Wu, Ting-Ting; Sun, Ren

2016-11-01

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available. To fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts.

PubMed

Xu, Lijing; Furlotte, Nicholas; Lin, Yunyue; Heinrich, Kevin; Berry, Michael W; George, Ebenezer O; Homayouni, Ramin

2011-04-14

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. GCAT is freely available at http://binf1.memphis.edu/gcat.

Transcriptional dynamics of the developing sweet cherry (Prunus avium L.) fruit: sequencing, annotation and expression profiling of exocarp-associated genes

PubMed Central

Alkio, Merianne; Jonas, Uwe; Declercq, Myriam; Van Nocker, Steven; Knoche, Moritz

2014-01-01

The exocarp, or skin, of fleshy fruit is a specialized tissue that protects the fruit, attracts seed dispersing fruit eaters, and has large economical relevance for fruit quality. Development of the exocarp involves regulated activities of many genes. This research analyzed global gene expression in the exocarp of developing sweet cherry (Prunus avium L., ‘Regina’), a fruit crop species with little public genomic resources. A catalog of transcript models (contigs) representing expressed genes was constructed from de novo assembled short complementary DNA (cDNA) sequences generated from developing fruit between flowering and maturity at 14 time points. Expression levels in each sample were estimated for 34 695 contigs from numbers of reads mapping to each contig. Contigs were annotated functionally based on BLAST, gene ontology and InterProScan analyses. Coregulated genes were detected using partitional clustering of expression patterns. The results are discussed with emphasis on genes putatively involved in cuticle deposition, cell wall metabolism and sugar transport. The high temporal resolution of the expression patterns presented here reveals finely tuned developmental specialization of individual members of gene families. Moreover, the de novo assembled sweet cherry fruit transcriptome with 7760 full-length protein coding sequences and over 20 000 other, annotated cDNA sequences together with their developmental expression patterns is expected to accelerate molecular research on this important tree fruit crop. PMID:26504533

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.

funRiceGenes dataset for comprehensive understanding and application of rice functional genes.

PubMed

Yao, Wen; Li, Guangwei; Yu, Yiming; Ouyang, Yidan

2018-01-01

As a main staple food, rice is also a model plant for functional genomic studies of monocots. Decoding of every DNA element of the rice genome is essential for genetic improvement to address increasing food demands. The past 15 years have witnessed extraordinary advances in rice functional genomics. Systematic characterization and proper deposition of every rice gene are vital for both functional studies and crop genetic improvement. We built a comprehensive and accurate dataset of ∼2800 functionally characterized rice genes and ∼5000 members of different gene families by integrating data from available databases and reviewing every publication on rice functional genomic studies. The dataset accounts for 19.2% of the 39 045 annotated protein-coding rice genes, which provides the most exhaustive archive for investigating the functions of rice genes. We also constructed 214 gene interaction networks based on 1841 connections between 1310 genes. The largest network with 762 genes indicated that pleiotropic genes linked different biological pathways. Increasing degree of conservation of the flowering pathway was observed among more closely related plants, implying substantial value of rice genes for future dissection of flowering regulation in other crops. All data are deposited in the funRiceGenes database (https://funricegenes.github.io/). Functionality for advanced search and continuous updating of the database are provided by a Shiny application (http://funricegenes.ncpgr.cn/). The funRiceGenes dataset would enable further exploring of the crosslink between gene functions and natural variations in rice, which can also facilitate breeding design to improve target agronomic traits of rice. © The Authors 2017. Published by Oxford University Press.

A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes.

PubMed

Islam, Mohammad Tawhidul; Mohamedali, Abidali; Ahn, Seong Beom; Nawar, Ishmam; Baker, Mark S; Ranganathan, Shoba

2017-01-01

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

Structuring osteosarcoma knowledge: an osteosarcoma-gene association database based on literature mining and manual annotation.

PubMed

Poos, Kathrin; Smida, Jan; Nathrath, Michaela; Maugg, Doris; Baumhoer, Daniel; Neumann, Anna; Korsching, Eberhard

2014-01-01

Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers. Up to now, clinico-pathological factors are the key determinants to guide prognosis and therapeutic treatments. Each day, new studies about OS are published and complicate the acquisition of information to support biomarker discovery and therapeutic improvements. Thus, it is necessary to provide a structured and annotated view on the current OS knowledge that is quick and easily accessible to researchers of the field. Therefore, we developed a publicly available database and Web interface that serves as resource for OS-associated genes and microRNAs. Genes and microRNAs were collected using an automated dictionary-based gene recognition procedure followed by manual review and annotation by experts of the field. In total, 911 genes and 81 microRNAs related to 1331 PubMed abstracts were collected (last update: 29 October 2013). Users can evaluate genes and microRNAs according to their potential prognostic and therapeutic impact, the experimental procedures, the sample types, the biological contexts and microRNA target gene interactions. Additionally, a pathway enrichment analysis of the collected genes highlights different aspects of OS progression. OS requires pathways commonly deregulated in cancer but also features OS-specific alterations like deregulated osteoclast differentiation. To our knowledge, this is the first effort of an OS database containing manual reviewed and annotated up-to-date OS knowledge. It might be a useful resource especially for the bone tumor research community, as specific

Structuring osteosarcoma knowledge: an osteosarcoma-gene association database based on literature mining and manual annotation

PubMed Central

Poos, Kathrin; Smida, Jan; Nathrath, Michaela; Maugg, Doris; Baumhoer, Daniel; Neumann, Anna; Korsching, Eberhard

2014-01-01

Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers. Up to now, clinico-pathological factors are the key determinants to guide prognosis and therapeutic treatments. Each day, new studies about OS are published and complicate the acquisition of information to support biomarker discovery and therapeutic improvements. Thus, it is necessary to provide a structured and annotated view on the current OS knowledge that is quick and easily accessible to researchers of the field. Therefore, we developed a publicly available database and Web interface that serves as resource for OS-associated genes and microRNAs. Genes and microRNAs were collected using an automated dictionary-based gene recognition procedure followed by manual review and annotation by experts of the field. In total, 911 genes and 81 microRNAs related to 1331 PubMed abstracts were collected (last update: 29 October 2013). Users can evaluate genes and microRNAs according to their potential prognostic and therapeutic impact, the experimental procedures, the sample types, the biological contexts and microRNA target gene interactions. Additionally, a pathway enrichment analysis of the collected genes highlights different aspects of OS progression. OS requires pathways commonly deregulated in cancer but also features OS-specific alterations like deregulated osteoclast differentiation. To our knowledge, this is the first effort of an OS database containing manual reviewed and annotated up-to-date OS knowledge. It might be a useful resource especially for the bone tumor research community, as specific

New Genes and Functional Innovation in Mammals.

PubMed

Luis Villanueva-Cañas, José; Ruiz-Orera, Jorge; Agea, M Isabel; Gallo, Maria; Andreu, David; Albà, M Mar

2017-07-01

The birth of genes that encode new protein sequences is a major source of evolutionary innovation. However, we still understand relatively little about how these genes come into being and which functions they are selected for. To address these questions, we have obtained a large collection of mammalian-specific gene families that lack homologues in other eukaryotic groups. We have combined gene annotations and de novo transcript assemblies from 30 different mammalian species, obtaining ∼6,000 gene families. In general, the proteins in mammalian-specific gene families tend to be short and depleted in aromatic and negatively charged residues. Proteins which arose early in mammalian evolution include milk and skin polypeptides, immune response components, and proteins involved in reproduction. In contrast, the functions of proteins which have a more recent origin remain largely unknown, despite the fact that these proteins also have extensive proteomics support. We identify several previously described cases of genes originated de novo from noncoding genomic regions, supporting the idea that this mechanism frequently underlies the evolution of new protein-coding genes in mammals. Finally, we show that most young mammalian genes are preferentially expressed in testis, suggesting that sexual selection plays an important role in the emergence of new functional genes. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle

PubMed Central

Crispim, Aline Camporez; Kelly, Matthew John; Guimarães, Simone Eliza Facioni; e Silva, Fabyano Fonseca; Fortes, Marina Rufino Salinas; Wenceslau, Raphael Rocha; Moore, Stephen

2015-01-01

Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to growth processes in beef cattle. The Brody model presented the best goodness of fit, and the heritability values for the parameter estimates for mature weight (A) and maturity rate (K) were 0.23 and 0.32, respectively, proving that these traits can be a feasible alternative when the objective is to change the shape of growth curves within genetic improvement programs. The genetic correlation between A and K was -0.84, indicating that animals with lower mature body weights reached that weight at younger ages. One hundred and sixty seven (167) and two hundred and sixty two (262) significant SNPs were associated with A and K, respectively. The annotated genes closest to the most significant SNPs for A had direct biological functions related to muscle development (RAB28), myogenic induction (BTG1), fetal growth (IL2), and body weights (APEX2); K genes were functionally associated with body weight, body height, average daily gain (TMEM18), and skeletal muscle development (SMN1). Candidate

Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle.

PubMed

Crispim, Aline Camporez; Kelly, Matthew John; Guimarães, Simone Eliza Facioni; Fonseca e Silva, Fabyano; Fortes, Marina Rufino Salinas; Wenceslau, Raphael Rocha; Moore, Stephen

2015-01-01

Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to growth processes in beef cattle. The Brody model presented the best goodness of fit, and the heritability values for the parameter estimates for mature weight (A) and maturity rate (K) were 0.23 and 0.32, respectively, proving that these traits can be a feasible alternative when the objective is to change the shape of growth curves within genetic improvement programs. The genetic correlation between A and K was -0.84, indicating that animals with lower mature body weights reached that weight at younger ages. One hundred and sixty seven (167) and two hundred and sixty two (262) significant SNPs were associated with A and K, respectively. The annotated genes closest to the most significant SNPs for A had direct biological functions related to muscle development (RAB28), myogenic induction (BTG1), fetal growth (IL2), and body weights (APEX2); K genes were functionally associated with body weight, body height, average daily gain (TMEM18), and skeletal muscle development (SMN1). Candidate

Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics.

PubMed

de Angelis, Martin Hrabě; Nicholson, George; Selloum, Mohammed; White, Jacqui; Morgan, Hugh; Ramirez-Solis, Ramiro; Sorg, Tania; Wells, Sara; Fuchs, Helmut; Fray, Martin; Adams, David J; Adams, Niels C; Adler, Thure; Aguilar-Pimentel, Antonio; Ali-Hadji, Dalila; Amann, Gregory; André, Philippe; Atkins, Sarah; Auburtin, Aurelie; Ayadi, Abdel; Becker, Julien; Becker, Lore; Bedu, Elodie; Bekeredjian, Raffi; Birling, Marie-Christine; Blake, Andrew; Bottomley, Joanna; Bowl, Mike; Brault, Véronique; Busch, Dirk H; Bussell, James N; Calzada-Wack, Julia; Cater, Heather; Champy, Marie-France; Charles, Philippe; Chevalier, Claire; Chiani, Francesco; Codner, Gemma F; Combe, Roy; Cox, Roger; Dalloneau, Emilie; Dierich, André; Di Fenza, Armida; Doe, Brendan; Duchon, Arnaud; Eickelberg, Oliver; Esapa, Chris T; El Fertak, Lahcen; Feigel, Tanja; Emelyanova, Irina; Estabel, Jeanne; Favor, Jack; Flenniken, Ann; Gambadoro, Alessia; Garrett, Lilian; Gates, Hilary; Gerdin, Anna-Karin; Gkoutos, George; Greenaway, Simon; Glasl, Lisa; Goetz, Patrice; Da Cruz, Isabelle Goncalves; Götz, Alexander; Graw, Jochen; Guimond, Alain; Hans, Wolfgang; Hicks, Geoff; Hölter, Sabine M; Höfler, Heinz; Hancock, John M; Hoehndorf, Robert; Hough, Tertius; Houghton, Richard; Hurt, Anja; Ivandic, Boris; Jacobs, Hughes; Jacquot, Sylvie; Jones, Nora; Karp, Natasha A; Katus, Hugo A; Kitchen, Sharon; Klein-Rodewald, Tanja; Klingenspor, Martin; Klopstock, Thomas; Lalanne, Valerie; Leblanc, Sophie; Lengger, Christoph; le Marchand, Elise; Ludwig, Tonia; Lux, Aline; McKerlie, Colin; Maier, Holger; Mandel, Jean-Louis; Marschall, Susan; Mark, Manuel; Melvin, David G; Meziane, Hamid; Micklich, Kateryna; Mittelhauser, Christophe; Monassier, Laurent; Moulaert, David; Muller, Stéphanie; Naton, Beatrix; Neff, Frauke; Nolan, Patrick M; Nutter, Lauryl Mj; Ollert, Markus; Pavlovic, Guillaume; Pellegata, Natalia S; Peter, Emilie; Petit-Demoulière, Benoit; Pickard, Amanda; Podrini, Christine; Potter, Paul; Pouilly, Laurent; Puk, Oliver; Richardson, David; Rousseau, Stephane; Quintanilla-Fend, Leticia; Quwailid, Mohamed M; Racz, Ildiko; Rathkolb, Birgit; Riet, Fabrice; Rossant, Janet; Roux, Michel; Rozman, Jan; Ryder, Ed; Salisbury, Jennifer; Santos, Luis; Schäble, Karl-Heinz; Schiller, Evelyn; Schrewe, Anja; Schulz, Holger; Steinkamp, Ralf; Simon, Michelle; Stewart, Michelle; Stöger, Claudia; Stöger, Tobias; Sun, Minxuan; Sunter, David; Teboul, Lydia; Tilly, Isabelle; Tocchini-Valentini, Glauco P; Tost, Monica; Treise, Irina; Vasseur, Laurent; Velot, Emilie; Vogt-Weisenhorn, Daniela; Wagner, Christelle; Walling, Alison; Weber, Bruno; Wendling, Olivia; Westerberg, Henrik; Willershäuser, Monja; Wolf, Eckhard; Wolter, Anne; Wood, Joe; Wurst, Wolfgang; Yildirim, Ali Önder; Zeh, Ramona; Zimmer, Andreas; Zimprich, Annemarie; Holmes, Chris; Steel, Karen P; Herault, Yann; Gailus-Durner, Valérie; Mallon, Ann-Marie; Brown, Steve Dm

2015-09-01

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse embryonic stem cell knockout resource provides a basis for the characterization of relationships between genes and phenotypes. The EUMODIC consortium developed and validated robust methodologies for the broad-based phenotyping of knockouts through a pipeline comprising 20 disease-oriented platforms. We developed new statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no previous functional annotation. We captured data from over 27,000 mice, finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. New phenotypes were uncovered for many genes with previously unknown function, providing a powerful basis for hypothesis generation and further investigation in diverse systems.

