Ensembl comparative genomics resources.

PubMed

Herrero, Javier; Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J; Searle, Stephen M J; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul

2016-01-01

Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.ensembl.org. © The Author(s) 2016. Published by Oxford University Press.

Ensembl comparative genomics resources

PubMed Central

Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J.; Searle, Stephen M. J.; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul

2016-01-01

Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.ensembl.org. PMID:26896847

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing

PubMed Central

Nguyen, Nga Thi Thuy; Vincens, Pierre

2018-01-01

Abstract Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). PMID:29087490

A Secure Alignment Algorithm for Mapping Short Reads to Human Genome.

PubMed

Zhao, Yongan; Wang, Xiaofeng; Tang, Haixu

2018-05-09

The elastic and inexpensive computing resources such as clouds have been recognized as a useful solution to analyzing massive human genomic data (e.g., acquired by using next-generation sequencers) in biomedical researches. However, outsourcing human genome computation to public or commercial clouds was hindered due to privacy concerns: even a small number of human genome sequences contain sufficient information for identifying the donor of the genomic data. This issue cannot be directly addressed by existing security and cryptographic techniques (such as homomorphic encryption), because they are too heavyweight to carry out practical genome computation tasks on massive data. In this article, we present a secure algorithm to accomplish the read mapping, one of the most basic tasks in human genomic data analysis based on a hybrid cloud computing model. Comparing with the existing approaches, our algorithm delegates most computation to the public cloud, while only performing encryption and decryption on the private cloud, and thus makes the maximum use of the computing resource of the public cloud. Furthermore, our algorithm reports similar results as the nonsecure read mapping algorithms, including the alignment between reads and the reference genome, which can be directly used in the downstream analysis such as the inference of genomic variations. We implemented the algorithm in C++ and Python on a hybrid cloud system, in which the public cloud uses an Apache Spark system.

Proteinortho: Detection of (Co-)orthologs in large-scale analysis

PubMed Central

2011-01-01

Background Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. Results The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. Conclusions Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware. PMID:21526987

Protecting genomic data analytics in the cloud: state of the art and opportunities.

PubMed

Tang, Haixu; Jiang, Xiaoqian; Wang, Xiaofeng; Wang, Shuang; Sofia, Heidi; Fox, Dov; Lauter, Kristin; Malin, Bradley; Telenti, Amalio; Xiong, Li; Ohno-Machado, Lucila

2016-10-13

The outsourcing of genomic data into public cloud computing settings raises concerns over privacy and security. Significant advancements in secure computation methods have emerged over the past several years, but such techniques need to be rigorously evaluated for their ability to support the analysis of human genomic data in an efficient and cost-effective manner. With respect to public cloud environments, there are concerns about the inadvertent exposure of human genomic data to unauthorized users. In analyses involving multiple institutions, there is additional concern about data being used beyond agreed research scope and being prcoessed in untrused computational environments, which may not satisfy institutional policies. To systematically investigate these issues, the NIH-funded National Center for Biomedical Computing iDASH (integrating Data for Analysis, 'anonymization' and SHaring) hosted the second Critical Assessment of Data Privacy and Protection competition to assess the capacity of cryptographic technologies for protecting computation over human genomes in the cloud and promoting cross-institutional collaboration. Data scientists were challenged to design and engineer practical algorithms for secure outsourcing of genome computation tasks in working software, whereby analyses are performed only on encrypted data. They were also challenged to develop approaches to enable secure collaboration on data from genomic studies generated by multiple organizations (e.g., medical centers) to jointly compute aggregate statistics without sharing individual-level records. The results of the competition indicated that secure computation techniques can enable comparative analysis of human genomes, but greater efficiency (in terms of compute time and memory utilization) are needed before they are sufficiently practical for real world environments.

DroSpeGe: rapid access database for new Drosophila species genomes.

PubMed

Gilbert, Donald G

2007-01-01

The Drosophila species comparative genome database DroSpeGe (http://insects.eugenes.org/DroSpeGe/) provides genome researchers with rapid, usable access to 12 new and old Drosophila genomes, since its inception in 2004. Scientists can use, with minimal computing expertise, the wealth of new genome information for developing new insights into insect evolution. New genome assemblies provided by several sequencing centers have been annotated with known model organism gene homologies and gene predictions to provided basic comparative data. TeraGrid supplies the shared cyberinfrastructure for the primary computations. This genome database includes homologies to Drosophila melanogaster and eight other eukaryote model genomes, and gene predictions from several groups. BLAST searches of the newest assemblies are integrated with genome maps. GBrowse maps provide detailed views of cross-species aligned genomes. BioMart provides for data mining of annotations and sequences. Common chromosome maps identify major synteny among species. Potential gain and loss of genes is suggested by Gene Ontology groupings for genes of the new species. Summaries of essential genome statistics include sizes, genes found and predicted, homology among genomes, phylogenetic trees of species and comparisons of several gene predictions for sensitivity and specificity in finding new and known genes.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing.

PubMed

Nguyen, Nga Thi Thuy; Vincens, Pierre; Roest Crollius, Hugues; Louis, Alexandra

2018-01-04

Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Comparative analysis of gene regulatory networks: from network reconstruction to evolution.

PubMed

Thompson, Dawn; Regev, Aviv; Roy, Sushmita

2015-01-01

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

Neptune: a bioinformatics tool for rapid discovery of genomic variation in bacterial populations

PubMed Central

Marinier, Eric; Zaheer, Rahat; Berry, Chrystal; Weedmark, Kelly A.; Domaratzki, Michael; Mabon, Philip; Knox, Natalie C.; Reimer, Aleisha R.; Graham, Morag R.; Chui, Linda; Patterson-Fortin, Laura; Zhang, Jian; Pagotto, Franco; Farber, Jeff; Mahony, Jim; Seyer, Karine; Bekal, Sadjia; Tremblay, Cécile; Isaac-Renton, Judy; Prystajecky, Natalie; Chen, Jessica; Slade, Peter

2017-01-01

Abstract The ready availability of vast amounts of genomic sequence data has created the need to rethink comparative genomics algorithms using ‘big data’ approaches. Neptune is an efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptune’s loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses. Tests on simulated and real datasets showed that Neptune rapidly identifies regions that are both sensitive and specific. We demonstrate that this system can identify trait-specific loci from different bacterial lineages. Neptune is broadly applicable for comparative bacterial analyses, yet will particularly benefit pathogenomic applications, owing to efficient and sensitive discovery of differentially abundant genomic loci. The software is available for download at: http://github.com/phac-nml/neptune. PMID:29048594

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

gmos: Rapid Detection of Genome Mosaicism over Short Evolutionary Distances.

PubMed

Domazet-Lošo, Mirjana; Domazet-Lošo, Tomislav

2016-01-01

Prokaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos).

gmos: Rapid Detection of Genome Mosaicism over Short Evolutionary Distances

PubMed Central

Domazet-Lošo, Mirjana; Domazet-Lošo, Tomislav

2016-01-01

Prokaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos). PMID:27846272

USE OF COMPETITIVE DNA HYBRIDIZATION TO IDENTIFY DIFFERENCES IN THE GENOMES OF TWO CLOSELY RELATED FECAL INDICATOR BACTERIA

EPA Science Inventory

Although recent technological advances in DNA sequencing and computational biology now allow scientists to compare entire microbial genomes, comparisons of closely related bacterial species and individual isolates by whole-genome sequencing approaches remains prohibitively expens...

Aligning the unalignable: bacteriophage whole genome alignments.

PubMed

Bérard, Sèverine; Chateau, Annie; Pompidor, Nicolas; Guertin, Paul; Bergeron, Anne; Swenson, Krister M

2016-01-13

In recent years, many studies focused on the description and comparison of large sets of related bacteriophage genomes. Due to the peculiar mosaic structure of these genomes, few informative approaches for comparing whole genomes exist: dot plots diagrams give a mostly qualitative assessment of the similarity/dissimilarity between two or more genomes, and clustering techniques are used to classify genomes. Multiple alignments are conspicuously absent from this scene. Indeed, whole genome aligners interpret lack of similarity between sequences as an indication of rearrangements, insertions, or losses. This behavior makes them ill-prepared to align bacteriophage genomes, where even closely related strains can accomplish the same biological function with highly dissimilar sequences. In this paper, we propose a multiple alignment strategy that exploits functional collinearity shared by related strains of bacteriophages, and uses partial orders to capture mosaicism of sets of genomes. As classical alignments do, the computed alignments can be used to predict that genes have the same biological function, even in the absence of detectable similarity. The Alpha aligner implements these ideas in visual interactive displays, and is used to compute several examples of alignments of Staphylococcus aureus and Mycobacterium bacteriophages, involving up to 29 genomes. Using these datasets, we prove that Alpha alignments are at least as good as those computed by standard aligners. Comparison with the progressive Mauve aligner - which implements a partial order strategy, but whose alignments are linearized - shows a greatly improved interactive graphic display, while avoiding misalignments. Multiple alignments of whole bacteriophage genomes work, and will become an important conceptual and visual tool in comparative genomics of sets of related strains. A python implementation of Alpha, along with installation instructions for Ubuntu and OSX, is available on bitbucket (https://bitbucket.org/thekswenson/alpha).

Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

PubMed

Muley, Vijaykumar Yogesh; Ranjan, Akash

2012-01-01

Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable accuracy of predictions when computational resources are limited.

CoCoNUT: an efficient system for the comparison and analysis of genomes

PubMed Central

2008-01-01

Background Comparative genomics is the analysis and comparison of genomes from different species. This area of research is driven by the large number of sequenced genomes and heavily relies on efficient algorithms and software to perform pairwise and multiple genome comparisons. Results Most of the software tools available are tailored for one specific task. In contrast, we have developed a novel system CoCoNUT (Computational Comparative geNomics Utility Toolkit) that allows solving several different tasks in a unified framework: (1) finding regions of high similarity among multiple genomic sequences and aligning them, (2) comparing two draft or multi-chromosomal genomes, (3) locating large segmental duplications in large genomic sequences, and (4) mapping cDNA/EST to genomic sequences. Conclusion CoCoNUT is competitive with other software tools w.r.t. the quality of the results. The use of state of the art algorithms and data structures allows CoCoNUT to solve comparative genomics tasks more efficiently than previous tools. With the improved user interface (including an interactive visualization component), CoCoNUT provides a unified, versatile, and easy-to-use software tool for large scale studies in comparative genomics. PMID:19014477

Assessing the Robustness of Complete Bacterial Genome Segmentations

NASA Astrophysics Data System (ADS)

Devillers, Hugo; Chiapello, Hélène; Schbath, Sophie; El Karoui, Meriem

Comparison of closely related bacterial genomes has revealed the presence of highly conserved sequences forming a "backbone" that is interrupted by numerous, less conserved, DNA fragments. Segmentation of bacterial genomes into backbone and variable regions is particularly useful to investigate bacterial genome evolution. Several software tools have been designed to compare complete bacterial chromosomes and a few online databases store pre-computed genome comparisons. However, very few statistical methods are available to evaluate the reliability of these software tools and to compare the results obtained with them. To fill this gap, we have developed two local scores to measure the robustness of bacterial genome segmentations. Our method uses a simulation procedure based on random perturbations of the compared genomes. The scores presented in this paper are simple to implement and our results show that they allow to discriminate easily between robust and non-robust bacterial genome segmentations when using aligners such as MAUVE and MGA.

Determining protein function and interaction from genome analysis

DOEpatents

Eisenberg, David; Marcotte, Edward M.; Thompson, Michael J.; Pellegrini, Matteo; Yeates, Todd O.

2004-08-03

A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

On Computing Breakpoint Distances for Genomes with Duplicate Genes.

PubMed

Shao, Mingfu; Moret, Bernard M E

2017-06-01

A fundamental problem in comparative genomics is to compute the distance between two genomes in terms of its higher level organization (given by genes or syntenic blocks). For two genomes without duplicate genes, we can easily define (and almost always efficiently compute) a variety of distance measures, but the problem is NP-hard under most models when genomes contain duplicate genes. To tackle duplicate genes, three formulations (exemplar, maximum matching, and any matching) have been proposed, all of which aim to build a matching between homologous genes so as to minimize some distance measure. Of the many distance measures, the breakpoint distance (the number of nonconserved adjacencies) was the first one to be studied and remains of significant interest because of its simplicity and model-free property. The three breakpoint distance problems corresponding to the three formulations have been widely studied. Although we provided last year a solution for the exemplar problem that runs very fast on full genomes, computing optimal solutions for the other two problems has remained challenging. In this article, we describe very fast, exact algorithms for these two problems. Our algorithms rely on a compact integer-linear program that we further simplify by developing an algorithm to remove variables, based on new results on the structure of adjacencies and matchings. Through extensive experiments using both simulations and biological data sets, we show that our algorithms run very fast (in seconds) on mammalian genomes and scale well beyond. We also apply these algorithms (as well as the classic orthology tool MSOAR) to create orthology assignment, then compare their quality in terms of both accuracy and coverage. We find that our algorithm for the "any matching" formulation significantly outperforms other methods in terms of accuracy while achieving nearly maximum coverage.

CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

PubMed

Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

2016-12-23

A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

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 correction of gene models enabling improvement of computational prediction. This utility was especially relevant for certain types of gene families such as the EXPANSIN like genes. Finally, this high quality gene set will supply the kiwifruit and general plant community with a new tool for genomics and other comparative analysis.

Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

PubMed

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

2011-01-01

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

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

PubMed Central

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

2011-01-01

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

Rosetta stone method for detecting protein function and protein-protein interactions from genome sequences

DOEpatents

Eisenberg, David; Marcotte, Edward M.; Pellegrini, Matteo; Thompson, Michael J.; Yeates, Todd O.

2002-10-15

A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

Genomecmp: computer software to detect genomic rearrangements using markers

NASA Astrophysics Data System (ADS)

Kulawik, Maciej; Nowak, Robert M.

2017-08-01

Detection of genomics rearrangements is a tough task, because of the size of data to be processed. As genome sequences may consist of hundreds of millions symbols, it is not only practically impossible to compare them by hand, but it is also complex problem for computer software. The way to significantly accelerate the process is to use rearrangement detection algorithm based on unique short sequences called markers. The algorithm described in this paper develops markers using base genome and find the markers positions on other genome. The algorithm has been extended by support for ambiguity symbols. Web application with graphical user interface has been created using three-layer architecture, where users could run the task simultaneously. The accuracy and efficiency of proposed solution has been studied using generated and real data.

GenomeFingerprinter: the genome fingerprint and the universal genome fingerprint analysis for systematic comparative genomics.

PubMed

Ai, Yuncan; Ai, Hannan; Meng, Fanmei; Zhao, Lei

2013-01-01

No attention has been paid on comparing a set of genome sequences crossing genetic components and biological categories with far divergence over large size range. We define it as the systematic comparative genomics and aim to develop the methodology. First, we create a method, GenomeFingerprinter, to unambiguously produce a set of three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections, to illustrate the genome fingerprint of a given genome sequence. Second, we develop a set of concepts and tools, and thereby establish a method called the universal genome fingerprint analysis (UGFA). Particularly, we define the total genetic component configuration (TGCC) (including chromosome, plasmid, and phage) for describing a strain as a systematic unit, the universal genome fingerprint map (UGFM) of TGCC for differentiating strains as a universal system, and the systematic comparative genomics (SCG) for comparing a set of genomes crossing genetic components and biological categories. Third, we construct a method of quantitative analysis to compare two genomes by using the outcome dataset of genome fingerprint analysis. Specifically, we define the geometric center and its geometric mean for a given genome fingerprint map, followed by the Euclidean distance, the differentiate rate, and the weighted differentiate rate to quantitatively describe the difference between two genomes of comparison. Moreover, we demonstrate the applications through case studies on various genome sequences, giving tremendous insights into the critical issues in microbial genomics and taxonomy. We have created a method, GenomeFingerprinter, for rapidly computing, geometrically visualizing, intuitively comparing a set of genomes at genome fingerprint level, and hence established a method called the universal genome fingerprint analysis, as well as developed a method of quantitative analysis of the outcome dataset. These have set up the methodology of systematic comparative genomics based on the genome fingerprint analysis.

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives.

PubMed

Zhao, Min; Wang, Qingguo; Wang, Quan; Jia, Peilin; Zhao, Zhongming

2013-01-01

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

PubMed Central

2013-01-01

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development. PMID:24564169

PanWeb: A web interface for pan-genomic analysis.

PubMed

Pantoja, Yan; Pinheiro, Kenny; Veras, Allan; Araújo, Fabrício; Lopes de Sousa, Ailton; Guimarães, Luis Carlos; Silva, Artur; Ramos, Rommel T J

2017-01-01

With increased production of genomic data since the advent of next-generation sequencing (NGS), there has been a need to develop new bioinformatics tools and areas, such as comparative genomics. In comparative genomics, the genetic material of an organism is directly compared to that of another organism to better understand biological species. Moreover, the exponentially growing number of deposited prokaryote genomes has enabled the investigation of several genomic characteristics that are intrinsic to certain species. Thus, a new approach to comparative genomics, termed pan-genomics, was developed. In pan-genomics, various organisms of the same species or genus are compared. Currently, there are many tools that can perform pan-genomic analyses, such as PGAP (Pan-Genome Analysis Pipeline), Panseq (Pan-Genome Sequence Analysis Program) and PGAT (Prokaryotic Genome Analysis Tool). Among these software tools, PGAP was developed in the Perl scripting language and its reliance on UNIX platform terminals and its requirement for an extensive parameterized command line can become a problem for users without previous computational knowledge. Thus, the aim of this study was to develop a web application, known as PanWeb, that serves as a graphical interface for PGAP. In addition, using the output files of the PGAP pipeline, the application generates graphics using custom-developed scripts in the R programming language. PanWeb is freely available at http://www.computationalbiology.ufpa.br/panweb.

Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics

PubMed Central

Louis, Alexandra; Nguyen, Nga Thi Thuy; Muffato, Matthieu; Roest Crollius, Hugues

2015-01-01

The Genomicus web server (http://www.genomicus.biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and AlignView) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved non-coding elements with their putative gene targets. PMID:25378326

COMPETITIVE METAGENOMIC DNA HYBRIDIZATION IDENTIFIES HOST-SPECIFIC GENETIC MARKERS IN HUMAN FECAL MICROBIAL COMMUNITIES

EPA Science Inventory

Although recent technological advances in DNA sequencing and computational biology now allow scientists to compare entire microbial genomes, the use of these approaches to discern key genomic differences between natural microbial communities remains prohibitively expensive for mo...

Identification of Bacterial DNA Markers for the Detection of Human and Cattle Fecal Pollution - SLIDES

EPA Science Inventory

Technological advances in DNA sequencing and computational biology allow scientists to compare entire microbial genomes. However, the use of these approaches to discern key genomic differences between natural microbial communities remains prohibitively expensive for most laborato...

IDENTIFICATION OF BACTERIAL DNA MARKERS FOR THE DETECTION OF HUMAN AND CATTLE FECAL POLLUTION

EPA Science Inventory

Technological advances in DNA sequencing and computational biology allow scientists to compare entire microbial genomes. However, the use of these approaches to discern key genomic differences between natural microbial communities remains prohibitively expensive for most laborato...

Comparing memory-efficient genome assemblers on stand-alone and cloud infrastructures.

PubMed

Kleftogiannis, Dimitrios; Kalnis, Panos; Bajic, Vladimir B

2013-01-01

A fundamental problem in bioinformatics is genome assembly. Next-generation sequencing (NGS) technologies produce large volumes of fragmented genome reads, which require large amounts of memory to assemble the complete genome efficiently. With recent improvements in DNA sequencing technologies, it is expected that the memory footprint required for the assembly process will increase dramatically and will emerge as a limiting factor in processing widely available NGS-generated reads. In this report, we compare current memory-efficient techniques for genome assembly with respect to quality, memory consumption and execution time. Our experiments prove that it is possible to generate draft assemblies of reasonable quality on conventional multi-purpose computers with very limited available memory by choosing suitable assembly methods. Our study reveals the minimum memory requirements for different assembly programs even when data volume exceeds memory capacity by orders of magnitude. By combining existing methodologies, we propose two general assembly strategies that can improve short-read assembly approaches and result in reduction of the memory footprint. Finally, we discuss the possibility of utilizing cloud infrastructures for genome assembly and we comment on some findings regarding suitable computational resources for assembly.

mySyntenyPortal: an application package to construct websites for synteny block analysis.

PubMed

Lee, Jongin; Lee, Daehwan; Sim, Mikang; Kwon, Daehong; Kim, Juyeon; Ko, Younhee; Kim, Jaebum

2018-06-05

Advances in sequencing technologies have facilitated large-scale comparative genomics based on whole genome sequencing. Constructing and investigating conserved genomic regions among multiple species (called synteny blocks) are essential in the comparative genomics. However, they require significant amounts of computational resources and time in addition to bioinformatics skills. Many web interfaces have been developed to make such tasks easier. However, these web interfaces cannot be customized for users who want to use their own set of genome sequences or definition of synteny blocks. To resolve this limitation, we present mySyntenyPortal, a stand-alone application package to construct websites for synteny block analyses by using users' own genome data. mySyntenyPortal provides both command line and web-based interfaces to build and manage websites for large-scale comparative genomic analyses. The websites can be also easily published and accessed by other users. To demonstrate the usability of mySyntenyPortal, we present an example study for building websites to compare genomes of three mammalian species (human, mouse, and cow) and show how they can be easily utilized to identify potential genes affected by genome rearrangements. mySyntenyPortal will contribute for extended comparative genomic analyses based on large-scale whole genome sequences by providing unique functionality to support the easy creation of interactive websites for synteny block analyses from user's own genome data.

A new computational method for the detection of horizontal gene transfer events.

PubMed

Tsirigos, Aristotelis; Rigoutsos, Isidore

2005-01-01

In recent years, the increase in the amounts of available genomic data has made it easier to appreciate the extent by which organisms increase their genetic diversity through horizontally transferred genetic material. Such transfers have the potential to give rise to extremely dynamic genomes where a significant proportion of their coding DNA has been contributed by external sources. Because of the impact of these horizontal transfers on the ecological and pathogenic character of the recipient organisms, methods are continuously sought that are able to computationally determine which of the genes of a given genome are products of transfer events. In this paper, we introduce and discuss a novel computational method for identifying horizontal transfers that relies on a gene's nucleotide composition and obviates the need for knowledge of codon boundaries. In addition to being applicable to individual genes, the method can be easily extended to the case of clusters of horizontally transferred genes. With the help of an extensive and carefully designed set of experiments on 123 archaeal and bacterial genomes, we demonstrate that the new method exhibits significant improvement in sensitivity when compared to previously published approaches. In fact, it achieves an average relative improvement across genomes of between 11 and 41% compared to the Codon Adaptation Index method in distinguishing native from foreign genes. Our method's horizontal gene transfer predictions for 123 microbial genomes are available online at http://cbcsrv.watson.ibm.com/HGT/.

TRACTOR_DB: a database of regulatory networks in gamma-proteobacterial genomes

PubMed Central

González, Abel D.; Espinosa, Vladimir; Vasconcelos, Ana T.; Pérez-Rueda, Ernesto; Collado-Vides, Julio

2005-01-01

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites and operons) within its genome. As more genomes of gamma-proteobacteria are being sequenced, the prediction of these elements in a growing number of organisms has become more feasible, as a step towards the study of how different bacteria respond to environmental changes at the level of transcriptional regulation. In this work, we present TRACTOR_DB (TRAnscription FaCTORs' predicted binding sites in prokaryotic genomes), a relational database that contains computational predictions of new members of 74 regulons in 17 gamma-proteobacterial genomes. For these predictions we used a comparative genomics approach regarding which several proof-of-principle articles for large regulons have been published. TRACTOR_DB may be currently accessed at http://www.bioinfo.cu/Tractor_DB, http://www.tractor.lncc.br/ or at http://www.cifn.unam.mx/Computational_Genomics/tractorDB. Contact Email id is tractor@cifn.unam.mx. PMID:15608293

A comprehensive overview of computational resources to aid in precision genome editing with engineered nucleases.

PubMed

Periwal, Vinita

2017-07-01

Genome editing with engineered nucleases (zinc finger nucleases, TAL effector nucleases s and Clustered regularly inter-spaced short palindromic repeats/CRISPR-associated) has recently been shown to have great promise in a variety of therapeutic and biotechnological applications. However, their exploitation in genetic analysis and clinical settings largely depends on their specificity for the intended genomic target. Large and complex genomes often contain highly homologous/repetitive sequences, which limits the specificity of genome editing tools and could result in off-target activity. Over the past few years, various computational approaches have been developed to assist the design process and predict/reduce the off-target activity of these nucleases. These tools could be efficiently used to guide the design of constructs for engineered nucleases and evaluate results after genome editing. This review provides a comprehensive overview of various databases, tools, web servers and resources for genome editing and compares their features and functionalities. Additionally, it also describes tools that have been developed to analyse post-genome editing results. The article also discusses important design parameters that could be considered while designing these nucleases. This review is intended to be a quick reference guide for experimentalists as well as computational biologists working in the field of genome editing with engineered nucleases. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Genetic, genomic, and molecular tools for studying the protoploid yeast, L. waltii.

PubMed

Di Rienzi, Sara C; Lindstrom, Kimberly C; Lancaster, Ragina; Rolczynski, Lisa; Raghuraman, M K; Brewer, Bonita J

2011-02-01

Sequencing of the yeast Kluyveromyces waltii (recently renamed Lachancea waltii) provided evidence of a whole genome duplication event in the lineage leading to the well-studied Saccharomyces cerevisiae. While comparative genomic analyses of these yeasts have proven to be extremely instructive in modeling the loss or maintenance of gene duplicates, experimental tests of the ramifications following such genome alterations remain difficult. To transform L. waltii from an organism of the computational comparative genomic literature into an organism of the functional comparative genomic literature, we have developed genetic, molecular and genomic tools for working with L. waltii. In particular, we have characterized basic properties of L. waltii (growth, ploidy, molecular karyotype, mating type and the sexual cycle), developed transformation, cell cycle arrest and synchronization protocols, and have created centromeric and non-centromeric vectors as well as a genome browser for L. waltii. We hope that these tools will be used by the community to follow up on the ideas generated by sequence data and lead to a greater understanding of eukaryotic biology and genome evolution. 2010 John Wiley & Sons, Ltd.

Genetic, genomic, and molecular tools for studying the protoploid yeast, L. waltii

PubMed Central

Di Rienzi, Sara C.; Lindstrom, Kimberly C.; Lancaster, Ragina; Rolczynski, Lisa; Raghuraman, M. K.; Brewer, Bonita J.

2011-01-01

Sequencing of the yeast Kluyveromyces waltii (recently renamed Lachancea waltii) provided evidence of a whole genome duplication event in the lineage leading to the well-studied Saccharomyces cerevisiae. While comparative genomic analyses of these yeasts have proven to be extremely instructive in modeling the loss or maintenance of gene duplicates, experimental tests of the ramifications following such genome alterations remain difficult. To transform L. waltii from an organism of the computational comparative genomic literature into an organism of the functional comparative genomic literature, we have developed genetic, molecular and genomic tools for working with L. waltii. In particular, we have characterized basic properties of L. waltii (growth, ploidy, molecular karyotype, mating type and the sexual cycle), developed transformation, cell cycle arrest and synchronization protocols, and have created centromeric and non-centromeric vectors as well as a genome browser for L. waltii. We hope that these tools will be used by the community to follow up on the ideas generated by sequence data and lead to a greater understanding of eukaryotic biology and genome evolution. PMID:21246627

A comparative analysis of soft computing techniques for gene prediction.