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

PubMed Central

2013-01-01

Background Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale. Results We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters. Conclusions M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http

New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'.

PubMed

Bromberg, Yana; Yachdav, Guy; Ofran, Yanay; Schneider, Reinhard; Rost, Burkhard

2009-05-01

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones

PubMed Central

Imanishi, Tadashi; Itoh, Takeshi; Suzuki, Yutaka; O'Donovan, Claire; Fukuchi, Satoshi; Koyanagi, Kanako O; Barrero, Roberto A; Tamura, Takuro; Yamaguchi-Kabata, Yumi; Tanino, Motohiko; Yura, Kei; Miyazaki, Satoru; Ikeo, Kazuho; Homma, Keiichi; Kasprzyk, Arek; Nishikawa, Tetsuo; Hirakawa, Mika; Thierry-Mieg, Jean; Thierry-Mieg, Danielle; Ashurst, Jennifer; Jia, Libin; Nakao, Mitsuteru; Thomas, Michael A; Mulder, Nicola; Karavidopoulou, Youla; Jin, Lihua; Kim, Sangsoo; Yasuda, Tomohiro; Lenhard, Boris; Eveno, Eric; Suzuki, Yoshiyuki; Yamasaki, Chisato; Takeda, Jun-ichi; Gough, Craig; Hilton, Phillip; Fujii, Yasuyuki; Sakai, Hiroaki; Tanaka, Susumu; Amid, Clara; Bellgard, Matthew; Bonaldo, Maria de Fatima; Bono, Hidemasa; Bromberg, Susan K; Brookes, Anthony J; Bruford, Elspeth; Carninci, Piero; Chelala, Claude; Couillault, Christine; de Souza, Sandro J.; Debily, Marie-Anne; Devignes, Marie-Dominique; Dubchak, Inna; Endo, Toshinori; Estreicher, Anne; Eyras, Eduardo; Fukami-Kobayashi, Kaoru; R. Gopinath, Gopal; Graudens, Esther; Hahn, Yoonsoo; Han, Michael; Han, Ze-Guang; Hanada, Kousuke; Hanaoka, Hideki; Harada, Erimi; Hashimoto, Katsuyuki; Hinz, Ursula; Hirai, Momoki; Hishiki, Teruyoshi; Hopkinson, Ian; Imbeaud, Sandrine; Inoko, Hidetoshi; Kanapin, Alexander; Kaneko, Yayoi; Kasukawa, Takeya; Kelso, Janet; Kersey, Paul; Kikuno, Reiko; Kimura, Kouichi; Korn, Bernhard; Kuryshev, Vladimir; Makalowska, Izabela; Makino, Takashi; Mano, Shuhei; Mariage-Samson, Regine; Mashima, Jun; Matsuda, Hideo; Mewes, Hans-Werner; Minoshima, Shinsei; Nagai, Keiichi; Nagasaki, Hideki; Nagata, Naoki; Nigam, Rajni; Ogasawara, Osamu; Ohara, Osamu; Ohtsubo, Masafumi; Okada, Norihiro; Okido, Toshihisa; Oota, Satoshi; Ota, Motonori; Ota, Toshio; Otsuki, Tetsuji; Piatier-Tonneau, Dominique; Poustka, Annemarie; Ren, Shuang-Xi; Saitou, Naruya; Sakai, Katsunaga; Sakamoto, Shigetaka; Sakate, Ryuichi; Schupp, Ingo; Servant, Florence; Sherry, Stephen; Shiba, Rie; Shimizu, Nobuyoshi; Shimoyama, Mary; Simpson, Andrew J; Soares, Bento; Steward, Charles; Suwa, Makiko; Suzuki, Mami; Takahashi, Aiko; Tamiya, Gen; Tanaka, Hiroshi; Taylor, Todd; Terwilliger, Joseph D; Unneberg, Per; Veeramachaneni, Vamsi; Watanabe, Shinya; Wilming, Laurens; Yasuda, Norikazu; Yoo, Hyang-Sook; Stodolsky, Marvin; Makalowski, Wojciech; Go, Mitiko; Nakai, Kenta; Takagi, Toshihisa; Kanehisa, Minoru; Sakaki, Yoshiyuki; Quackenbush, John; Okazaki, Yasushi; Hayashizaki, Yoshihide; Hide, Winston; Chakraborty, Ranajit; Nishikawa, Ken; Sugawara, Hideaki; Tateno, Yoshio; Chen, Zhu; Oishi, Michio; Tonellato, Peter; Apweiler, Rolf; Okubo, Kousaku; Wagner, Lukas; Wiemann, Stefan; Strausberg, Robert L; Isogai, Takao; Auffray, Charles; Nomura, Nobuo; Sugano, Sumio

2004-01-01

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In

Extensive complementarity between gene function prediction methods.

PubMed

Vidulin, Vedrana; Šmuc, Tomislav; Supek, Fran

2016-12-01

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them. The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials 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.

Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3.

PubMed

Han, Mira V; Thomas, Gregg W C; Lugo-Martinez, Jose; Hahn, Matthew W

2013-08-01

Current sequencing methods produce large amounts of data, but genome assemblies constructed from these data are often fragmented and incomplete. Incomplete and error-filled assemblies result in many annotation errors, especially in the number of genes present in a genome. This means that methods attempting to estimate rates of gene duplication and loss often will be misled by such errors and that rates of gene family evolution will be consistently overestimated. Here, we present a method that takes these errors into account, allowing one to accurately infer rates of gene gain and loss among genomes even with low assembly and annotation quality. The method is implemented in the newest version of the software package CAFE, along with several other novel features. We demonstrate the accuracy of the method with extensive simulations and reanalyze several previously published data sets. Our results show that errors in genome annotation do lead to higher inferred rates of gene gain and loss but that CAFE 3 sufficiently accounts for these errors to provide accurate estimates of important evolutionary parameters.

Optimizing high performance computing workflow for protein functional annotation

PubMed Central

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-01-01

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. PMID:25313296

Optimizing high performance computing workflow for protein functional annotation.

PubMed

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-09-10

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

Functional annotation of chemical libraries across diverse biological processes.

PubMed

Piotrowski, Jeff S; Li, Sheena C; Deshpande, Raamesh; Simpkins, Scott W; Nelson, Justin; Yashiroda, Yoko; Barber, Jacqueline M; Safizadeh, Hamid; Wilson, Erin; Okada, Hiroki; Gebre, Abraham A; Kubo, Karen; Torres, Nikko P; LeBlanc, Marissa A; Andrusiak, Kerry; Okamoto, Reika; Yoshimura, Mami; DeRango-Adem, Eva; van Leeuwen, Jolanda; Shirahige, Katsuhiko; Baryshnikova, Anastasia; Brown, Grant W; Hirano, Hiroyuki; Costanzo, Michael; Andrews, Brenda; Ohya, Yoshikazu; Osada, Hiroyuki; Yoshida, Minoru; Myers, Chad L; Boone, Charles

2017-09-01

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

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.

Modularity-like objective function in annotated networks

NASA Astrophysics Data System (ADS)

Xie, Jia-Rong; Wang, Bing-Hong

2017-12-01

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

BG7: A New Approach for Bacterial Genome Annotation Designed for Next Generation Sequencing Data

PubMed Central

Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Pareja, Eduardo; Tobes, Raquel

2012-01-01

BG7 is a new system for de novo bacterial, archaeal and viral genome annotation based on a new approach specifically designed for annotating genomes sequenced with next generation sequencing technologies. The system is versatile and able to annotate genes even in the step of preliminary assembly of the genome. It is especially efficient detecting unexpected genes horizontally acquired from bacterial or archaeal distant genomes, phages, plasmids, and mobile elements. From the initial phases of the gene annotation process, BG7 exploits the massive availability of annotated protein sequences in databases. BG7 predicts ORFs and infers their function based on protein similarity with a wide set of reference proteins, integrating ORF prediction and functional annotation phases in just one step. BG7 is especially tolerant to sequencing errors in start and stop codons, to frameshifts, and to assembly or scaffolding errors. The system is also tolerant to the high level of gene fragmentation which is frequently found in not fully assembled genomes. BG7 current version – which is developed in Java, takes advantage of Amazon Web Services (AWS) cloud computing features, but it can also be run locally in any operating system. BG7 is a fast, automated and scalable system that can cope with the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies. Its capabilities and efficiency were demonstrated in the 2011 EHEC Germany outbreak in which BG7 was used to get the first annotations right the next day after the first entero-hemorrhagic E. coli genome sequences were made publicly available. The suitability of BG7 for genome annotation has been proved for Illumina, 454, Ion Torrent, and PacBio sequencing technologies. Besides, thanks to its plasticity, our system could be very easily adapted to work with new technologies in the future. PMID:23185310

Discovering functions of unannotated genes from a transcriptome survey of wild fungal isolates.

PubMed

Ellison, Christopher E; Kowbel, David; Glass, N Louise; Taylor, John W; Brem, Rachel B

2014-04-01

Most fungal genomes are poorly annotated, and many fungal traits of industrial and biomedical relevance are not well suited to classical genetic screens. Assigning genes to phenotypes on a genomic scale thus remains an urgent need in the field. We developed an approach to infer gene function from expression profiles of wild fungal isolates, and we applied our strategy to the filamentous fungus Neurospora crassa. Using transcriptome measurements in 70 strains from two well-defined clades of this microbe, we first identified 2,247 cases in which the expression of an unannotated gene rose and fell across N. crassa strains in parallel with the expression of well-characterized genes. We then used image analysis of hyphal morphologies, quantitative growth assays, and expression profiling to test the functions of four genes predicted from our population analyses. The results revealed two factors that influenced regulation of metabolism of nonpreferred carbon and nitrogen sources, a gene that governed hyphal architecture, and a gene that mediated amino acid starvation resistance. These findings validate the power of our population-transcriptomic approach for inference of novel gene function, and we suggest that this strategy will be of broad utility for genome-scale annotation in many fungal systems. IMPORTANCE Some fungal species cause deadly infections in humans or crop plants, and other fungi are workhorses of industrial chemistry, including the production of biofuels. Advances in medical and industrial mycology require an understanding of the genes that control fungal traits. We developed a method to infer functions of uncharacterized genes by observing correlated expression of their mRNAs with those of known genes across wild fungal isolates. We applied this strategy to a filamentous fungus and predicted functions for thousands of unknown genes. In four cases, we experimentally validated the predictions from our method, discovering novel genes involved in the

A computational platform to maintain and migrate manual functional annotations for BioCyc databases.

PubMed

Walsh, Jesse R; Sen, Taner Z; Dickerson, Julie A

2014-10-12

BioCyc databases are an important resource for information on biological pathways and genomic data. Such databases represent the accumulation of biological data, some of which has been manually curated from literature. An essential feature of these databases is the continuing data integration as new knowledge is discovered. As functional annotations are improved, scalable methods are needed for curators to manage annotations without detailed knowledge of the specific design of the BioCyc database. We have developed CycTools, a software tool which allows curators to maintain functional annotations in a model organism database. This tool builds on existing software to improve and simplify annotation data imports of user provided data into BioCyc databases. Additionally, CycTools automatically resolves synonyms and alternate identifiers contained within the database into the appropriate internal identifiers. Automating steps in the manual data entry process can improve curation efforts for major biological databases. The functionality of CycTools is demonstrated by transferring GO term annotations from MaizeCyc to matching proteins in CornCyc, both maize metabolic pathway databases available at MaizeGDB, and by creating strain specific databases for metabolic engineering.

The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction

PubMed Central

2012-01-01

Background Physalis peruviana commonly known as Cape gooseberry is a member of the Solanaceae family that has an increasing popularity due to its nutritional and medicinal values. A broad range of genomic tools is available for other Solanaceae, including tomato and potato. However, limited genomic resources are currently available for Cape gooseberry. Results We report the generation of a total of 652,614 P. peruviana Expressed Sequence Tags (ESTs), using 454 GS FLX Titanium technology. ESTs, with an average length of 371 bp, were obtained from a normalized leaf cDNA library prepared using a Colombian commercial variety. De novo assembling was performed to generate a collection of 24,014 isotigs and 110,921 singletons, with an average length of 1,638 bp and 354 bp, respectively. Functional annotation was performed using NCBI’s BLAST tools and Blast2GO, which identified putative functions for 21,191 assembled sequences, including gene families involved in all the major biological processes and molecular functions as well as defense response and amino acid metabolism pathways. Gene model predictions in P. peruviana were obtained by using the genomes of Solanum lycopersicum (tomato) and Solanum tuberosum (potato). We predict 9,436 P. peruviana sequences with multiple-exon models and conserved intron positions with respect to the potato and tomato genomes. Additionally, to study species diversity we developed 5,971 SSR markers from assembled ESTs. Conclusions We present the first comprehensive analysis of the Physalis peruviana leaf transcriptome, which will provide valuable resources for development of genetic tools in the species. Assembled transcripts with gene models could serve as potential candidates for marker discovery with a variety of applications including: functional diversity, conservation and improvement to increase productivity and fruit quality. P. peruviana was estimated to be phylogenetically branched out before the divergence of five other

The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction.