PubMed

Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure

PubMed Central

Theis, Corinna; Zirbel, Craig L.; zu Siederdissen, Christian Höner; Anthon, Christian; Hofacker, Ivo L.; Nielsen, Henrik; Gorodkin, Jan

2015-01-01

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence. PMID:26509713

ITEP: an integrated toolkit for exploration of microbial pan-genomes.

PubMed

Benedict, Matthew N; Henriksen, James R; Metcalf, William W; Whitaker, Rachel J; Price, Nathan D

2014-01-03

Comparative genomics is a powerful approach for studying variation in physiological traits as well as the evolution and ecology of microorganisms. Recent technological advances have enabled sequencing large numbers of related genomes in a single project, requiring computational tools for their integrated analysis. In particular, accurate annotations and identification of gene presence and absence are critical for understanding and modeling the cellular physiology of newly sequenced genomes. Although many tools are available to compare the gene contents of related genomes, new tools are necessary to enable close examination and curation of protein families from large numbers of closely related organisms, to integrate curation with the analysis of gain and loss, and to generate metabolic networks linking the annotations to observed phenotypes. We have developed ITEP, an Integrated Toolkit for Exploration of microbial Pan-genomes, to curate protein families, compute similarities to externally-defined domains, analyze gene gain and loss, and generate draft metabolic networks from one or more curated reference network reconstructions in groups of related microbial species among which the combination of core and variable genes constitute the their "pan-genomes". The ITEP toolkit consists of: (1) a series of modular command-line scripts for identification, comparison, curation, and analysis of protein families and their distribution across many genomes; (2) a set of Python libraries for programmatic access to the same data; and (3) pre-packaged scripts to perform common analysis workflows on a collection of genomes. ITEP's capabilities include de novo protein family prediction, ortholog detection, analysis of functional domains, identification of core and variable genes and gene regions, sequence alignments and tree generation, annotation curation, and the integration of cross-genome analysis and metabolic networks for study of metabolic network evolution. ITEP is a powerful, flexible toolkit for generation and curation of protein families. ITEP's modular design allows for straightforward extension as analysis methods and tools evolve. By integrating comparative genomics with the development of draft metabolic networks, ITEP harnesses the power of comparative genomics to build confidence in links between genotype and phenotype and helps disambiguate gene annotations when they are evaluated in both evolutionary and metabolic network contexts.

Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database.

PubMed

Winsor, Geoffrey L; Griffiths, Emma J; Lo, Raymond; Dhillon, Bhavjinder K; Shay, Julie A; Brinkman, Fiona S L

2016-01-04

The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using community-based approaches including a major new Pseudomonas community initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Genomic impact of eukaryotic transposable elements

PubMed Central

2012-01-01

The third international conference on the genomic impact of eukaryotic transposable elements (TEs) was held 24 to 28 February 2012 at the Asilomar Conference Center, Pacific Grove, CA, USA. Sponsored in part by the National Institutes of Health grant 5 P41 LM006252, the goal of the conference was to bring together researchers from around the world who study the impact and mechanisms of TEs using multiple computational and experimental approaches. The meeting drew close to 170 attendees and included invited floor presentations on the biology of TEs and their genomic impact, as well as numerous talks contributed by young scientists. The workshop talks were devoted to computational analysis of TEs with additional time for discussion of unresolved issues. Also, there was ample opportunity for poster presentations and informal evening discussions. The success of the meeting reflects the important role of Repbase in comparative genomic studies, and emphasizes the need for close interactions between experimental and computational biologists in the years to come. PMID:23171443

Genomic impact of eukaryotic transposable elements.

PubMed

Arkhipova, Irina R; Batzer, Mark A; Brosius, Juergen; Feschotte, Cédric; Moran, John V; Schmitz, Jürgen; Jurka, Jerzy

2012-11-21

The third international conference on the genomic impact of eukaryotic transposable elements (TEs) was held 24 to 28 February 2012 at the Asilomar Conference Center, Pacific Grove, CA, USA. Sponsored in part by the National Institutes of Health grant 5 P41 LM006252, the goal of the conference was to bring together researchers from around the world who study the impact and mechanisms of TEs using multiple computational and experimental approaches. The meeting drew close to 170 attendees and included invited floor presentations on the biology of TEs and their genomic impact, as well as numerous talks contributed by young scientists. The workshop talks were devoted to computational analysis of TEs with additional time for discussion of unresolved issues. Also, there was ample opportunity for poster presentations and informal evening discussions. The success of the meeting reflects the important role of Repbase in comparative genomic studies, and emphasizes the need for close interactions between experimental and computational biologists in the years to come.

Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics.

PubMed

Louis, Alexandra; Nguyen, Nga Thi Thuy; Muffato, Matthieu; Roest Crollius, Hugues

2015-01-01

The Genomicus web server (http://www.genomicus.biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and AlignView) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved non-coding elements with their putative gene targets. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Compare Gene Calls

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

Ecale Zhou, Carol L.

2016-07-05

Compare Gene Calls (CGC) is a Python code used for combining and comparing gene calls from any number of gene callers. A gene caller is a computer program that predicts the extends of open reading frames within genomes of biological organisms.

GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies.

PubMed

Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan

2011-05-01

Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions. We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card. GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

PubMed

Lee, Kang-Hoon; Shin, Kyung-Seop; Lim, Debora; Kim, Woo-Chan; Chung, Byung Chang; Han, Gyu-Bum; Roh, Jeongkyu; Cho, Dong-Ho; Cho, Kiho

2015-07-01

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes. Copyright © 2015 Elsevier Inc. All rights reserved.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: Challenges, opportunities, and research needs

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

Burgess-Herbert, Sarah L., E-mail: sarah.burgess@alum.mit.edu; Euling, Susan Y.

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionarymore » biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.« less

Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy.

PubMed

Ahmad, Meraj; Sinha, Anubhav; Ghosh, Sreya; Kumar, Vikrant; Davila, Sonia; Yajnik, Chittaranjan S; Chandak, Giriraj R

2017-07-27

Imputation is a computational method based on the principle of haplotype sharing allowing enrichment of genome-wide association study datasets. It depends on the haplotype structure of the population and density of the genotype data. The 1000 Genomes Project led to the generation of imputation reference panels which have been used globally. However, recent studies have shown that population-specific panels provide better enrichment of genome-wide variants. We compared the imputation accuracy using 1000 Genomes phase 3 reference panel and a panel generated from genome-wide data on 407 individuals from Western India (WIP). The concordance of imputed variants was cross-checked with next-generation re-sequencing data on a subset of genomic regions. Further, using the genome-wide data from 1880 individuals, we demonstrate that WIP works better than the 1000 Genomes phase 3 panel and when merged with it, significantly improves the imputation accuracy throughout the minor allele frequency range. We also show that imputation using only South Asian component of the 1000 Genomes phase 3 panel works as good as the merged panel, making it computationally less intensive job. Thus, our study stresses that imputation accuracy using 1000 Genomes phase 3 panel can be further improved by including population-specific reference panels from South Asia.

Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers.

PubMed

Church, Philip C; Goscinski, Andrzej; Holt, Kathryn; Inouye, Michael; Ghoting, Amol; Makarychev, Konstantin; Reumann, Matthias

2011-01-01

The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.

A universal genomic coordinate translator for comparative genomics

PubMed Central

2014-01-01

Background Genomic duplications constitute major events in the evolution of species, allowing paralogous copies of genes to take on fine-tuned biological roles. Unambiguously identifying the orthology relationship between copies across multiple genomes can be resolved by synteny, i.e. the conserved order of genomic sequences. However, a comprehensive analysis of duplication events and their contributions to evolution would require all-to-all genome alignments, which increases at N2 with the number of available genomes, N. Results Here, we introduce Kraken, software that omits the all-to-all requirement by recursively traversing a graph of pairwise alignments and dynamically re-computing orthology. Kraken scales linearly with the number of targeted genomes, N, which allows for including large numbers of genomes in analyses. We first evaluated the method on the set of 12 Drosophila genomes, finding that orthologous correspondence computed indirectly through a graph of multiple synteny maps comes at minimal cost in terms of sensitivity, but reduces overall computational runtime by an order of magnitude. We then used the method on three well-annotated mammalian genomes, human, mouse, and rat, and show that up to 93% of protein coding transcripts have unambiguous pairwise orthologous relationships across the genomes. On a nucleotide level, 70 to 83% of exons match exactly at both splice junctions, and up to 97% on at least one junction. We last applied Kraken to an RNA-sequencing dataset from multiple vertebrates and diverse tissues, where we confirmed that brain-specific gene family members, i.e. one-to-many or many-to-many homologs, are more highly correlated across species than single-copy (i.e. one-to-one homologous) genes. Not limited to protein coding genes, Kraken also identifies thousands of newly identified transcribed loci, likely non-coding RNAs that are consistently transcribed in human, chimpanzee and gorilla, and maintain significant correlation of expression levels across species. Conclusions Kraken is a computational genome coordinate translator that facilitates cross-species comparisons, distinguishes orthologs from paralogs, and does not require costly all-to-all whole genome mappings. Kraken is freely available under LPGL from http://github.com/nedaz/kraken. PMID:24976580

A universal genomic coordinate translator for comparative genomics.