PubMed

Garzón-Martínez, Gina A; Zhu, Z Iris; Landsman, David; Barrero, Luz S; Mariño-Ramírez, Leonardo

2012-04-25

Physalis peruviana commonly known as Cape gooseberry is a member of the Solanaceae family that has an increasing popularity due to its nutritional and medicinal values. A broad range of genomic tools is available for other Solanaceae, including tomato and potato. However, limited genomic resources are currently available for Cape gooseberry. We report the generation of a total of 652,614 P. peruviana Expressed Sequence Tags (ESTs), using 454 GS FLX Titanium technology. ESTs, with an average length of 371 bp, were obtained from a normalized leaf cDNA library prepared using a Colombian commercial variety. De novo assembling was performed to generate a collection of 24,014 isotigs and 110,921 singletons, with an average length of 1,638 bp and 354 bp, respectively. Functional annotation was performed using NCBI's BLAST tools and Blast2GO, which identified putative functions for 21,191 assembled sequences, including gene families involved in all the major biological processes and molecular functions as well as defense response and amino acid metabolism pathways. Gene model predictions in P. peruviana were obtained by using the genomes of Solanum lycopersicum (tomato) and Solanum tuberosum (potato). We predict 9,436 P. peruviana sequences with multiple-exon models and conserved intron positions with respect to the potato and tomato genomes. Additionally, to study species diversity we developed 5,971 SSR markers from assembled ESTs. We present the first comprehensive analysis of the Physalis peruviana leaf transcriptome, which will provide valuable resources for development of genetic tools in the species. Assembled transcripts with gene models could serve as potential candidates for marker discovery with a variety of applications including: functional diversity, conservation and improvement to increase productivity and fruit quality. P. peruviana was estimated to be phylogenetically branched out before the divergence of five other Solanaceae family members, S

De novo assembly and functional annotation of Myrciaria dubia fruit transcriptome reveals multiple metabolic pathways for L-ascorbic acid biosynthesis.

PubMed

Castro, Juan C; Maddox, J Dylan; Cobos, Marianela; Requena, David; Zimic, Mirko; Bombarely, Aureliano; Imán, Sixto A; Cerdeira, Luis A; Medina, Andersson E

2015-11-24

Myrciaria dubia is an Amazonian fruit shrub that produces numerous bioactive phytochemicals, but is best known by its high L-ascorbic acid (AsA) content in fruits. Pronounced variation in AsA content has been observed both within and among individuals, but the genetic factors responsible for this variation are largely unknown. The goals of this research, therefore, were to assemble, characterize, and annotate the fruit transcriptome of M. dubia in order to reconstruct metabolic pathways and determine if multiple pathways contribute to AsA biosynthesis. In total 24,551,882 high-quality sequence reads were de novo assembled into 70,048 unigenes (mean length = 1150 bp, N50 = 1775 bp). Assembled sequences were annotated using BLASTX against public databases such as TAIR, GR-protein, FB, MGI, RGD, ZFIN, SGN, WB, TIGR_CMR, and JCVI-CMR with 75.2 % of unigenes having annotations. Of the three core GO annotation categories, biological processes comprised 53.6 % of the total assigned annotations, whereas cellular components and molecular functions comprised 23.3 and 23.1 %, respectively. Based on the KEGG pathway assignment of the functionally annotated transcripts, five metabolic pathways for AsA biosynthesis were identified: animal-like pathway, myo-inositol pathway, L-gulose pathway, D-mannose/L-galactose pathway, and uronic acid pathway. All transcripts coding enzymes involved in the ascorbate-glutathione cycle were also identified. Finally, we used the assembly to identified 6314 genic microsatellites and 23,481 high quality SNPs. This study describes the first next-generation sequencing effort and transcriptome annotation of a non-model Amazonian plant that is relevant for AsA production and other bioactive phytochemicals. Genes encoding key enzymes were successfully identified and metabolic pathways involved in biosynthesis of AsA, anthocyanins, and other metabolic pathways have been reconstructed. The identification of these genes and pathways is in agreement with

MATRIX FACTORIZATION-BASED DATA FUSION FOR GENE FUNCTION PREDICTION IN BAKER’S YEAST AND SLIME MOLD

PubMed Central

ŽITNIK, MARINKA; ZUPAN, BLAŽ

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker’s yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps. PMID:24297565

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes.

PubMed

Osato, Naoki

2018-01-19

Transcriptional target genes show functional enrichment of genes. However, how many and how significantly transcriptional target genes include functional enrichments are still unclear. To address these issues, I predicted human transcriptional target genes using open chromatin regions, ChIP-seq data and DNA binding sequences of transcription factors in databases, and examined functional enrichment and gene expression level of putative transcriptional target genes. Gene Ontology annotations showed four times larger numbers of functional enrichments in putative transcriptional target genes than gene expression information alone, independent of transcriptional target genes. To compare the number of functional enrichments of putative transcriptional target genes between cells or search conditions, I normalized the number of functional enrichment by calculating its ratios in the total number of transcriptional target genes. With this analysis, native putative transcriptional target genes showed the largest normalized number of functional enrichments, compared with target genes including 5-60% of randomly selected genes. The normalized number of functional enrichments was changed according to the criteria of enhancer-promoter interactions such as distance from transcriptional start sites and orientation of CTCF-binding sites. Forward-reverse orientation of CTCF-binding sites showed significantly higher normalized number of functional enrichments than the other orientations. Journal papers showed that the top five frequent functional enrichments were related to the cellular functions in the three cell types. The median expression level of transcriptional target genes changed according to the criteria of enhancer-promoter assignments (i.e. interactions) and was correlated with the changes of the normalized number of functional enrichments of transcriptional target genes. Human putative transcriptional target genes showed significant functional enrichments. Functional

RNA-Seq analysis and annotation of a draft blueberry genome assembly identifies candidate genes involved in fruit ripening, biosynthesis of bioactive compounds, and stage-specific alternative splicing.

PubMed

Gupta, Vikas; Estrada, April D; Blakley, Ivory; Reid, Rob; Patel, Ketan; Meyer, Mason D; Andersen, Stig Uggerhøj; Brown, Allan F; Lila, Mary Ann; Loraine, Ann E

2015-01-01

Blueberries are a rich source of antioxidants and other beneficial compounds that can protect against disease. Identifying genes involved in synthesis of bioactive compounds could enable the breeding of berry varieties with enhanced health benefits. Toward this end, we annotated a previously sequenced draft blueberry genome assembly using RNA-Seq data from five stages of berry fruit development and ripening. Genome-guided assembly of RNA-Seq read alignments combined with output from ab initio gene finders produced around 60,000 gene models, of which more than half were similar to proteins from other species, typically the grape Vitis vinifera. Comparison of gene models to the PlantCyc database of metabolic pathway enzymes identified candidate genes involved in synthesis of bioactive compounds, including bixin, an apocarotenoid with potential disease-fighting properties, and defense-related cyanogenic glycosides, which are toxic. Cyanogenic glycoside (CG) biosynthetic enzymes were highly expressed in green fruit, and a candidate CG detoxification enzyme was up-regulated during fruit ripening. Candidate genes for ethylene, anthocyanin, and 400 other biosynthetic pathways were also identified. Homology-based annotation using Blast2GO and InterPro assigned Gene Ontology terms to around 15,000 genes. RNA-Seq expression profiling showed that blueberry growth, maturation, and ripening involve dynamic gene expression changes, including coordinated up- and down-regulation of metabolic pathway enzymes and transcriptional regulators. Analysis of RNA-seq alignments identified developmentally regulated alternative splicing, promoter use, and 3' end formation. We report genome sequence, gene models, functional annotations, and RNA-Seq expression data that provide an important new resource enabling high throughput studies in blueberry.

MEETING: Chlamydomonas Annotation Jamboree - October 2003

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

Grossman, Arthur R

2007-04-13

Shotgun sequencing of the nuclear genome of Chlamydomonas reinhardtii (Chlamydomonas throughout) was performed at an approximate 10X coverage by JGI. Roughly half of the genome is now contained on 26 scaffolds, all of which are at least 1.6 Mb, and the coverage of the genome is ~95%. There are now over 200,000 cDNA sequence reads that we have generated as part of the Chlamydomonas genome project (Grossman, 2003; Shrager et al., 2003; Grossman et al. 2007; Merchant et al., 2007); other sequences have also been generated by the Kasuza sequence group (Asamizu et al., 1999; Asamizu et al., 2000) ormore » individual laboratories that have focused on specific genes. Shrager et al. (2003) placed the reads into distinct contigs (an assemblage of reads with overlapping nucleotide sequences), and contigs that group together as part of the same genes have been designated ACEs (assembly of contigs generated from EST information). All of the reads have also been mapped to the Chlamydomonas nuclear genome and the cDNAs and their corresponding genomic sequences have been reassembled, and the resulting assemblage is called an ACEG (an Assembly of contiguous EST sequences supported by genomic sequence) (Jain et al., 2007). Most of the unique genes or ACEGs are also represented by gene models that have been generated by the Joint Genome Institute (JGI, Walnut Creek, CA). These gene models have been placed onto the DNA scaffolds and are presented as a track on the Chlamydomonas genome browser associated with the genome portal (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html). Ultimately, the meeting grant awarded by DOE has helped enormously in the development of an annotation pipeline (a set of guidelines used in the annotation of genes) and resulted in high quality annotation of over 4,000 genes; the annotators were from both Europe and the USA. Some of the people who led the annotation initiative were Arthur Grossman, Olivier Vallon, and Sabeeha Merchant (with many individual

Current challenges in genome annotation through structural biology and bioinformatics.

PubMed

Furnham, Nicholas; de Beer, Tjaart A P; Thornton, Janet M

2012-10-01

With the huge volume in genomic sequences being generated from high-throughout sequencing projects the requirement for providing accurate and detailed annotations of gene products has never been greater. It is proving to be a huge challenge for computational biologists to use as much information as possible from experimental data to provide annotations for genome data of unknown function. A central component to this process is to use experimentally determined structures, which provide a means to detect homology that is not discernable from just the sequence and permit the consequences of genomic variation to be realized at the molecular level. In particular, structures also form the basis of many bioinformatics methods for improving the detailed functional annotations of enzymes in combination with similarities in sequence and chemistry. Copyright © 2012. Published by Elsevier Ltd.

A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

PubMed

Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

2015-05-27

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures.

PubMed

Mazandu, Gaston K; Mulder, Nicola J

2013-09-25

The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore these different GO similarity measure approaches and their biological applications. We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which incorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis.

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

PubMed Central

Zheng, Jie; Stoyanovich, Julia; Manduchi, Elisabetta; Liu, Junmin; Stoeckert, Christian J.

2011-01-01

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute. Database URL: http://www.cbil.upenn.edu/annotCompute/ PMID:22190598

Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics.

PubMed

Chen, Wenan; McDonnell, Shannon K; Thibodeau, Stephen N; Tillmans, Lori S; Schaid, Daniel J

2016-11-01

Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities. Copyright © 2016 by the Genetics Society of America.

Mining a database of single amplified genomes from Red Sea brine pool extremophiles—improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA)

PubMed Central

Grötzinger, Stefan W.; Alam, Intikhab; Ba Alawi, Wail; Bajic, Vladimir B.; Stingl, Ulrich; Eppinger, Jörg

2014-01-01

Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs) and poor homology of novel extremophile's genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the Integrated Data Warehouse of Microbial Genomes (INDIGO) data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes) may translate into false positives when searching for specific functions. The Profile and Pattern Matching (PPM) strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO)-terms (which represent enzyme function profiles) and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern). The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2577 enzyme commission (E.C.) numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from six different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter) and PROSITE IDs (pattern filter). Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits) are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns) are present. Scripts for annotation, as well as for the PPM algorithm, are available

Report on the 2011 Critical Assessment of Function Annotation (CAFA) meeting

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

Friedberg, Iddo

2015-01-21

The Critical Assessment of Function Annotation meeting was held July 14-15, 2011 at the Austria Conference Center in Vienna, Austria. There were 73 registered delegates at the meeting. We thank the DOE for this award. It helped us organize and support a scientific meeting AFP 2011 as a special interest group (SIG) meeting associated with the ISMB 2011 conference. The conference was held in Vienna, Austria, in July 2011. The AFP SIG was held on July 15-16, 2011 (immediately preceding the conference). The meeting consisted of two components, the first being a series of talks (invited and contributed) and discussionmore » sections dedicated to protein function research, with an emphasis on the theory and practice of computational methods utilized in functional annotation. The second component provided a large-scale assessment of computational methods through participation in the Critical Assessment of Functional Annotation (CAFA). The meeting was exciting and, based on feedback, quite successful. There were 73 registered participants. The schedule was only slightly different from the one proposed, due to two cancellations. Dr. Olga Troyanskaya has canceled and we invited Dr. David Jones instead. Similarly, instead of Dr. Richard Roberts, Dr. Simon Kasif gave a closing keynote. The remaining invited speakers were Janet Thornton (EBI) and Amos Bairoch (University of Geneva).« less

PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements.

PubMed

Mi, Huaiyu; Huang, Xiaosong; Muruganujan, Anushya; Tang, Haiming; Mills, Caitlin; Kang, Diane; Thomas, Paul D

2017-01-04

The PANTHER database (Protein ANalysis THrough Evolutionary Relationships, http://pantherdb.org) contains comprehensive information on the evolution and function of protein-coding genes from 104 completely sequenced genomes. PANTHER software tools allow users to classify new protein sequences, and to analyze gene lists obtained from large-scale genomics experiments. In the past year, major improvements include a large expansion of classification information available in PANTHER, as well as significant enhancements to the analysis tools. Protein subfamily functional classifications have more than doubled due to progress of the Gene Ontology Phylogenetic Annotation Project. For human genes (as well as a few other organisms), PANTHER now also supports enrichment analysis using pathway classifications from the Reactome resource. The gene list enrichment tools include a new 'hierarchical view' of results, enabling users to leverage the structure of the classifications/ontologies; the tools also allow users to upload genetic variant data directly, rather than requiring prior conversion to a gene list. The updated coding single-nucleotide polymorphisms (SNP) scoring tool uses an improved algorithm. The hidden Markov model (HMM) search tools now use HMMER3, dramatically reducing search times and improving accuracy of E-value statistics. Finally, the PANTHER Tree-Attribute Viewer has been implemented in JavaScript, with new views for exploring protein sequence evolution. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Annotation of Ehux ESTs

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

Kuo, Alan; Grigoriev, Igor

2009-06-12

22 percent ESTs do no align with scaffolds. EST Pipeleine assembles 17126 consensi from the noaligned ESTs. Annotation Pipeline predicts 8564 ORFS on the consensi. Domain analysis of ORFs reveals missing genes. Cluster analysis reveals missing genes. Expression analysis reveals potential strain specific genes.

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants.

PubMed

Pilkington, Sarah M; Crowhurst, Ross; Hilario, Elena; Nardozza, Simona; Fraser, Lena; Peng, Yongyan; Gunaseelan, Kularajathevan; Simpson, Robert; Tahir, Jibran; Deroles, Simon C; Templeton, Kerry; Luo, Zhiwei; Davy, Marcus; Cheng, Canhong; McNeilage, Mark; Scaglione, Davide; Liu, Yifei; Zhang, Qiong; Datson, Paul; De Silva, Nihal; Gardiner, Susan E; Bassett, Heather; Chagné, David; McCallum, John; Dzierzon, Helge; Deng, Cecilia; Wang, Yen-Yi; Barron, Lorna; Manako, Kelvina; Bowen, Judith; Foster, Toshi M; Erridge, Zoe A; Tiffin, Heather; Waite, Chethi N; Davies, Kevin M; Grierson, Ella P; Laing, William A; Kirk, Rebecca; Chen, Xiuyin; Wood, Marion; Montefiori, Mirco; Brummell, David A; Schwinn, Kathy E; Catanach, Andrew; Fullerton, Christina; Li, Dawei; Meiyalaghan, Sathiyamoorthy; Nieuwenhuizen, Niels; Read, Nicola; Prakash, Roneel; Hunter, Don; Zhang, Huaibi; McKenzie, Marian; Knäbel, Mareike; Harris, Alastair; Allan, Andrew C; Gleave, Andrew; Chen, Angela; Janssen, Bart J; Plunkett, Blue; Ampomah-Dwamena, Charles; Voogd, Charlotte; Leif, Davin; Lafferty, Declan; Souleyre, Edwige J F; Varkonyi-Gasic, Erika; Gambi, Francesco; Hanley, Jenny; Yao, Jia-Long; Cheung, Joey; David, Karine M; Warren, Ben; Marsh, Ken; Snowden, Kimberley C; Lin-Wang, Kui; Brian, Lara; Martinez-Sanchez, Marcela; Wang, Mindy; Ileperuma, Nadeesha; Macnee, Nikolai; Campin, Robert; McAtee, Peter; Drummond, Revel S M; Espley, Richard V; Ireland, Hilary S; Wu, Rongmei; Atkinson, Ross G; Karunairetnam, Sakuntala; Bulley, Sean; Chunkath, Shayhan; Hanley, Zac; Storey, Roy; Thrimawithana, Amali H; Thomson, Susan; David, Charles; Testolin, Raffaele; Huang, Hongwen; Hellens, Roger P; Schaffer, Robert J

2018-04-16

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164 Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models. A second genome of an A. chinensis (genotype Red5) was fully sequenced. This new sequence resulted in a 554.0 Mb assembly with all but 6 Mb assigned to pseudo-chromosomes. Pseudo-chromosomal comparisons showed a considerable number of translocation events have occurred following a whole genome duplication (WGD) event some consistent with centromeric Robertsonian-like translocations. RNA sequencing data from 12 tissues and ab initio analysis informed a genome-wide manual annotation, using the WebApollo tool. In total, 33,044 gene loci represented by 33,123 isoforms were identified, named and tagged for quality of evidential support. Of these 3114 (9.4%) were identical to a protein within 'Hongyang' The Kiwifruit Information Resource (KIR v2). Some proportion of the differences will be varietal polymorphisms. However, as most computationally predicted Red5 models required manual re-annotation this proportion is expected to be small. The quality of the new gene models was tested by fully sequencing 550 cloned 'Hort16A' cDNAs and comparing with the predicted protein models for Red5 and both the original 'Hongyang' assembly and the revised annotation from KIR v2. Only 48.9% and 63.5% of the cDNAs had a match with 90% identity or better to the original and revised 'Hongyang' annotation, respectively, compared with 90.9% to the Red5 models. Our study highlights the need to take a cautious approach to draft genomes and computationally predicted genes. Our use of the manual annotation tool WebApollo facilitated manual checking and

Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics.

PubMed

Sakai, Hiroaki; Lee, Sung Shin; Tanaka, Tsuyoshi; Numa, Hisataka; Kim, Jungsok; Kawahara, Yoshihiro; Wakimoto, Hironobu; Yang, Ching-chia; Iwamoto, Masao; Abe, Takashi; Yamada, Yuko; Muto, Akira; Inokuchi, Hachiro; Ikemura, Toshimichi; Matsumoto, Takashi; Sasaki, Takuji; Itoh, Takeshi

2013-02-01

The Rice Annotation Project Database (RAP-DB, http://rapdb.dna.affrc.go.jp/) has been providing a comprehensive set of gene annotations for the genome sequence of rice, Oryza sativa (japonica group) cv. Nipponbare. Since the first release in 2005, RAP-DB has been updated several times along with the genome assembly updates. Here, we present our newest RAP-DB based on the latest genome assembly, Os-Nipponbare-Reference-IRGSP-1.0 (IRGSP-1.0), which was released in 2011. We detected 37,869 loci by mapping transcript and protein sequences of 150 monocot species. To provide plant researchers with highly reliable and up to date rice gene annotations, we have been incorporating literature-based manually curated data, and 1,626 loci currently incorporate literature-based annotation data, including commonly used gene names or gene symbols. Transcriptional activities are shown at the nucleotide level by mapping RNA-Seq reads derived from 27 samples. We also mapped the Illumina reads of a Japanese leading japonica cultivar, Koshihikari, and a Chinese indica cultivar, Guangluai-4, to the genome and show alignments together with the single nucleotide polymorphisms (SNPs) and gene functional annotations through a newly developed browser, Short-Read Assembly Browser (S-RAB). We have developed two satellite databases, Plant Gene Family Database (PGFD) and Integrative Database of Cereal Gene Phylogeny (IDCGP), which display gene family and homologous gene relationships among diverse plant species. RAP-DB and the satellite databases offer simple and user-friendly web interfaces, enabling plant and genome researchers to access the data easily and facilitating a broad range of plant research topics.

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.

PubMed

Karchin, Rachel; Diekhans, Mark; Kelly, Libusha; Thomas, Daryl J; Pieper, Ursula; Eswar, Narayanan; Haussler, David; Sali, Andrej

2005-06-15

The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org http://salilab.org/LS-SNP/supp-info.pdf.

Automated update, revision, and quality control of the maize genome annotations using MAKER-P improves the B73 RefGen_v3 gene models and identifies new genes

USDA-ARS?s Scientific Manuscript database

The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-...

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin.

PubMed

Liu, Ching-Ti; Raghavan, Sridharan; Maruthur, Nisa; Kabagambe, Edmond Kato; Hong, Jaeyoung; Ng, Maggie C Y; Hivert, Marie-France; Lu, Yingchang; An, Ping; Bentley, Amy R; Drolet, Anne M; Gaulton, Kyle J; Guo, Xiuqing; Armstrong, Loren L; Irvin, Marguerite R; Li, Man; Lipovich, Leonard; Rybin, Denis V; Taylor, Kent D; Agyemang, Charles; Palmer, Nicholette D; Cade, Brian E; Chen, Wei-Min; Dauriz, Marco; Delaney, Joseph A C; Edwards, Todd L; Evans, Daniel S; Evans, Michele K; Lange, Leslie A; Leong, Aaron; Liu, Jingmin; Liu, Yongmei; Nayak, Uma; Patel, Sanjay R; Porneala, Bianca C; Rasmussen-Torvik, Laura J; Snijder, Marieke B; Stallings, Sarah C; Tanaka, Toshiko; Yanek, Lisa R; Zhao, Wei; Becker, Diane M; Bielak, Lawrence F; Biggs, Mary L; Bottinger, Erwin P; Bowden, Donald W; Chen, Guanjie; Correa, Adolfo; Couper, David J; Crawford, Dana C; Cushman, Mary; Eicher, John D; Fornage, Myriam; Franceschini, Nora; Fu, Yi-Ping; Goodarzi, Mark O; Gottesman, Omri; Hara, Kazuo; Harris, Tamara B; Jensen, Richard A; Johnson, Andrew D; Jhun, Min A; Karter, Andrew J; Keller, Margaux F; Kho, Abel N; Kizer, Jorge R; Krauss, Ronald M; Langefeld, Carl D; Li, Xiaohui; Liang, Jingling; Liu, Simin; Lowe, William L; Mosley, Thomas H; North, Kari E; Pacheco, Jennifer A; Peyser, Patricia A; Patrick, Alan L; Rice, Kenneth M; Selvin, Elizabeth; Sims, Mario; Smith, Jennifer A; Tajuddin, Salman M; Vaidya, Dhananjay; Wren, Mary P; Yao, Jie; Zhu, Xiaofeng; Ziegler, Julie T; Zmuda, Joseph M; Zonderman, Alan B; Zwinderman, Aeilko H; Adeyemo, Adebowale; Boerwinkle, Eric; Ferrucci, Luigi; Hayes, M Geoffrey; Kardia, Sharon L R; Miljkovic, Iva; Pankow, James S; Rotimi, Charles N; Sale, Michele M; Wagenknecht, Lynne E; Arnett, Donna K; Chen, Yii-Der Ida; Nalls, Michael A; Province, Michael A; Kao, W H Linda; Siscovick, David S; Psaty, Bruce M; Wilson, James G; Loos, Ruth J F; Dupuis, Josée; Rich, Stephen S; Florez, Jose C; Rotter, Jerome I; Morris, Andrew P; Meigs, James B

2016-07-07

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci. Copyright © 2016 American Society of Human Genetics. All rights reserved.

GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML.

PubMed

Vlaic, Sebastian; Hoffmann, Bianca; Kupfer, Peter; Weber, Michael; Dräger, Andreas

2013-09-01

GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage. GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490. General information on GRN2SBML, examples and tutorials are available at the tool's web page.

ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO.

PubMed

Ni, Ming; Ye, Fuqiang; Zhu, Juanjuan; Li, Zongwei; Yang, Shuai; Yang, Bite; Han, Lu; Wu, Yongge; Chen, Ying; Li, Fei; Wang, Shengqi; Bo, Xiaochen

2014-12-01

Numerous public microarray datasets are valuable resources for the scientific communities. Several online tools have made great steps to use these data by querying related datasets with users' own gene signatures or expression profiles. However, dataset annotation and result exhibition still need to be improved. ExpTreeDB is a database that allows for queries on human and mouse microarray experiments from Gene Expression Omnibus with gene signatures or profiles. Compared with similar applications, ExpTreeDB pays more attention to dataset annotations and result visualization. We introduced a multiple-level annotation system to depict and organize original experiments. For example, a tamoxifen-treated cell line experiment is hierarchically annotated as 'agent→drug→estrogen receptor antagonist→tamoxifen'. Consequently, retrieved results are exhibited by an interactive tree-structured graphics, which provide an overview for related experiments and might enlighten users on key items of interest. The database is freely available at http://biotech.bmi.ac.cn/ExpTreeDB. Web site is implemented in Perl, PHP, R, MySQL and Apache. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures

PubMed Central

2013-01-01

Background The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore these different GO similarity measure approaches and their biological applications. Results We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which incorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. Conclusions The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis. PMID:24067102

NegGOA: negative GO annotations selection using ontology structure.

PubMed

Fu, Guangyuan; Wang, Jun; Yang, Bo; Yu, Guoxian

2016-10-01

Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space. In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods. The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa gxyu@swu.edu.cn 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.

MicroScope-an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data.

PubMed

Médigue, Claudine; Calteau, Alexandra; Cruveiller, Stéphane; Gachet, Mathieu; Gautreau, Guillaume; Josso, Adrien; Lajus, Aurélie; Langlois, Jordan; Pereira, Hugo; Planel, Rémi; Roche, David; Rollin, Johan; Rouy, Zoe; Vallenet, David

2017-09-12

The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources. © The Author 2017. Published by Oxford University Press.

Systematic analysis of human kinase genes: a large number of genes and alternative splicing events result in functional and structural diversity

PubMed Central

Milanesi, Luciano; Petrillo, Mauro; Sepe, Leandra; Boccia, Angelo; D'Agostino, Nunzio; Passamano, Myriam; Di Nardo, Salvatore; Tasco, Gianluca; Casadio, Rita; Paolella, Giovanni

2005-01-01

Background Protein kinases are a well defined family of proteins, characterized by the presence of a common kinase catalytic domain and playing a significant role in many important cellular processes, such as proliferation, maintenance of cell shape, apoptosys. In many members of the family, additional non-kinase domains contribute further specialization, resulting in subcellular localization, protein binding and regulation of activity, among others. About 500 genes encode members of the kinase family in the human genome, and although many of them represent well known genes, a larger number of genes code for proteins of more recent identification, or for unknown proteins identified as kinase only after computational studies. Results A systematic in silico study performed on the human genome, led to the identification of 5 genes, on chromosome 1, 11, 13, 15 and 16 respectively, and 1 pseudogene on chromosome X; some of these genes are reported as kinases from NCBI but are absent in other databases, such as KinBase. Comparative analysis of 483 gene regions and subsequent computational analysis, aimed at identifying unannotated exons, indicates that a large number of kinase may code for alternately spliced forms or be incorrectly annotated. An InterProScan automated analysis was perfomed to study domain distribution and combination in the various families. At the same time, other structural features were also added to the annotation process, including the putative presence of transmembrane alpha helices, and the cystein propensity to participate into a disulfide bridge. Conclusion The predicted human kinome was extended by identifiying both additional genes and potential splice variants, resulting in a varied panorama where functionality may be searched at the gene and protein level. Structural analysis of kinase proteins domains as defined in multiple sources together with transmembrane alpha helices and signal peptide prediction provides hints to function assignment

Sma3s: a three-step modular annotator for large sequence datasets.