PubMed

Zamani, Neda; Sundström, Görel; Meadows, Jennifer R S; Höppner, Marc P; Dainat, Jacques; Lantz, Henrik; Haas, Brian J; Grabherr, Manfred G

2014-06-30

Genomic duplications constitute major events in the evolution of species, allowing paralogous copies of genes to take on fine-tuned biological roles. Unambiguously identifying the orthology relationship between copies across multiple genomes can be resolved by synteny, i.e. the conserved order of genomic sequences. However, a comprehensive analysis of duplication events and their contributions to evolution would require all-to-all genome alignments, which increases at N2 with the number of available genomes, N. Here, we introduce Kraken, software that omits the all-to-all requirement by recursively traversing a graph of pairwise alignments and dynamically re-computing orthology. Kraken scales linearly with the number of targeted genomes, N, which allows for including large numbers of genomes in analyses. We first evaluated the method on the set of 12 Drosophila genomes, finding that orthologous correspondence computed indirectly through a graph of multiple synteny maps comes at minimal cost in terms of sensitivity, but reduces overall computational runtime by an order of magnitude. We then used the method on three well-annotated mammalian genomes, human, mouse, and rat, and show that up to 93% of protein coding transcripts have unambiguous pairwise orthologous relationships across the genomes. On a nucleotide level, 70 to 83% of exons match exactly at both splice junctions, and up to 97% on at least one junction. We last applied Kraken to an RNA-sequencing dataset from multiple vertebrates and diverse tissues, where we confirmed that brain-specific gene family members, i.e. one-to-many or many-to-many homologs, are more highly correlated across species than single-copy (i.e. one-to-one homologous) genes. Not limited to protein coding genes, Kraken also identifies thousands of newly identified transcribed loci, likely non-coding RNAs that are consistently transcribed in human, chimpanzee and gorilla, and maintain significant correlation of expression levels across species. Kraken is a computational genome coordinate translator that facilitates cross-species comparisons, distinguishes orthologs from paralogs, and does not require costly all-to-all whole genome mappings. Kraken is freely available under LPGL from http://github.com/nedaz/kraken.

Plant Comparative and Functional Genomics

DOE PAGES

Yang, Xiaohan; Leebens-Mack, Jim; Chen, Feng; ...

2015-01-01

Plants form the foundation for our global ecosystem and are essential for environmental and human health. An increasing number of available plant genomes and tractable experimental systems, comparative and functional plant genomics research is greatly expanding our knowledge of the molecular basis of economically and nutritionally important traits in crop plants. Inferences drawn from comparative genomics are motivating experimental investigations of gene function and gene interactions. In this special issue aims to highlight recent advances made in comparative and functional genomics research in plants. Nine original research articles in this special issue cover five important topics: (1) transcription factor genemore » families relevant to abiotic stress tolerance; (2) plant secondary metabolism; (3) transcriptomebased markers for quantitative trait locus; (4) epigenetic modifications in plant-microbe interactions; and (5) computational prediction of protein-protein interactions. Finally, we studied the plant species in these articles which include model species as well as nonmodel plant species of economic importance (e.g., food crops and medicinal plants).« less

Plant Comparative and Functional Genomics

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

Yang, Xiaohan; Leebens-Mack, Jim; Chen, Feng

Plants form the foundation for our global ecosystem and are essential for environmental and human health. An increasing number of available plant genomes and tractable experimental systems, comparative and functional plant genomics research is greatly expanding our knowledge of the molecular basis of economically and nutritionally important traits in crop plants. Inferences drawn from comparative genomics are motivating experimental investigations of gene function and gene interactions. In this special issue aims to highlight recent advances made in comparative and functional genomics research in plants. Nine original research articles in this special issue cover five important topics: (1) transcription factor genemore » families relevant to abiotic stress tolerance; (2) plant secondary metabolism; (3) transcriptomebased markers for quantitative trait locus; (4) epigenetic modifications in plant-microbe interactions; and (5) computational prediction of protein-protein interactions. Finally, we studied the plant species in these articles which include model species as well as nonmodel plant species of economic importance (e.g., food crops and medicinal plants).« less

Fast and Accurate Approximation to Significance Tests in Genome-Wide Association Studies

PubMed Central

Zhang, Yu; Liu, Jun S.

2011-01-01

Genome-wide association studies commonly involve simultaneous tests of millions of single nucleotide polymorphisms (SNP) for disease association. The SNPs in nearby genomic regions, however, are often highly correlated due to linkage disequilibrium (LD, a genetic term for correlation). Simple Bonferonni correction for multiple comparisons is therefore too conservative. Permutation tests, which are often employed in practice, are both computationally expensive for genome-wide studies and limited in their scopes. We present an accurate and computationally efficient method, based on Poisson de-clumping heuristics, for approximating genome-wide significance of SNP associations. Compared with permutation tests and other multiple comparison adjustment approaches, our method computes the most accurate and robust p-value adjustments for millions of correlated comparisons within seconds. We demonstrate analytically that the accuracy and the efficiency of our method are nearly independent of the sample size, the number of SNPs, and the scale of p-values to be adjusted. In addition, our method can be easily adopted to estimate false discovery rate. When applied to genome-wide SNP datasets, we observed highly variable p-value adjustment results evaluated from different genomic regions. The variation in adjustments along the genome, however, are well conserved between the European and the African populations. The p-value adjustments are significantly correlated with LD among SNPs, recombination rates, and SNP densities. Given the large variability of sequence features in the genome, we further discuss a novel approach of using SNP-specific (local) thresholds to detect genome-wide significant associations. This article has supplementary material online. PMID:22140288

Genomic identification of regulatory elements by evolutionary sequence comparison and functional analysis.

PubMed

Loots, Gabriela G

2008-01-01

Despite remarkable recent advances in genomics that have enabled us to identify most of the genes in the human genome, comparable efforts to define transcriptional cis-regulatory elements that control gene expression are lagging behind. The difficulty of this task stems from two equally important problems: our knowledge of how regulatory elements are encoded in genomes remains elementary, and there is a vast genomic search space for regulatory elements, since most of mammalian genomes are noncoding. Comparative genomic approaches are having a remarkable impact on the study of transcriptional regulation in eukaryotes and currently represent the most efficient and reliable methods of predicting noncoding sequences likely to control the patterns of gene expression. By subjecting eukaryotic genomic sequences to computational comparisons and subsequent experimentation, we are inching our way toward a more comprehensive catalog of common regulatory motifs that lie behind fundamental biological processes. We are still far from comprehending how the transcriptional regulatory code is encrypted in the human genome and providing an initial global view of regulatory gene networks, but collectively, the continued development of comparative and experimental approaches will rapidly expand our knowledge of the transcriptional regulome.

A new strategy for genome assembly using short sequence reads and reduced representation libraries.

PubMed

Young, Andrew L; Abaan, Hatice Ozel; Zerbino, Daniel; Mullikin, James C; Birney, Ewan; Margulies, Elliott H

2010-02-01

We have developed a novel approach for using massively parallel short-read sequencing to generate fast and inexpensive de novo genomic assemblies comparable to those generated by capillary-based methods. The ultrashort (<100 base) sequences generated by this technology pose specific biological and computational challenges for de novo assembly of large genomes. To account for this, we devised a method for experimentally partitioning the genome using reduced representation (RR) libraries prior to assembly. We use two restriction enzymes independently to create a series of overlapping fragment libraries, each containing a tractable subset of the genome. Together, these libraries allow us to reassemble the entire genome without the need of a reference sequence. As proof of concept, we applied this approach to sequence and assembled the majority of the 125-Mb Drosophila melanogaster genome. We subsequently demonstrate the accuracy of our assembly method with meaningful comparisons against the current available D. melanogaster reference genome (dm3). The ease of assembly and accuracy for comparative genomics suggest that our approach will scale to future mammalian genome-sequencing efforts, saving both time and money without sacrificing quality.

Ergatis: a web interface and scalable software system for bioinformatics workflows

PubMed Central

Orvis, Joshua; Crabtree, Jonathan; Galens, Kevin; Gussman, Aaron; Inman, Jason M.; Lee, Eduardo; Nampally, Sreenath; Riley, David; Sundaram, Jaideep P.; Felix, Victor; Whitty, Brett; Mahurkar, Anup; Wortman, Jennifer; White, Owen; Angiuoli, Samuel V.

2010-01-01

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users. Results: We have developed a workflow management system named Ergatis that enables users to build, execute and monitor pipelines for computational analysis of genomics data. Ergatis contains preconfigured components and template pipelines for a number of common bioinformatics tasks such as prokaryotic genome annotation and genome comparisons. Outputs from many of these components can be loaded into a Chado relational database. Ergatis was designed to be accessible to a broad class of users and provides a user friendly, web-based interface. Ergatis supports high-throughput batch processing on distributed compute clusters and has been used for data management in a number of genome annotation and comparative genomics projects. Availability: Ergatis is an open-source project and is freely available at http://ergatis.sourceforge.net Contact: jorvis@users.sourceforge.net PMID:20413634

Mapping the Space of Genomic Signatures

PubMed Central

Kari, Lila; Hill, Kathleen A.; Sayem, Abu S.; Karamichalis, Rallis; Bryans, Nathaniel; Davis, Katelyn; Dattani, Nikesh S.