PubMed

Muñoz-Mérida, Antonio; Viguera, Enrique; Claros, M Gonzalo; Trelles, Oswaldo; Pérez-Pulido, Antonio J

2014-08-01

Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Transcriptator: An Automated Computational Pipeline to Annotate Assembled Reads and Identify Non Coding RNA.

PubMed

Tripathi, Kumar Parijat; Evangelista, Daniela; Zuccaro, Antonio; Guarracino, Mario Rosario

2015-01-01

RNA-seq is a new tool to measure RNA transcript counts, using high-throughput sequencing at an extraordinary accuracy. It provides quantitative means to explore the transcriptome of an organism of interest. However, interpreting this extremely large data into biological knowledge is a problem, and biologist-friendly tools are lacking. In our lab, we developed Transcriptator, a web application based on a computational Python pipeline with a user-friendly Java interface. This pipeline uses the web services available for BLAST (Basis Local Search Alignment Tool), QuickGO and DAVID (Database for Annotation, Visualization and Integrated Discovery) tools. It offers a report on statistical analysis of functional and Gene Ontology (GO) annotation's enrichment. It helps users to identify enriched biological themes, particularly GO terms, pathways, domains, gene/proteins features and protein-protein interactions related informations. It clusters the transcripts based on functional annotations and generates a tabular report for functional and gene ontology annotations for each submitted transcript to the web server. The implementation of QuickGo web-services in our pipeline enable the users to carry out GO-Slim analysis, whereas the integration of PORTRAIT (Prediction of transcriptomic non coding RNA (ncRNA) by ab initio methods) helps to identify the non coding RNAs and their regulatory role in transcriptome. In summary, Transcriptator is a useful software for both NGS and array data. It helps the users to characterize the de-novo assembled reads, obtained from NGS experiments for non-referenced organisms, while it also performs the functional enrichment analysis of differentially expressed transcripts/genes for both RNA-seq and micro-array experiments. It generates easy to read tables and interactive charts for better understanding of the data. The pipeline is modular in nature, and provides an opportunity to add new plugins in the future. Web application is freely

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

DOE PAGES

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

2016-02-24

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

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

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

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

Evolutionary interrogation of human biology in well-annotated genomic framework of rhesus macaque.

PubMed

Zhang, Shi-Jian; Liu, Chu-Jun; Yu, Peng; Zhong, Xiaoming; Chen, Jia-Yu; Yang, Xinzhuang; Peng, Jiguang; Yan, Shouyu; Wang, Chenqu; Zhu, Xiaotong; Xiong, Jingwei; Zhang, Yong E; Tan, Bertrand Chin-Ming; Li, Chuan-Yun

2014-05-01

With genome sequence and composition highly analogous to human, rhesus macaque represents a unique reference for evolutionary studies of human biology. Here, we developed a comprehensive genomic framework of rhesus macaque, the RhesusBase2, for evolutionary interrogation of human genes and the associated regulations. A total of 1,667 next-generation sequencing (NGS) data sets were processed, integrated, and evaluated, generating 51.2 million new functional annotation records. With extensive NGS annotations, RhesusBase2 refined the fine-scale structures in 30% of the macaque Ensembl transcripts, reporting an accurate, up-to-date set of macaque gene models. On the basis of these annotations and accurate macaque gene models, we further developed an NGS-oriented Molecular Evolution Gateway to access and visualize macaque annotations in reference to human orthologous genes and associated regulations (www.rhesusbase.org/molEvo). We highlighted the application of this well-annotated genomic framework in generating hypothetical link of human-biased regulations to human-specific traits, by using mechanistic characterization of the DIEXF gene as an example that provides novel clues to the understanding of digestive system reduction in human evolution. On a global scale, we also identified a catalog of 9,295 human-biased regulatory events, which may represent novel elements that have a substantial impact on shaping human transcriptome and possibly underpin recent human phenotypic evolution. Taken together, we provide an NGS data-driven, information-rich framework that will broadly benefit genomics research in general and serves as an important resource for in-depth evolutionary studies of human biology.

WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

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

Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian

With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don’t exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomicmore » data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.« less

WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

DOE PAGES

Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian; ...

2017-03-06

With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don’t exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomicmore » data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.« less

Towards an informative mutant phenotype for every bacterial gene

DOE PAGES

Deutschbauer, Adam; Price, Morgan N.; Wetmore, Kelly M.; ...

2014-08-11

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, inmore » Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.« less

Reveal genes functionally associated with ACADS by a network study.

PubMed

Chen, Yulong; Su, Zhiguang

2015-09-15

Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.

Genome re-annotation of the wild strawberry Fragaria vesca using extensive Illumina- and SMRT-based RNA-seq datasets

PubMed Central

Li, Yongping; Wei, Wei; Feng, Jia; Luo, Huifeng; Pi, Mengting; Liu, Zhongchi; Kang, Chunying

2018-01-01

Abstract The genome of the wild diploid strawberry species Fragaria vesca, an ideal model system of cultivated strawberry (Fragaria × ananassa, octoploid) and other Rosaceae family crops, was first published in 2011 and followed by a new assembly (Fvb). However, the annotation for Fvb mainly relied on ab initio predictions and included only predicted coding sequences, therefore an improved annotation is highly desirable. Here, a new annotation version named v2.0.a2 was created for the Fvb genome by a pipeline utilizing one PacBio library, 90 Illumina RNA-seq libraries, and 9 small RNA-seq libraries. Altogether, 18,641 genes (55.6% out of 33,538 genes) were augmented with information on the 5′ and/or 3′ UTRs, 13,168 (39.3%) protein-coding genes were modified or newly identified, and 7,370 genes were found to possess alternative isoforms. In addition, 1,938 long non-coding RNAs, 171 miRNAs, and 51,714 small RNA clusters were integrated into the annotation. This new annotation of F. vesca is substantially improved in both accuracy and integrity of gene predictions, beneficial to the gene functional studies in strawberry and to the comparative genomic analysis of other horticultural crops in Rosaceae family. PMID:29036429

On the detection of functionally coherent groups of protein domains with an extension to protein annotation

PubMed Central

McLaughlin, William A; Chen, Ken; Hou, Tingjun; Wang, Wei

2007-01-01

Background Protein domains coordinate to perform multifaceted cellular functions, and domain combinations serve as the functional building blocks of the cell. The available methods to identify functional domain combinations are limited in their scope, e.g. to the identification of combinations falling within individual proteins or within specific regions in a translated genome. Further effort is needed to identify groups of domains that span across two or more proteins and are linked by a cooperative function. Such functional domain combinations can be useful for protein annotation. Results Using a new computational method, we have identified 114 groups of domains, referred to as domain assembly units (DASSEM units), in the proteome of budding yeast Saccharomyces cerevisiae. The units participate in many important cellular processes such as transcription regulation, translation initiation, and mRNA splicing. Within the units the domains were found to function in a cooperative manner; and each domain contributed to a different aspect of the unit's overall function. The member domains of DASSEM units were found to be significantly enriched among proteins contained in transcription modules, defined as genes sharing similar expression profiles and presumably similar functions. The observation further confirmed the functional coherence of DASSEM units. The functional linkages of units were found in both functionally characterized and uncharacterized proteins, which enabled the assessment of protein function based on domain composition. Conclusion A new computational method was developed to identify groups of domains that are linked by a common function in the proteome of Saccharomyces cerevisiae. These groups can either lie within individual proteins or span across different proteins. We propose that the functional linkages among the domains within the DASSEM units can be used as a non-homology based tool to annotate uncharacterized proteins. PMID:17937820

Metagenomic insights into the effect of oxytetracycline on microbial structures, functions and functional genes in sediment denitrification.

PubMed

Zou, Yong; Lin, Manxia; Xiong, Wenguang; Wang, Mei; Zhang, Jiaxuan; Wang, Mianzhi; Sun, Yongxue

2018-06-02

Denitrification is an indispensable pathway of nitrogen removal in aquatic ecosystems, and plays an important role in decreasing eutrophication induced by excessive reactive nitrogen pollution. Aquatic environments also suffer from antibiotic pollution due to runoff from farms and sewage systems. The aim of this study was to investigate the effect of oxytetracycline stress on denitrifying functional genes, the microbial community and metabolic pathways in sediments using high-throughput sequencing and metagenomic analysis. The oxytetracycline was observed to significantly inhibit the abundance of nirK and nosZ genes (P < 0.001). KEGG pathway annotation indicated that oxytetracycline treatment decreased the abundance of nitrate reductase, nitrite reductase and N 2 O reductase. Functional annotations revealed that oxytetracycline exposure decreased the abundance of the protein metabolism subsystem in the bacterial community. Metagenomic sequencing demonstrated that the abundance of Proteobacteria and Firmicutes increased with oxytetracycline exposure while the Actinobacteria decreased. In sediments, Pseudomonas and Bradyrhizobium were major contributors to denitrification and oxytetracycline exposure resulted in a decreased abundance of Bradyrhizobium. These results indicated that oxytetracycline residues influences the denitrifier community and may heighten occurrence of reactive nitrogen in aquatic ecosystems. Copyright © 2018. Published by Elsevier Inc.

Bovine Genome Database: supporting community annotation and analysis of the Bos taurus genome

PubMed Central

2010-01-01

Background A goal of the Bovine Genome Database (BGD; http://BovineGenome.org) has been to support the Bovine Genome Sequencing and Analysis Consortium (BGSAC) in the annotation and analysis of the bovine genome. We were faced with several challenges, including the need to maintain consistent quality despite diversity in annotation expertise in the research community, the need to maintain consistent data formats, and the need to minimize the potential duplication of annotation effort. With new sequencing technologies allowing many more eukaryotic genomes to be sequenced, the demand for collaborative annotation is likely to increase. Here we present our approach, challenges and solutions facilitating a large distributed annotation project. Results and Discussion BGD has provided annotation tools that supported 147 members of the BGSAC in contributing 3,871 gene models over a fifteen-week period, and these annotations have been integrated into the bovine Official Gene Set. Our approach has been to provide an annotation system, which includes a BLAST site, multiple genome browsers, an annotation portal, and the Apollo Annotation Editor configured to connect directly to our Chado database. In addition to implementing and integrating components of the annotation system, we have performed computational analyses to create gene evidence tracks and a consensus gene set, which can be viewed on individual gene pages at BGD. Conclusions We have provided annotation tools that alleviate challenges associated with distributed annotation. Our system provides a consistent set of data to all annotators and eliminates the need for annotators to format data. Involving the bovine research community in genome annotation has allowed us to leverage expertise in various areas of bovine biology to provide biological insight into the genome sequence. PMID:21092105

Columba: an integrated database of proteins, structures, and annotations.

PubMed

Trissl, Silke; Rother, Kristian; Müller, Heiko; Steinke, Thomas; Koch, Ina; Preissner, Robert; Frömmel, Cornelius; Leser, Ulf

2005-03-31

Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures. COLUMBA currently integrates twelve different databases, including PDB, KEGG, Swiss-Prot, CATH, SCOP, the Gene Ontology, and ENZYME. The database can be searched using either keyword search or data source-specific web forms. Users can thus quickly select and download PDB entries that, for instance, participate in a particular pathway, are classified as containing a certain CATH architecture, are annotated as having a certain molecular function in the Gene Ontology, and whose structures have a resolution under a defined threshold. The results of queries are provided in both machine-readable extensible markup language and human-readable format. The structures themselves can be viewed interactively on the web. The COLUMBA database facilitates the creation of protein structure data sets for many structure-based studies. It allows to combine queries on a number of structure-related databases not covered by other projects at present. Thus, information on both many and few protein structures can be used efficiently. The web interface for COLUMBA is available at http://www.columba-db.de.

Sequencing, annotation and comparative analysis of nine BACs of giant panda (Ailuropoda melanoleuca).

PubMed

Zheng, Yang; Cai, Jing; Li, JianWen; Li, Bo; Lin, Runmao; Tian, Feng; Wang, XiaoLing; Wang, Jun

2010-01-01

A 10-fold BAC library for giant panda was constructed and nine BACs were selected to generate finish sequences. These BACs could be used as a validation resource for the de novo assembly accuracy of the whole genome shotgun sequencing reads of giant panda newly generated by the Illumina GA sequencing technology. Complete sanger sequencing, assembly, annotation and comparative analysis were carried out on the selected BACs of a joint length 878 kb. Homologue search and de novo prediction methods were used to annotate genes and repeats. Twelve protein coding genes were predicted, seven of which could be functionally annotated. The seven genes have an average gene size of about 41 kb, an average coding size of about 1.2 kb and an average exon number of 6 per gene. Besides, seven tRNA genes were found. About 27 percent of the BAC sequence is composed of repeats. A phylogenetic tree was constructed using neighbor-join algorithm across five species, including giant panda, human, dog, cat and mouse, which reconfirms dog as the most related species to giant panda. Our results provide detailed sequence and structure information for new genes and repeats of giant panda, which will be helpful for further studies on the giant panda.

EST Express: PHP/MySQL based automated annotation of ESTs from expression libraries.

PubMed

Smith, Robin P; Buchser, William J; Lemmon, Marcus B; Pardinas, Jose R; Bixby, John L; Lemmon, Vance P

2008-04-10

Several biological techniques result in the acquisition of functional sets of cDNAs that must be sequenced and analyzed. The emergence of redundant databases such as UniGene and centralized annotation engines such as Entrez Gene has allowed the development of software that can analyze a great number of sequences in a matter of seconds. We have developed "EST Express", a suite of analytical tools that identify and annotate ESTs originating from specific mRNA populations. The software consists of a user-friendly GUI powered by PHP and MySQL that allows for online collaboration between researchers and continuity with UniGene, Entrez Gene and RefSeq. Two key features of the software include a novel, simplified Entrez Gene parser and tools to manage cDNA library sequencing projects. We have tested the software on a large data set (2,016 samples) produced by subtractive hybridization. EST Express is an open-source, cross-platform web server application that imports sequences from cDNA libraries, such as those generated through subtractive hybridization or yeast two-hybrid screens. It then provides several layers of annotation based on Entrez Gene and RefSeq to allow the user to highlight useful genes and manage cDNA library projects.

EST Express: PHP/MySQL based automated annotation of ESTs from expression libraries

PubMed Central

Smith, Robin P; Buchser, William J; Lemmon, Marcus B; Pardinas, Jose R; Bixby, John L; Lemmon, Vance P

2008-01-01

Background Several biological techniques result in the acquisition of functional sets of cDNAs that must be sequenced and analyzed. The emergence of redundant databases such as UniGene and centralized annotation engines such as Entrez Gene has allowed the development of software that can analyze a great number of sequences in a matter of seconds. Results We have developed "EST Express", a suite of analytical tools that identify and annotate ESTs originating from specific mRNA populations. The software consists of a user-friendly GUI powered by PHP and MySQL that allows for online collaboration between researchers and continuity with UniGene, Entrez Gene and RefSeq. Two key features of the software include a novel, simplified Entrez Gene parser and tools to manage cDNA library sequencing projects. We have tested the software on a large data set (2,016 samples) produced by subtractive hybridization. Conclusion EST Express is an open-source, cross-platform web server application that imports sequences from cDNA libraries, such as those generated through subtractive hybridization or yeast two-hybrid screens. It then provides several layers of annotation based on Entrez Gene and RefSeq to allow the user to highlight useful genes and manage cDNA library projects. PMID:18402700

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations

PubMed Central

Peng, Cheng; Shiu, Shin-Han

2016-01-01

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets. PMID:27935950

Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data.

PubMed

He, Zihuai; Xu, Bin; Lee, Seunggeun; Ionita-Laza, Iuliana

2017-09-07

Substantial progress has been made in the functional annotation of genetic variation in the human genome. Integrative analysis that incorporates such functional annotations into sequencing studies can aid the discovery of disease-associated genetic variants, especially those with unknown function and located outside protein-coding regions. Direct incorporation of one functional annotation as weight in existing dispersion and burden tests can suffer substantial loss of power when the functional annotation is not predictive of the risk status of a variant. Here, we have developed unified tests that can utilize multiple functional annotations simultaneously for integrative association analysis with efficient computational techniques. We show that the proposed tests significantly improve power when variant risk status can be predicted by functional annotations. Importantly, when functional annotations are not predictive of risk status, the proposed tests incur only minimal loss of power in relation to existing dispersion and burden tests, and under certain circumstances they can even have improved power by learning a weight that better approximates the underlying disease model in a data-adaptive manner. The tests can be constructed with summary statistics of existing dispersion and burden tests for sequencing data, therefore allowing meta-analysis of multiple studies without sharing individual-level data. We applied the proposed tests to a meta-analysis of noncoding rare variants in Metabochip data on 12,281 individuals from eight studies for lipid traits. By incorporating the Eigen functional score, we detected significant associations between noncoding rare variants in SLC22A3 and low-density lipoprotein and total cholesterol, associations that are missed by standard dispersion and burden tests. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Sequencing and functional annotation of the whole genome of the filamentous fungus Aspergillus westerdijkiae.

PubMed

Han, Xiaolong; Chakrabortti, Alolika; Zhu, Jindong; Liang, Zhao-Xun; Li, Jinming

2016-08-15

Aspergillus westerdijkiae produces ochratoxin A (OTA) in Aspergillus section Circumdati. It is responsible for the contamination of agricultural crops, fruits, and food commodities, as its secondary metabolite OTA poses a potential threat to animals and humans. As a member of the filamentous fungi family, its capacity for enzymatic catalysis and secondary metabolite production is valuable in industrial production and medicine. To understand the genetic factors underlying its pathogenicity, enzymatic degradation, and secondary metabolism, we analysed the whole genome of A. westerdijkiae and compared it with eight other sequenced Aspergillus species. We sequenced the complete genome of A. westerdijkiae and assembled approximately 36 Mb of its genomic DNA, in which we identified 10,861 putative protein-coding genes. We constructed a phylogenetic tree of A. westerdijkiae and eight other sequenced Aspergillus species and found that the sister group of A. westerdijkiae was the A. oryzae - A. flavus clade. By searching the associated databases, we identified 716 cytochrome P450 enzymes, 633 carbohydrate-active enzymes, and 377 proteases. By combining comparative analysis with Kyoto Encyclopaedia of Genes and Genomes (KEGG), Conserved Domains Database (CDD), and Pfam annotations, we predicted 228 potential carbohydrate-active enzymes related to plant polysaccharide degradation (PPD). We found a large number of secondary biosynthetic gene clusters, which suggested that A. westerdijkiae had a remarkable capacity to produce secondary metabolites. Furthermore, we obtained two more reliable and integrated gene sequences containing the reported portions of OTA biosynthesis and identified their respective secondary metabolite clusters. We also systematically annotated these two hybrid t1pks-nrps gene clusters involved in OTA biosynthesis. These two clusters were separate in the genome, and one of them encoded a couple of GH3 and AA3 enzyme genes involved in sucrose and glucose

Discovery and Characterization of Chromatin States for Systematic Annotation of the Human Genome

NASA Astrophysics Data System (ADS)

Ernst, Jason; Kellis, Manolis

A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal chromatin states in human T cells, based on recurrent and spatially coherent combinations of chromatin marks.We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, largescale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.

Gene Expression Profiling Reveals Functional Specialization along the Intestinal Tract of a Carnivorous Teleostean Fish (Dicentrarchus labrax)

PubMed Central

Calduch-Giner, Josep A.; Sitjà-Bobadilla, Ariadna; Pérez-Sánchez, Jaume

2016-01-01

High-quality sequencing reads from the intestine of European sea bass were assembled, annotated by similarity against protein reference databases and combined with nucleotide sequences from public and private databases. After redundancy filtering, 24,906 non-redundant annotated sequences encoding 15,367 different gene descriptions were obtained. These annotated sequences were used to design a custom, high-density oligo-microarray (8 × 15 K) for the transcriptomic profiling of anterior (AI), middle (MI), and posterior (PI) intestinal segments. Similar molecular signatures were found for AI and MI segments, which were combined in a single group (AI-MI) whereas the PI outstood separately, with more than 1900 differentially expressed genes with a fold-change cutoff of 2. Functional analysis revealed that molecular and cellular functions related to feed digestion and nutrient absorption and transport were over-represented in AI-MI segments. By contrast, the initiation and establishment of immune defense mechanisms became especially relevant in PI, although the microarray expression profiling validated by qPCR indicated that these functional changes are gradual from anterior to posterior intestinal segments. This functional divergence occurred in association with spatial transcriptional changes in nutrient transporters and the mucosal chemosensing system via G protein-coupled receptors. These findings contribute to identify key indicators of gut functions and to compare different fish feeding strategies and immune defense mechanisms acquired along the evolution of teleosts. PMID:27610085

Gene Expression Profiling Reveals Functional Specialization along the Intestinal Tract of a Carnivorous Teleostean Fish (Dicentrarchus labrax).

PubMed

Calduch-Giner, Josep A; Sitjà-Bobadilla, Ariadna; Pérez-Sánchez, Jaume

2016-01-01

High-quality sequencing reads from the intestine of European sea bass were assembled, annotated by similarity against protein reference databases and combined with nucleotide sequences from public and private databases. After redundancy filtering, 24,906 non-redundant annotated sequences encoding 15,367 different gene descriptions were obtained. These annotated sequences were used to design a custom, high-density oligo-microarray (8 × 15 K) for the transcriptomic profiling of anterior (AI), middle (MI), and posterior (PI) intestinal segments. Similar molecular signatures were found for AI and MI segments, which were combined in a single group (AI-MI) whereas the PI outstood separately, with more than 1900 differentially expressed genes with a fold-change cutoff of 2. Functional analysis revealed that molecular and cellular functions related to feed digestion and nutrient absorption and transport were over-represented in AI-MI segments. By contrast, the initiation and establishment of immune defense mechanisms became especially relevant in PI, although the microarray expression profiling validated by qPCR indicated that these functional changes are gradual from anterior to posterior intestinal segments. This functional divergence occurred in association with spatial transcriptional changes in nutrient transporters and the mucosal chemosensing system via G protein-coupled receptors. These findings contribute to identify key indicators of gut functions and to compare different fish feeding strategies and immune defense mechanisms acquired along the evolution of teleosts.

categoryCompare, an analytical tool based on feature annotations

PubMed Central

Flight, Robert M.; Harrison, Benjamin J.; Mohammad, Fahim; Bunge, Mary B.; Moon, Lawrence D. F.; Petruska, Jeffrey C.; Rouchka, Eric C.

2014-01-01

Assessment of high-throughput—omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered “biomarkers.” The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, categoryCompare, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohn's disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohn's vs. UC comparison showed gross similarities in inflammatory

EGASP: the human ENCODE Genome Annotation Assessment Project

PubMed Central

Guigó, Roderic; Flicek, Paul; Abril, Josep F; Reymond, Alexandre; Lagarde, Julien; Denoeud, France; Antonarakis, Stylianos; Ashburner, Michael; Bajic, Vladimir B; Birney, Ewan; Castelo, Robert; Eyras, Eduardo; Ucla, Catherine; Gingeras, Thomas R; Harrow, Jennifer; Hubbard, Tim; Lewis, Suzanna E; Reese, Martin G

2006-01-01

Background We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. Results The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified. Conclusion This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence. PMID:16925836

Artemis and ACT: viewing, annotating and comparing sequences stored in a relational database.

PubMed

Carver, Tim; Berriman, Matthew; Tivey, Adrian; Patel, Chinmay; Böhme, Ulrike; Barrell, Barclay G; Parkhill, Julian; Rajandream, Marie-Adèle

2008-12-01

Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences. Artemis and ACT have now been extended to read and write directly to the Generic Model Organism Database (GMOD, http://www.gmod.org) Chado relational database schema. In addition, a Gene Builder tool has been developed to provide structured forms and tables to edit coordinates of gene models and edit functional annotation, based on standard ontologies, controlled vocabularies and free text. Artemis and ACT are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/ http://www.sanger.ac.uk/Software/ACT/

The Protein Information Resource: an integrated public resource of functional annotation of proteins

PubMed Central

Wu, Cathy H.; Huang, Hongzhan; Arminski, Leslie; Castro-Alvear, Jorge; Chen, Yongxing; Hu, Zhang-Zhi; Ledley, Robert S.; Lewis, Kali C.; Mewes, Hans-Werner; Orcutt, Bruce C.; Suzek, Baris E.; Tsugita, Akira; Vinayaka, C. R.; Yeh, Lai-Su L.; Zhang, Jian; Barker, Winona C.

2002-01-01

The Protein Information Resource (PIR) serves as an integrated public resource of functional annotation of protein data to support genomic/proteomic research and scientific discovery. The PIR, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the PIR-International Protein Sequence Database (PSD), the major annotated protein sequence database in the public domain, containing about 250 000 proteins. To improve protein annotation and the coverage of experimentally validated data, a bibliography submission system is developed for scientists to submit, categorize and retrieve literature information. Comprehensive protein information is available from iProClass, which includes family classification at the superfamily, domain and motif levels, structural and functional features of proteins, as well as cross-references to over 40 biological databases. To provide timely and comprehensive protein data with source attribution, we have introduced a non-redundant reference protein database, PIR-NREF. The database consists of about 800 000 proteins collected from PIR-PSD, SWISS-PROT, TrEMBL, GenPept, RefSeq and PDB, with composite protein names and literature data. To promote database interoperability, we provide XML data distribution and open database schema, and adopt common ontologies. The PIR web site (http://pir.georgetown.edu/) features data mining and sequence analysis tools for information retrieval and functional identification of proteins based on both sequence and annotation information. The PIR databases and other files are also available by FTP (ftp://nbrfa.georgetown.edu/pir_databases). PMID:11752247

Concept annotation in the CRAFT corpus.

PubMed

Bada, Michael; Eckert, Miriam; Evans, Donald; Garcia, Kristin; Shipley, Krista; Sitnikov, Dmitry; Baumgartner, William A; Cohen, K Bretonnel; Verspoor, Karin; Blake, Judith A; Hunter, Lawrence E

2012-07-09

Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.

Next Generation Models for Storage and Representation of Microbial Biological Annotation

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

Quest, Daniel J; Land, Miriam L; Brettin, Thomas S

2010-01-01

Background Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation. Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software systemmore » to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable. The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way. Results Here, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files. Conclusions The power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is

nGASP - the nematode genome annotation assessment project

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

Coghlan, A; Fiedler, T J; McKay, S J

2008-12-19

While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets for 10 Mb of the C. elegans genome. Predictions were compared to reference gene sets consisting of confirmed or manually curated gene models from WormBase. The most accurate gene-finders were 'combiner'more » algorithms, which made use of transcript- and protein-alignments and multi-genome alignments, as well as gene predictions from other gene-finders. Gene-finders that used alignments of ESTs, mRNAs and proteins came in second place. There was a tie for third place between gene-finders that used multi-genome alignments and ab initio gene-finders. The median gene level sensitivity of combiners was 78% and their specificity was 42%, which is nearly the same accuracy as reported for combiners in the human genome. C. elegans genes with exons of unusual hexamer content, as well as those with many exons, short exons, long introns, a weak translation start signal, weak splice sites, or poorly conserved orthologs were the most challenging for gene-finders. While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets for 10 Mb of the C. elegans

nGASP--the nematode genome annotation assessment project.