2015-01-01

We propose a computational method to measure and visualize interrelationships among any number of DNA sequences allowing, for example, the examination of hundreds or thousands of complete mitochondrial genomes. An "image distance" is computed for each pair of graphical representations of DNA sequences, and the distances are visualized as a Molecular Distance Map: Each point on the map represents a DNA sequence, and the spatial proximity between any two points reflects the degree of structural similarity between the corresponding sequences. The graphical representation of DNA sequences utilized, Chaos Game Representation (CGR), is genome- and species-specific and can thus act as a genomic signature. Consequently, Molecular Distance Maps could inform species identification, taxonomic classifications and, to a certain extent, evolutionary history. The image distance employed, Structural Dissimilarity Index (DSSIM), implicitly compares the occurrences of oligomers of length up to k (herein k = 9) in DNA sequences. We computed DSSIM distances for more than 5 million pairs of complete mitochondrial genomes, and used Multi-Dimensional Scaling (MDS) to obtain Molecular Distance Maps that visually display the sequence relatedness in various subsets, at different taxonomic levels. This general-purpose method does not require DNA sequence alignment and can thus be used to compare similar or vastly different DNA sequences, genomic or computer-generated, of the same or different lengths. We illustrate potential uses of this approach by applying it to several taxonomic subsets: phylum Vertebrata, (super)kingdom Protista, classes Amphibia-Insecta-Mammalia, class Amphibia, and order Primates. This analysis of an extensive dataset confirms that the oligomer composition of full mtDNA sequences can be a source of taxonomic information. This method also correctly finds the mtDNA sequences most closely related to that of the anatomically modern human (the Neanderthal, the Denisovan, and the chimp), and that the sequence most different from it in this dataset belongs to a cucumber. PMID:26000734

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.

PubMed

Chen, Feng; Wang, Chenghong; Dai, Wenrui; Jiang, Xiaoqian; Mohammed, Noman; Al Aziz, Md Momin; Sadat, Md Nazmus; Sahinalp, Cenk; Lauter, Kristin; Wang, Shuang

2017-07-26

Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data. We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing. We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation. The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

Construction of Red Fox Chromosomal Fragments from the Short-Read Genome Assembly.

PubMed

Rando, Halie M; Farré, Marta; Robson, Michael P; Won, Naomi B; Johnson, Jennifer L; Buch, Ronak; Bastounes, Estelle R; Xiang, Xueyan; Feng, Shaohong; Liu, Shiping; Xiong, Zijun; Kim, Jaebum; Zhang, Guojie; Trut, Lyudmila N; Larkin, Denis M; Kukekova, Anna V

2018-06-20

The genome of a red fox ( Vulpes vulpes ) was recently sequenced and assembled using next-generation sequencing (NGS). The assembly is of high quality, with 94X coverage and a scaffold N50 of 11.8 Mbp, but is split into 676,878 scaffolds, some of which are likely to contain assembly errors. Fragmentation and misassembly hinder accurate gene prediction and downstream analysis such as the identification of loci under selection. Therefore, assembly of the genome into chromosome-scale fragments was an important step towards developing this genomic model. Scaffolds from the assembly were aligned to the dog reference genome and compared to the alignment of an outgroup genome (cat) against the dog to identify syntenic sequences among species. The program Reference-Assisted Chromosome Assembly (RACA) then integrated the comparative alignment with the mapping of the raw sequencing reads generated during assembly against the fox scaffolds. The 128 sequence fragments RACA assembled were compared to the fox meiotic linkage map to guide the construction of 40 chromosomal fragments. This computational approach to assembly was facilitated by prior research in comparative mammalian genomics, and the continued improvement of the red fox genome can in turn offer insight into canid and carnivore chromosome evolution. This assembly is also necessary for advancing genetic research in foxes and other canids.

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species.

PubMed

Perumal, Deepak; Lim, Chu Sing; Chow, Vincent T K; Sakharkar, Kishore R; Sakharkar, Meena K

2008-09-10

Comparative genomic analysis has revolutionized our ability to predict the metabolic subsystems that occur in newly sequenced genomes, and to explore the functional roles of the set of genes within each subsystem. These computational predictions can considerably reduce the volume of experimental studies required to assess basic metabolic properties of multiple bacterial species. However, experimental validations are still required to resolve the apparent inconsistencies in the predictions by multiple resources. Here, we present combined computational-experimental analyses on eight completely sequenced Pseudomonas species. Comparative pathway analyses reveal that several pathways within the Pseudomonas species show high plasticity and versatility. Potential bypasses in 11 metabolic pathways were identified. We further confirmed the presence of the enzyme O-acetyl homoserine (thiol) lyase (EC: 2.5.1.49) in P. syringae pv. tomato that revealed inconsistent annotations in KEGG and in the recently published SYSTOMONAS database. These analyses connect and integrate systematic data generation, computational data interpretation, and experimental validation and represent a synergistic and powerful means for conducting biological research.

A machine-learned computational functional genomics-based approach to drug classification.

PubMed

Lötsch, Jörn; Ultsch, Alfred

2016-12-01

The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug discovery and repurposing.

Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment.

PubMed

Baichoo, Shakuntala; Ouzounis, Christos A

A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality. Copyright © 2017 Elsevier B.V. All rights reserved.

Genome-Wide Comparative Gene Family Classification

PubMed Central

Frech, Christian; Chen, Nansheng

2010-01-01

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

From days to hours: reporting clinically actionable variants from whole genome sequencing.

PubMed

Middha, Sumit; Baheti, Saurabh; Hart, Steven N; Kocher, Jean-Pierre A

2014-01-01

As the cost of whole genome sequencing (WGS) decreases, clinical laboratories will be looking at broadly adopting this technology to screen for variants of clinical significance. To fully leverage this technology in a clinical setting, results need to be reported quickly, as the turnaround rate could potentially impact patient care. The latest sequencers can sequence a whole human genome in about 24 hours. However, depending on the computing infrastructure available, the processing of data can take several days, with the majority of computing time devoted to aligning reads to genomics regions that are to date not clinically interpretable. In an attempt to accelerate the reporting of clinically actionable variants, we have investigated the utility of a multi-step alignment algorithm focused on aligning reads and calling variants in genomic regions of clinical relevance prior to processing the remaining reads on the whole genome. This iterative workflow significantly accelerates the reporting of clinically actionable variants with no loss of accuracy when compared to genotypes obtained with the OMNI SNP platform or to variants detected with a standard workflow that combines Novoalign and GATK.

Post-Genomics and Vaccine Improvement for Leishmania

PubMed Central

Seyed, Negar; Taheri, Tahereh; Rafati, Sima

2016-01-01

Leishmaniasis is a parasitic disease that primarily affects Asia, Africa, South America, and the Mediterranean basin. Despite extensive efforts to develop an effective prophylactic vaccine, no promising vaccine is available yet. However, recent advancements in computational vaccinology on the one hand and genome sequencing approaches on the other have generated new hopes in vaccine development. Computational genome mining for new vaccine candidates is known as reverse vaccinology and is believed to further extend the current list of Leishmania vaccine candidates. Reverse vaccinology can also reduce the intrinsic risks associated with live attenuated vaccines. Individual epitopes arranged in tandem as polytopes are also a possible outcome of reverse genome mining. Here, we will briefly compare reverse vaccinology with conventional vaccinology in respect to Leishmania vaccine, and we will discuss how it influences the aforementioned topics. We will also introduce new in vivo models that will bridge the gap between human and laboratory animal models in future studies. PMID:27092123

Private genome analysis through homomorphic encryption

PubMed Central

2015-01-01

Background The rapid development of genome sequencing technology allows researchers to access large genome datasets. However, outsourcing the data processing o the cloud poses high risks for personal privacy. The aim of this paper is to give a practical solution for this problem using homomorphic encryption. In our approach, all the computations can be performed in an untrusted cloud without requiring the decryption key or any interaction with the data owner, which preserves the privacy of genome data. Methods We present evaluation algorithms for secure computation of the minor allele frequencies and χ2 statistic in a genome-wide association studies setting. We also describe how to privately compute the Hamming distance and approximate Edit distance between encrypted DNA sequences. Finally, we compare performance details of using two practical homomorphic encryption schemes - the BGV scheme by Gentry, Halevi and Smart and the YASHE scheme by Bos, Lauter, Loftus and Naehrig. Results The approach with the YASHE scheme analyzes data from 400 people within about 2 seconds and picks a variant associated with disease from 311 spots. For another task, using the BGV scheme, it took about 65 seconds to securely compute the approximate Edit distance for DNA sequences of size 5K and figure out the differences between them. Conclusions The performance numbers for BGV are better than YASHE when homomorphically evaluating deep circuits (like the Hamming distance algorithm or approximate Edit distance algorithm). On the other hand, it is more efficient to use the YASHE scheme for a low-degree computation, such as minor allele frequencies or χ2 test statistic in a case-control study. PMID:26733152

A computer-based education intervention to enhance surrogates' informed consent for genomics research.