PubMed

Coghlan, Avril; Fiedler, Tristan J; McKay, Sheldon J; Flicek, Paul; Harris, Todd W; Blasiar, Darin; Stein, Lincoln D

2008-12-19

While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets across 10 Mb of the C. elegans genome. Predictions were compared to reference gene sets consisting of confirmed or manually curated gene models from WormBase. The most accurate gene-finders were 'combiner' algorithms, which made use of transcript- and protein-alignments and multi-genome alignments, as well as gene predictions from other gene-finders. Gene-finders that used alignments of ESTs, mRNAs and proteins came in second. There was a tie for third place between gene-finders that used multi-genome alignments and ab initio gene-finders. The median gene level sensitivity of combiners was 78% and their specificity was 42%, which is nearly the same accuracy reported for combiners in the human genome. C. elegans genes with exons of unusual hexamer content, as well as those with unusually many exons, short exons, long introns, a weak translation start signal, weak splice sites, or poorly conserved orthologs posed the greatest difficulty for gene-finders. This experiment establishes a baseline of gene prediction accuracy in Caenorhabditis genomes, and has guided the choice of gene-finders for the annotation of newly sequenced genomes of Caenorhabditis and other nematode species. We have created new gene sets for C. briggsae, C. remanei, C. brenneri, C. japonica, and Brugia malayi using some of the best-performing gene-finders.

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna; ...

2016-04-26

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

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

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system.

PubMed

Chen, I-Min A; Markowitz, Victor M; Palaniappan, Krishna; Szeto, Ernest; Chu, Ken; Huang, Jinghua; Ratner, Anna; Pillay, Manoj; Hadjithomas, Michalis; Huntemann, Marcel; Mikhailova, Natalia; Ovchinnikova, Galina; Ivanova, Natalia N; Kyrpides, Nikos C

2016-04-26

The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existing IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.

The Aspergillus Genome Database: multispecies curation and incorporation of RNA-Seq data to improve structural gene annotations.

PubMed

Cerqueira, Gustavo C; Arnaud, Martha B; Inglis, Diane O; Skrzypek, Marek S; Binkley, Gail; Simison, Matt; Miyasato, Stuart R; Binkley, Jonathan; Orvis, Joshua; Shah, Prachi; Wymore, Farrell; Sherlock, Gavin; Wortman, Jennifer R

2014-01-01

The Aspergillus Genome Database (AspGD; http://www.aspgd.org) is a freely available web-based resource that was designed for Aspergillus researchers and is also a valuable source of information for the entire fungal research community. In addition to being a repository and central point of access to genome, transcriptome and polymorphism data, AspGD hosts a comprehensive comparative genomics toolbox that facilitates the exploration of precomputed orthologs among the 20 currently available Aspergillus genomes. AspGD curators perform gene product annotation based on review of the literature for four key Aspergillus species: Aspergillus nidulans, Aspergillus oryzae, Aspergillus fumigatus and Aspergillus niger. We have iteratively improved the structural annotation of Aspergillus genomes through the analysis of publicly available transcription data, mostly expressed sequenced tags, as described in a previous NAR Database article (Arnaud et al. 2012). In this update, we report substantive structural annotation improvements for A. nidulans, A. oryzae and A. fumigatus genomes based on recently available RNA-Seq data. Over 26 000 loci were updated across these species; although those primarily comprise the addition and extension of untranslated regions (UTRs), the new analysis also enabled over 1000 modifications affecting the coding sequence of genes in each target genome.

Annotation and comparative analysis of the glycoside hydrolase genes in Brachypodium distachyon

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

Tyler, Ludmila; Bragg, Jennifer; Wu, Jiajie

2010-01-01

Background Glycoside hydrolases cleave the bond between a carbohydrate and another carbohydrate, a protein, lipid or other moiety. Genes encoding glycoside hydrolases are found in a wide range of organisms, from archea to animals, and are relatively abundant in plant genomes. In plants, these enzymes are involved in diverse processes, including starch metabolism, defense, and cell-wall remodeling. Glycoside hydrolase genes have been previously cataloged for Oryza sativa (rice), the model dicotyledonous plant Arabidopsis thaliana, and the fast-growing tree Populus trichocarpa (poplar). To improve our understanding of glycoside hydrolases in plants generally and in grasses specifically, we annotated the glycoside hydrolasemore » genes in the grasses Brachypodium distachyon (an emerging monocotyledonous model) and Sorghum bicolor (sorghum). We then compared the glycoside hydrolases across species, both at the whole-genome level and at the level of individual glycoside hydrolase families. Results We identified 356 glycoside hydrolase genes in Brachypodium and 404 in sorghum. The corresponding proteins fell into the same 34 families that are represented in rice, Arabidopsis, and poplar, helping to define a glycoside hydrolase family profile which may be common to flowering plants. Examination of individual glycoside hydrolase familes (GH5, GH13, GH18, GH19, GH28, and GH51) revealed both similarities and distinctions between monocots and dicots, as well as between species. Shared evolutionary histories appear to be modified by lineage-specific expansions or deletions. Within families, the Brachypodium and sorghum proteins generally cluster with those from other monocots. Conclusions This work provides the foundation for further comparative and functional analyses of plant glycoside hydrolases. Defining the Brachypodium glycoside hydrolases sets the stage for Brachypodium to be a monocot model for investigations of these enzymes and their diverse roles in planta

Transcriptome sequencing and annotation for the Jamaican fruit bat (Artibeus jamaicensis).

PubMed

Shaw, Timothy I; Srivastava, Anuj; Chou, Wen-Chi; Liu, Liang; Hawkinson, Ann; Glenn, Travis C; Adams, Rick; Schountz, Tony

2012-01-01

The Jamaican fruit bat (Artibeus jamaicensis) is one of the most common bats in the tropical Americas. It is thought to be a potential reservoir host of Tacaribe virus, an arenavirus closely related to the South American hemorrhagic fever viruses. We performed transcriptome sequencing and annotation from lung, kidney and spleen tissues using 454 and Illumina platforms to develop this species as an animal model. More than 100,000 contigs were assembled, with 25,000 genes that were functionally annotated. Of the remaining unannotated contigs, 80% were found within bat genomes or transcriptomes. Annotated genes are involved in a broad range of activities ranging from cellular metabolism to genome regulation through ncRNAs. Reciprocal BLAST best hits yielded 8,785 sequences that are orthologous to mouse, rat, cattle, horse and human. Species tree analysis of sequences from 2,378 loci was used to achieve 95% bootstrap support for the placement of bat as sister to the clade containing horse, dog, and cattle. Through substitution rate estimation between bat and human, 32 genes were identified with evidence for positive selection. We also identified 466 immune-related genes, which may be useful for studying Tacaribe virus infection of this species. The Jamaican fruit bat transcriptome dataset is a resource that should provide additional candidate markers for studying bat evolution and ecology, and tools for analysis of the host response and pathology of disease.

Concept annotation in the CRAFT corpus

PubMed Central

2012-01-01

Background Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. Results This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. Conclusions As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http

FARO server: Meta-analysis of gene expression by matching gene expression signatures to a compendium of public gene expression data.

PubMed

Manijak, Mieszko P; Nielsen, Henrik B

2011-06-11

Although, systematic analysis of gene annotation is a powerful tool for interpreting gene expression data, it sometimes is blurred by incomplete gene annotation, missing expression response of key genes and secondary gene expression responses. These shortcomings may be partially circumvented by instead matching gene expression signatures to signatures of other experiments. To facilitate this we present the Functional Association Response by Overlap (FARO) server, that match input signatures to a compendium of 242 gene expression signatures, extracted from more than 1700 Arabidopsis microarray experiments. Hereby we present a publicly available tool for robust characterization of Arabidopsis gene expression experiments which can point to similar experimental factors in other experiments. The server is available at http://www.cbs.dtu.dk/services/faro/.

De Novo Assembly and Annotation of the Transcriptome of the Agricultural Weed Ipomoea purpurea Uncovers Gene Expression Changes Associated with Herbicide Resistance

PubMed Central

Leslie, Trent; Baucom, Regina S.

2014-01-01

Human-mediated selection can lead to rapid evolution in very short time scales, and the evolution of herbicide resistance in agricultural weeds is an excellent example of this phenomenon. The common morning glory, Ipomoea purpurea, is resistant to the herbicide glyphosate, but genetic investigations of this trait have been hampered by the lack of genomic resources for this species. Here, we present the annotated transcriptome of the common morning glory, Ipomoea purpurea, along with an examination of whole genome expression profiling to assess potential gene expression differences between three artificially selected herbicide resistant lines and three susceptible lines. The assembled Ipomoea transcriptome reported in this work contains 65,459 assembled transcripts, ~28,000 of which were functionally annotated by assignment to Gene Ontology categories. Our RNA-seq survey using this reference transcriptome identified 19 differentially expressed genes associated with resistance—one of which, a cytochrome P450, belongs to a large plant family of genes involved in xenobiotic detoxification. The differentially expressed genes also broadly implicated receptor-like kinases, which were down-regulated in the resistant lines, and other growth and defense genes, which were up-regulated in resistant lines. Interestingly, the target of glyphosate—EPSP synthase—was not overexpressed in the resistant Ipomoea lines as in other glyphosate resistant weeds. Overall, this work identifies potential candidate resistance loci for future investigations and dramatically increases genomic resources for this species. The assembled transcriptome presented herein will also provide a valuable resource to the Ipomoea community, as well as to those interested in utilizing the close relationship between the Convolvulaceae and the Solanaceae for phylogenetic and comparative genomics examinations. PMID:25155274

De novo assembly and annotation of the transcriptome of the agricultural weed Ipomoea purpurea uncovers gene expression changes associated with herbicide resistance.

PubMed

Leslie, Trent; Baucom, Regina S

2014-08-25

Human-mediated selection can lead to rapid evolution in very short time scales, and the evolution of herbicide resistance in agricultural weeds is an excellent example of this phenomenon. The common morning glory, Ipomoea purpurea, is resistant to the herbicide glyphosate, but genetic investigations of this trait have been hampered by the lack of genomic resources for this species. Here, we present the annotated transcriptome of the common morning glory, Ipomoea purpurea, along with an examination of whole genome expression profiling to assess potential gene expression differences between three artificially selected herbicide resistant lines and three susceptible lines. The assembled Ipomoea transcriptome reported in this work contains 65,459 assembled transcripts, ~28,000 of which were functionally annotated by assignment to Gene Ontology categories. Our RNA-seq survey using this reference transcriptome identified 19 differentially expressed genes associated with resistance-one of which, a cytochrome P450, belongs to a large plant family of genes involved in xenobiotic detoxification. The differentially expressed genes also broadly implicated receptor-like kinases, which were down-regulated in the resistant lines, and other growth and defense genes, which were up-regulated in resistant lines. Interestingly, the target of glyphosate-EPSP synthase-was not overexpressed in the resistant Ipomoea lines as in other glyphosate resistant weeds. Overall, this work identifies potential candidate resistance loci for future investigations and dramatically increases genomic resources for this species. The assembled transcriptome presented herein will also provide a valuable resource to the Ipomoea community, as well as to those interested in utilizing the close relationship between the Convolvulaceae and the Solanaceae for phylogenetic and comparative genomics examinations. Copyright © 2014 Leslie and Baucom.

Functionally Enigmatic Genes: A Case Study of the Brain Ignorome

PubMed Central

Pandey, Ashutosh K.; Lu, Lu; Wang, Xusheng; Homayouni, Ramin; Williams, Robert W.

2014-01-01

What proportion of genes with intense and selective expression in specific tissues, cells, or systems are still almost completely uncharacterized with respect to biological function? In what ways do these functionally enigmatic genes differ from well-studied genes? To address these two questions, we devised a computational approach that defines so-called ignoromes. As proof of principle, we extracted and analyzed a large subset of genes with intense and selective expression in brain. We find that publications associated with this set are highly skewed—the top 5% of genes absorb 70% of the relevant literature. In contrast, approximately 20% of genes have essentially no neuroscience literature. Analysis of the ignorome over the past decade demonstrates that it is stubbornly persistent, and the rapid expansion of the neuroscience literature has not had the expected effect on numbers of these genes. Surprisingly, ignorome genes do not differ from well-studied genes in terms of connectivity in coexpression networks. Nor do they differ with respect to numbers of orthologs, paralogs, or protein domains. The major distinguishing characteristic between these sets of genes is date of discovery, early discovery being associated with greater research momentum—a genomic bandwagon effect. Finally we ask to what extent massive genomic, imaging, and phenotype data sets can be used to provide high-throughput functional annotation for an entire ignorome. In a majority of cases we have been able to extract and add significant information for these neglected genes. In several cases—ELMOD1, TMEM88B, and DZANK1—we have exploited sequence polymorphisms, large phenome data sets, and reverse genetic methods to evaluate the function of ignorome genes. PMID:24523945

Functionally enigmatic genes: a case study of the brain ignorome.

PubMed

Pandey, Ashutosh K; Lu, Lu; Wang, Xusheng; Homayouni, Ramin; Williams, Robert W

2014-01-01

What proportion of genes with intense and selective expression in specific tissues, cells, or systems are still almost completely uncharacterized with respect to biological function? In what ways do these functionally enigmatic genes differ from well-studied genes? To address these two questions, we devised a computational approach that defines so-called ignoromes. As proof of principle, we extracted and analyzed a large subset of genes with intense and selective expression in brain. We find that publications associated with this set are highly skewed--the top 5% of genes absorb 70% of the relevant literature. In contrast, approximately 20% of genes have essentially no neuroscience literature. Analysis of the ignorome over the past decade demonstrates that it is stubbornly persistent, and the rapid expansion of the neuroscience literature has not had the expected effect on numbers of these genes. Surprisingly, ignorome genes do not differ from well-studied genes in terms of connectivity in coexpression networks. Nor do they differ with respect to numbers of orthologs, paralogs, or protein domains. The major distinguishing characteristic between these sets of genes is date of discovery, early discovery being associated with greater research momentum--a genomic bandwagon effect. Finally we ask to what extent massive genomic, imaging, and phenotype data sets can be used to provide high-throughput functional annotation for an entire ignorome. In a majority of cases we have been able to extract and add significant information for these neglected genes. In several cases--ELMOD1, TMEM88B, and DZANK1--we have exploited sequence polymorphisms, large phenome data sets, and reverse genetic methods to evaluate the function of ignorome genes.

Warehousing re-annotated cancer genes for biomarker meta-analysis.

PubMed

Orsini, M; Travaglione, A; Capobianco, E

2013-07-01

Translational research in cancer genomics assigns a fundamental role to bioinformatics in support of candidate gene prioritization with regard to both biomarker discovery and target identification for drug development. Efforts in both such directions rely on the existence and constant update of large repositories of gene expression data and omics records obtained from a variety of experiments. Users who interactively interrogate such repositories may have problems in retrieving sample fields that present limited associated information, due for instance to incomplete entries or sometimes unusable files. Cancer-specific data sources present similar problems. Given that source integration usually improves data quality, one of the objectives is keeping the computational complexity sufficiently low to allow an optimal assimilation and mining of all the information. In particular, the scope of integrating intraomics data can be to improve the exploration of gene co-expression landscapes, while the scope of integrating interomics sources can be that of establishing genotype-phenotype associations. Both integrations are relevant to cancer biomarker meta-analysis, as the proposed study demonstrates. Our approach is based on re-annotating cancer-specific data available at the EBI's ArrayExpress repository and building a data warehouse aimed to biomarker discovery and validation studies. Cancer genes are organized by tissue with biomedical and clinical evidences combined to increase reproducibility and consistency of results. For better comparative evaluation, multiple queries have been designed to efficiently address all types of experiments and platforms, and allow for retrieval of sample-related information, such as cell line, disease state and clinical aspects. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

FIGENIX: Intelligent automation of genomic annotation: expertise integration in a new software platform

PubMed Central

Gouret, Philippe; Vitiello, Vérane; Balandraud, Nathalie; Gilles, André; Pontarotti, Pierre; Danchin, Etienne GJ

2005-01-01

Background Two of the main objectives of the genomic and post-genomic era are to structurally and functionally annotate genomes which consists of detecting genes' position and structure, and inferring their function (as well as of other features of genomes). Structural and functional annotation both require the complex chaining of numerous different software, algorithms and methods under the supervision of a biologist. The automation of these pipelines is necessary to manage huge amounts of data released by sequencing projects. Several pipelines already automate some of these complex chaining but still necessitate an important contribution of biologists for supervising and controlling the results at various steps. Results Here we propose an innovative automated platform, FIGENIX, which includes an expert system capable to substitute to human expertise at several key steps. FIGENIX currently automates complex pipelines of structural and functional annotation under the supervision of the expert system (which allows for example to make key decisions, check intermediate results or refine the dataset). The quality of the results produced by FIGENIX is comparable to those obtained by expert biologists with a drastic gain in terms of time costs and avoidance of errors due to the human manipulation of data. Conclusion The core engine and expert system of the FIGENIX platform currently handle complex annotation processes of broad interest for the genomic community. They could be easily adapted to new, or more specialized pipelines, such as for example the annotation of miRNAs, the classification of complex multigenic families, annotation of regulatory elements and other genomic features of interest. PMID:16083500

A Uniform System For The Annotation Of Human microRNA Genes And The Evolution Of The Human microRNAome

PubMed Central

Fromm, Bastian; Billipp, Tyler; Peck, Liam E.; Johansen, Morten; Tarver, James E.; King, Benjamin L.; Newcomb, James M.; Sempere, Lorenzo F.; Flatmark, Kjersti; Hovig, Eivind; Peterson, Kevin J.

2016-01-01

Although microRNAs (miRNAs) are among the most intensively studied molecules of the past 20 years, determining what is and what is not a miRNA has not been straightforward. Here, we present a uniform system for the annotation and nomenclature of miRNA genes. We show that fewer than a third of the 1,881 human miRBase entries, and only approximately 16% of the 7,095 metazoan miRBase entries, are robustly supported as miRNA genes. Furthermore, we show that the human repertoire of miRNAs has been shaped by periods of intense miRNA innovation, and that mature gene products show a very different tempo and mode of sequence evolution than star products. We establish a new open access database -- MirGeneDB (http://mirgenedb.org) -- to catalog this set of robustly supported miRNAs, which complements the efforts of miRBase, but differs from it by annotating the mature versus star products, and by imposing an evolutionary hierarchy upon this curated and consistently named repertoire. PMID:26473382

GENCODE: the reference human genome annotation for The ENCODE Project.

PubMed

Harrow, Jennifer; Frankish, Adam; Gonzalez, Jose M; Tapanari, Electra; Diekhans, Mark; Kokocinski, Felix; Aken, Bronwen L; Barrell, Daniel; Zadissa, Amonida; Searle, Stephen; Barnes, If; Bignell, Alexandra; Boychenko, Veronika; Hunt, Toby; Kay, Mike; Mukherjee, Gaurab; Rajan, Jeena; Despacio-Reyes, Gloria; Saunders, Gary; Steward, Charles; Harte, Rachel; Lin, Michael; Howald, Cédric; Tanzer, Andrea; Derrien, Thomas; Chrast, Jacqueline; Walters, Nathalie; Balasubramanian, Suganthi; Pei, Baikang; Tress, Michael; Rodriguez, Jose Manuel; Ezkurdia, Iakes; van Baren, Jeltje; Brent, Michael; Haussler, David; Kellis, Manolis; Valencia, Alfonso; Reymond, Alexandre; Gerstein, Mark; Guigó, Roderic; Hubbard, Tim J

2012-09-01

The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.

Functional Study of Genes Essential for Autogamy and Nuclear Reorganization in Paramecium▿§

PubMed Central

Nowak, Jacek K.; Gromadka, Robert; Juszczuk, Marek; Jerka-Dziadosz, Maria; Maliszewska, Kamila; Mucchielli, Marie-Hélène; Gout, Jean-François; Arnaiz, Olivier; Agier, Nicolas; Tang, Thomas; Aggerbeck, Lawrence P.; Cohen, Jean; Delacroix, Hervé; Sperling, Linda; Herbert, Christopher J.; Zagulski, Marek; Bétermier, Mireille

2011-01-01

Like all ciliates, Paramecium tetraurelia is a unicellular eukaryote that harbors two kinds of nuclei within its cytoplasm. At each sexual cycle, a new somatic macronucleus (MAC) develops from the germ line micronucleus (MIC) through a sequence of complex events, which includes meiosis, karyogamy, and assembly of the MAC genome from MIC sequences. The latter process involves developmentally programmed genome rearrangements controlled by noncoding RNAs and a specialized RNA interference machinery. We describe our first attempts to identify genes and biological processes that contribute to the progression of the sexual cycle. Given the high percentage of unknown genes annotated in the P. tetraurelia genome, we applied a global strategy to monitor gene expression profiles during autogamy, a self-fertilization process. We focused this pilot study on the genes carried by the largest somatic chromosome and designed dedicated DNA arrays covering 484 genes from this chromosome (1.2% of all genes annotated in the genome). Transcriptome analysis revealed four major patterns of gene expression, including two successive waves of gene induction. Functional analysis of 15 upregulated genes revealed four that are essential for vegetative growth, one of which is involved in the maintenance of MAC integrity and another in cell division or membrane trafficking. Two additional genes, encoding a MIC-specific protein and a putative RNA helicase localizing to the old and then to the new MAC, are specifically required during sexual processes. Our work provides a proof of principle that genes essential for meiosis and nuclear reorganization can be uncovered following genome-wide transcriptome analysis. PMID:21257794

RRE: a tool for the extraction of non-coding regions surrounding annotated genes from genomic datasets.

PubMed

Lazzarato, F; Franceschinis, G; Botta, M; Cordero, F; Calogero, R A

2004-11-01

RRE allows the extraction of non-coding regions surrounding a coding sequence [i.e. gene upstream region, 5'-untranslated region (5'-UTR), introns, 3'-UTR, downstream region] from annotated genomic datasets available at NCBI. RRE parser and web-based interface are accessible at http://www.bioinformatica.unito.it/bioinformatics/rre/rre.html

AnnotateGenomicRegions: a web application.

PubMed

Zammataro, Luca; DeMolfetta, Rita; Bucci, Gabriele; Ceol, Arnaud; Muller, Heiko

2014-01-01

Modern genomic technologies produce large amounts of data that can be mapped to specific regions in the genome. Among the first steps in interpreting the results is annotation of genomic regions with known features such as genes, promoters, CpG islands etc. Several tools have been published to perform this task. However, using these tools often requires a significant amount of bioinformatics skills and/or downloading and installing dedicated software. Here we present AnnotateGenomicRegions, a web application that accepts genomic regions as input and outputs a selection of overlapping and/or neighboring genome annotations. Supported organisms include human (hg18, hg19), mouse (mm8, mm9, mm10), zebrafish (danRer7), and Saccharomyces cerevisiae (sacCer2, sacCer3). AnnotateGenomicRegions is accessible online on a public server or can be installed locally. Some frequently used annotations and genomes are embedded in the application while custom annotations may be added by the user. The increasing spread of genomic technologies generates the need for a simple-to-use annotation tool for genomic regions that can be used by biologists and bioinformaticians alike. AnnotateGenomicRegions meets this demand. AnnotateGenomicRegions is an open-source web application that can be installed on any personal computer or institute server. AnnotateGenomicRegions is available at: http://cru.genomics.iit.it/AnnotateGenomicRegions.

A comprehensive collection of annotations to interpret sequence variation in human mitochondrial transfer RNAs.

PubMed

Diroma, Maria Angela; Lubisco, Paolo; Attimonelli, Marcella

2016-11-08

The abundance of biological data characterizing the genomics era is contributing to a comprehensive understanding of human mitochondrial genetics. Nevertheless, many aspects are still unclear, specifically about the variability of the 22 human mitochondrial transfer RNA (tRNA) genes and their involvement in diseases. The complex enrichment and isolation of tRNAs in vitro leads to an incomplete knowledge of their post-transcriptional modifications and three-dimensional folding, essential for correct tRNA functioning. An accurate annotation of mitochondrial tRNA variants would be definitely useful and appreciated by mitochondrial researchers and clinicians since the most of bioinformatics tools for variant annotation and prioritization available so far cannot shed light on the functional role of tRNA variations. To this aim, we updated our MToolBox pipeline for mitochondrial DNA analysis of high throughput and Sanger sequencing data by integrating tRNA variant annotations in order to identify and characterize relevant variants not only in protein coding regions, but also in tRNA genes. The annotation step in the pipeline now provides detailed information for variants mapping onto the 22 mitochondrial tRNAs. For each mt-tRNA position along the entire genome, the relative tRNA numbering, tRNA type, cloverleaf secondary domains (loops and stems), mature nucleotide and interactions in the three-dimensional folding were reported. Moreover, pathogenicity predictions for tRNA and rRNA variants were retrieved from the literature and integrated within the annotations provided by MToolBox, both in the stand-alone version and web-based tool at the Mitochondrial Disease Sequence Data Resource (MSeqDR) website. All the information available in the annotation step of MToolBox were exploited to generate custom tracks which can be displayed in the GBrowse instance at MSeqDR website. To the best of our knowledge, specific data regarding mitochondrial variants in tRNA genes were

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

PubMed Central

2010-01-01

Background Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome. Results We have developed ChIPpeakAnno as a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with ChIPpeakAnno can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. In addition, we have implemented functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, the package includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes. Conclusions ChIPpeakAnno enables batch annotation of the binding sites identified from ChIP-seq, ChIP-chip, CAGE or any technology that results in a large number of enriched genomic regions within the statistical programming environment R. Allowing users to pass their own annotation data such

Chado controller: advanced annotation management with a community annotation system.

PubMed

Guignon, Valentin; Droc, Gaëtan; Alaux, Michael; Baurens, Franc-Christophe; Garsmeur, Olivier; Poiron, Claire; Carver, Tim; Rouard, Mathieu; Bocs, Stéphanie

2012-04-01

We developed a controller that is compliant with the Chado database schema, GBrowse and genome annotation-editing tools such as Artemis and Apollo. It enables the management of public and private data, monitors manual annotation (with controlled vocabularies, structural and functional annotation controls) and stores versions of annotation for all modified features. The Chado controller uses PostgreSQL and Perl. The Chado Controller package is available for download at http://www.gnpannot.org/content/chado-controller and runs on any Unix-like operating system, and documentation is available at http://www.gnpannot.org/content/chado-controller-doc The system can be tested using the GNPAnnot Sandbox at http://www.gnpannot.org/content/gnpannot-sandbox-form valentin.guignon@cirad.fr; stephanie.sidibe-bocs@cirad.fr Supplementary data are available at Bioinformatics online.

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.

DynGO: a tool for visualizing and mining of Gene Ontology and its associations

PubMed Central

Liu, Hongfang; Hu, Zhang-Zhi; Wu, Cathy H

2005-01-01

Background A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed. Results We present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. Conclusion We have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are