PubMed

Shelton, Ann K; Freeman, Bradley D; Fish, Anne F; Bachman, Jean A; Richardson, Lloyd I

2015-03-01

Many research studies conducted today in critical care have a genomics component. Patients' surrogates asked to authorize participation in genomics research for a loved one in the intensive care unit may not be prepared to make informed decisions about a patient's participation in the research. To examine the effectiveness of a new, computer-based education module on surrogates' understanding of the process of informed consent for genomics research. A pilot study was conducted with visitors in the waiting rooms of 2 intensive care units in a Midwestern tertiary care medical center. Visitors were randomly assigned to the experimental (education module plus a sample genomics consent form; n = 65) or the control (sample genomics consent form only; n = 69) group. Participants later completed a test on informed genomics consent. Understanding the process of informed consent was greater (P = .001) in the experimental group than in the control group. Specifically, compared with the control group, the experimental group had a greater understanding of 8 of 13 elements of informed consent: intended benefits of research (P = .02), definition of surrogate consenter (P= .001), withdrawal from the study (P = .001), explanation of risk (P = .002), purpose of the institutional review board (P = .001), definition of substituted judgment (P = .03), compensation for harm (P = .001), and alternative treatments (P = .004). Computer-based education modules may be an important addition to conventional approaches for obtaining informed consent in the intensive care unit. Preparing patients' family members who may consider serving as surrogate consenters is critical to facilitating genomics research in critical care. ©2015 American Association of Critical-Care Nurses.

Advances in computer simulation of genome evolution: toward more realistic evolutionary genomics analysis by approximate bayesian computation.

PubMed

Arenas, Miguel

2015-04-01

NGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

PubMed Central

Fabregat-Traver, Diego; Sharapov, Sodbo Zh.; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL. PMID:25717363

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software.

PubMed

Fabregat-Traver, Diego; Sharapov, Sodbo Zh; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

Comparative Genomics in Drosophila.

PubMed

Oti, Martin; Pane, Attilio; Sammeth, Michael

2018-01-01

Since the pioneering studies of Thomas Hunt Morgan and coworkers at the dawn of the twentieth century, Drosophila melanogaster and its sister species have tremendously contributed to unveil the rules underlying animal genetics, development, behavior, evolution, and human disease. Recent advances in DNA sequencing technologies launched Drosophila into the post-genomic era and paved the way for unprecedented comparative genomics investigations. The complete sequencing and systematic comparison of the genomes from 12 Drosophila species represents a milestone achievement in modern biology, which allowed a plethora of different studies ranging from the annotation of known and novel genomic features to the evolution of chromosomes and, ultimately, of entire genomes. Despite the efforts of countless laboratories worldwide, the vast amount of data that were produced over the past 15 years is far from being fully explored.In this chapter, we will review some of the bioinformatic approaches that were developed to interrogate the genomes of the 12 Drosophila species. Setting off from alignments of the entire genomic sequences, the degree of conservation can be separately evaluated for every region of the genome, providing already first hints about elements that are under purifying selection and therefore likely functional. Furthermore, the careful analysis of repeated sequences sheds light on the evolutionary dynamics of transposons, an enigmatic and fascinating class of mobile elements housed in the genomes of animals and plants. Comparative genomics also aids in the computational identification of the transcriptionally active part of the genome, first and foremost of protein-coding loci, but also of transcribed nevertheless apparently noncoding regions, which were once considered "junk" DNA. Eventually, the synergy between functional and comparative genomics also facilitates in silico and in vivo studies on cis-acting regulatory elements, like transcription factor binding sites, that due to the high degree of sequence variability usually impose increased challenges for bioinformatics approaches.

Array comparative genomic hybridization and computational genome annotation in constitutional cytogenetics: suggesting candidate genes for novel submicroscopic chromosomal imbalance syndromes.

PubMed

Van Vooren, Steven; Coessens, Bert; De Moor, Bart; Moreau, Yves; Vermeesch, Joris R

2007-09-01

Genome-wide array comparative genomic hybridization screening is uncovering pathogenic submicroscopic chromosomal imbalances in patients with developmental disorders. In those patients, imbalances appear now to be scattered across the whole genome, and most patients carry different chromosomal anomalies. Screening patients with developmental disorders can be considered a forward functional genome screen. The imbalances pinpoint the location of genes that are involved in human development. Because most imbalances encompass regions harboring multiple genes, the challenge is to (1) identify those genes responsible for the specific phenotype and (2) disentangle the role of the different genes located in an imbalanced region. In this review, we discuss novel tools and relevant databases that have recently been developed to aid this gene discovery process. Identification of the functional relevance of genes will not only deepen our understanding of human development but will, in addition, aid in the data interpretation and improve genetic counseling.

Identification of cyanobacterial non-coding RNAs by comparative genome analysis.

PubMed

Axmann, Ilka M; Kensche, Philip; Vogel, Jörg; Kohl, Stefan; Herzel, Hanspeter; Hess, Wolfgang R

2005-01-01

Whole genome sequencing of marine cyanobacteria has revealed an unprecedented degree of genomic variation and streamlining. With a size of 1.66 megabase-pairs, Prochlorococcus sp. MED4 has the most compact of these genomes and it is enigmatic how the few identified regulatory proteins efficiently sustain the lifestyle of an ecologically successful marine microorganism. Small non-coding RNAs (ncRNAs) control a plethora of processes in eukaryotes as well as in bacteria; however, systematic searches for ncRNAs are still lacking for most eubacterial phyla outside the enterobacteria. Based on a computational prediction we show the presence of several ncRNAs (cyanobacterial functional RNA or Yfr) in several different cyanobacteria of the Prochlorococcus-Synechococcus lineage. Some ncRNA genes are present only in two or three of the four strains investigated, whereas the RNAs Yfr2 through Yfr5 are structurally highly related and are encoded by a rapidly evolving gene family as their genes exist in different copy numbers and at different sites in the four investigated genomes. One ncRNA, Yfr7, is present in at least seven other cyanobacteria. In addition, control elements for several ribosomal operons were predicted as well as riboswitches for thiamine pyrophosphate and cobalamin. This is the first genome-wide and systematic screen for ncRNAs in cyanobacteria. Several ncRNAs were both computationally predicted and their presence was biochemically verified. These RNAs may have regulatory functions and each shows a distinct phylogenetic distribution. Our approach can be applied to any group of microorganisms for which more than one total genome sequence is available for comparative analysis.

Computational pan-genomics: status, promises and challenges.

PubMed

2018-01-01

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains. © The Author 2016. Published by Oxford University Press.

A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies.

PubMed

Thakur, Shalabh; Guttman, David S

2016-06-30

Comparative analysis of whole genome sequence data from closely related prokaryotic species or strains is becoming an increasingly important and accessible approach for addressing both fundamental and applied biological questions. While there are number of excellent tools developed for performing this task, most scale poorly when faced with hundreds of genome sequences, and many require extensive manual curation. We have developed a de-novo genome analysis pipeline (DeNoGAP) for the automated, iterative and high-throughput analysis of data from comparative genomics projects involving hundreds of whole genome sequences. The pipeline is designed to perform reference-assisted and de novo gene prediction, homolog protein family assignment, ortholog prediction, functional annotation, and pan-genome analysis using a range of proven tools and databases. While most existing methods scale quadratically with the number of genomes since they rely on pairwise comparisons among predicted protein sequences, DeNoGAP scales linearly since the homology assignment is based on iteratively refined hidden Markov models. This iterative clustering strategy enables DeNoGAP to handle a very large number of genomes using minimal computational resources. Moreover, the modular structure of the pipeline permits easy updates as new analysis programs become available. DeNoGAP integrates bioinformatics tools and databases for comparative analysis of a large number of genomes. The pipeline offers tools and algorithms for annotation and analysis of completed and draft genome sequences. The pipeline is developed using Perl, BioPerl and SQLite on Ubuntu Linux version 12.04 LTS. Currently, the software package accompanies script for automated installation of necessary external programs on Ubuntu Linux; however, the pipeline should be also compatible with other Linux and Unix systems after necessary external programs are installed. DeNoGAP is freely available at https://sourceforge.net/projects/denogap/ .

Secure distributed genome analysis for GWAS and sequence comparison computation.

PubMed

Zhang, Yihua; Blanton, Marina; Almashaqbeh, Ghada

2015-01-01

The rapid increase in the availability and volume of genomic data makes significant advances in biomedical research possible, but sharing of genomic data poses challenges due to the highly sensitive nature of such data. To address the challenges, a competition for secure distributed processing of genomic data was organized by the iDASH research center. In this work we propose techniques for securing computation with real-life genomic data for minor allele frequency and chi-squared statistics computation, as well as distance computation between two genomic sequences, as specified by the iDASH competition tasks. We put forward novel optimizations, including a generalization of a version of mergesort, which might be of independent interest. We provide implementation results of our techniques based on secret sharing that demonstrate practicality of the suggested protocols and also report on performance improvements due to our optimization techniques. This work describes our techniques, findings, and experimental results developed and obtained as part of iDASH 2015 research competition to secure real-life genomic computations and shows feasibility of securely computing with genomic data in practice.

Secure distributed genome analysis for GWAS and sequence comparison computation

PubMed Central

2015-01-01

Background The rapid increase in the availability and volume of genomic data makes significant advances in biomedical research possible, but sharing of genomic data poses challenges due to the highly sensitive nature of such data. To address the challenges, a competition for secure distributed processing of genomic data was organized by the iDASH research center. Methods In this work we propose techniques for securing computation with real-life genomic data for minor allele frequency and chi-squared statistics computation, as well as distance computation between two genomic sequences, as specified by the iDASH competition tasks. We put forward novel optimizations, including a generalization of a version of mergesort, which might be of independent interest. Results We provide implementation results of our techniques based on secret sharing that demonstrate practicality of the suggested protocols and also report on performance improvements due to our optimization techniques. Conclusions This work describes our techniques, findings, and experimental results developed and obtained as part of iDASH 2015 research competition to secure real-life genomic computations and shows feasibility of securely computing with genomic data in practice. PMID:26733307

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.

Breaking Lander-Waterman’s Coverage Bound

PubMed Central

Nashta-ali, Damoun; Motahari, Seyed Abolfazl; Hosseinkhalaj, Babak

2016-01-01

Lander-Waterman’s coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collector’s problem which proves that for such genome, the total number of bases to be sequenced should be O(G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Waterman’s result and prove that by combining the sequencing and computing processes, one can re-sequence the whole genome with as low as O(G) sequenced bases in total. Our approach also dramatically reduces the required computational power for the combined process. Simulation results are performed on real genomes with different sequencing error rates. The results support our theory predicting the log G improvement on coverage bound and corresponding reduction in the total number of bases required to be sequenced. PMID:27806058

Comparing de novo genome assembly: the long and short of it.

PubMed

Narzisi, Giuseppe; Mishra, Bud

2011-04-29

Recent advances in DNA sequencing technology and their focal role in Genome Wide Association Studies (GWAS) have rekindled a growing interest in the whole-genome sequence assembly (WGSA) problem, thereby, inundating the field with a plethora of new formalizations, algorithms, heuristics and implementations. And yet, scant attention has been paid to comparative assessments of these assemblers' quality and accuracy. No commonly accepted and standardized method for comparison exists yet. Even worse, widely used metrics to compare the assembled sequences emphasize only size, poorly capturing the contig quality and accuracy. This paper addresses these concerns: it highlights common anomalies in assembly accuracy through a rigorous study of several assemblers, compared under both standard metrics (N50, coverage, contig sizes, etc.) as well as a more comprehensive metric (Feature-Response Curves, FRC) that is introduced here; FRC transparently captures the trade-offs between contigs' quality against their sizes. For this purpose, most of the publicly available major sequence assemblers--both for low-coverage long (Sanger) and high-coverage short (Illumina) reads technologies--are compared. These assemblers are applied to microbial (Escherichia coli, Brucella, Wolbachia, Staphylococcus, Helicobacter) and partial human genome sequences (Chr. Y), using sequence reads of various read-lengths, coverages, accuracies, and with and without mate-pairs. It is hoped that, based on these evaluations, computational biologists will identify innovative sequence assembly paradigms, bioinformaticists will determine promising approaches for developing "next-generation" assemblers, and biotechnologists will formulate more meaningful design desiderata for sequencing technology platforms. A new software tool for computing the FRC metric has been developed and is available through the AMOS open-source consortium.

Enhanced Methods for Local Ancestry Assignment in Sequenced Admixed Individuals

PubMed Central

Brown, Robert; Pasaniuc, Bogdan

2014-01-01

Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs. PMID:24743331

SvABA: genome-wide detection of structural variants and indels by local assembly.

PubMed

Wala, Jeremiah A; Bandopadhayay, Pratiti; Greenwald, Noah F; O'Rourke, Ryan; Sharpe, Ted; Stewart, Chip; Schumacher, Steve; Li, Yilong; Weischenfeldt, Joachim; Yao, Xiaotong; Nusbaum, Chad; Campbell, Peter; Getz, Gad; Meyerson, Matthew; Zhang, Cheng-Zhong; Imielinski, Marcin; Beroukhim, Rameen

2018-04-01

Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50-300 bp) SVs. © 2018 Wala et al.; Published by Cold Spring Harbor Laboratory Press.

An Exact Algorithm to Compute the Double-Cut-and-Join Distance for Genomes with Duplicate Genes.

PubMed

Shao, Mingfu; Lin, Yu; Moret, Bernard M E

2015-05-01

Computing the edit distance between two genomes is a basic problem in the study of genome evolution. The double-cut-and-join (DCJ) model has formed the basis for most algorithmic research on rearrangements over the last few years. The edit distance under the DCJ model can be computed in linear time for genomes without duplicate genes, while the problem becomes NP-hard in the presence of duplicate genes. In this article, we propose an integer linear programming (ILP) formulation to compute the DCJ distance between two genomes with duplicate genes. We also provide an efficient preprocessing approach to simplify the ILP formulation while preserving optimality. Comparison on simulated genomes demonstrates that our method outperforms MSOAR in computing the edit distance, especially when the genomes contain long duplicated segments. We also apply our method to assign orthologous gene pairs among human, mouse, and rat genomes, where once again our method outperforms MSOAR.

Genomes of diverse isolates of the marine cyanobacterium Prochlorococcus

PubMed Central

Biller, Steven J.; Berube, Paul M.; Berta-Thompson, Jessie W.; Kelly, Libusha; Roggensack, Sara E.; Awad, Lana; Roache-Johnson, Kathryn H.; Ding, Huiming; Giovannoni, Stephen J.; Rocap, Gabrielle; Moore, Lisa R.; Chisholm, Sallie W.

2014-01-01

The marine cyanobacterium Prochlorococcus is the numerically dominant photosynthetic organism in the oligotrophic oceans, and a model system in marine microbial ecology. Here we report 27 new whole genome sequences (2 complete and closed; 25 of draft quality) of cultured isolates, representing five major phylogenetic clades of Prochlorococcus. The sequenced strains were isolated from diverse regions of the oceans, facilitating studies of the drivers of microbial diversity—both in the lab and in the field. To improve the utility of these genomes for comparative genomics, we also define pre-computed clusters of orthologous groups of proteins (COGs), indicating how genes are distributed among these and other publicly available Prochlorococcus genomes. These data represent a significant expansion of Prochlorococcus reference genomes that are useful for numerous applications in microbial ecology, evolution and oceanography. PMID:25977791

Calculation of 3D genome structures for comparison of chromosome conformation capture experiments with microscopy: An evaluation of single-cell Hi-C protocols.

PubMed

Lando, David; Stevens, Tim J; Basu, Srinjan; Laue, Ernest D

2018-01-01

Single-cell chromosome conformation capture approaches are revealing the extent of cell-to-cell variability in the organization and packaging of genomes. These single-cell methods, unlike their multi-cell counterparts, allow straightforward computation of realistic chromosome conformations that may be compared and combined with other, independent, techniques to study 3D structure. Here we discuss how single-cell Hi-C and subsequent 3D genome structure determination allows comparison with data from microscopy. We then carry out a systematic evaluation of recently published single-cell Hi-C datasets to establish a computational approach for the evaluation of single-cell Hi-C protocols. We show that the calculation of genome structures provides a useful tool for assessing the quality of single-cell Hi-C data because it requires a self-consistent network of interactions, relating to the underlying 3D conformation, with few errors, as well as sufficient longer-range cis- and trans-chromosomal contacts.

MicroScope in 2017: an expanding and evolving integrated resource for community expertise of microbial genomes

PubMed Central

Vallenet, David; Calteau, Alexandra; Cruveiller, Stéphane; Gachet, Mathieu; Lajus, Aurélie; Josso, Adrien; Mercier, Jonathan; Renaux, Alexandre; Rollin, Johan; Rouy, Zoe; Roche, David; Scarpelli, Claude; Médigue, Claudine

2017-01-01

The annotation of genomes from NGS platforms needs to be automated and fully integrated. However, maintaining consistency and accuracy in genome annotation is a challenging problem because millions of protein database entries are not assigned reliable functions. This shortcoming limits the knowledge that can be extracted from genomes and metabolic models. Launched in 2005, 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. Effective comparative analysis requires a consistent and complete view of biological data, and therefore, support for reviewing the quality of functional annotation is critical. MicroScope allows users to analyze microbial (meta)genomes together with post-genomic experiment results if any (i.e. transcriptomics, re-sequencing of evolved strains, mutant collections, phenotype data). It combines tools and graphical interfaces to analyze genomes and to perform the expert curation of gene functions in a comparative context. Starting with a short overview of the MicroScope system, this paper focuses on some major improvements of the Web interface, mainly for the submission of genomic data and on original tools and pipelines that have been developed and integrated in the platform: computation of pan-genomes and prediction of biosynthetic gene clusters. Today the resource contains data for more than 6000 microbial genomes, and among the 2700 personal accounts (65% of which are now from foreign countries), 14% of the users are performing expert annotations, on at least a weekly basis, contributing to improve the quality of microbial genome annotations. PMID:27899624

dCITE: Measuring Necessary Cladistic Information Can Help You Reduce Polytomy Artefacts in Trees.

PubMed

Wise, Michael J

2016-01-01

Biologists regularly create phylogenetic trees to better understand the evolutionary origins of their species of interest, and often use genomes as their data source. However, as more and more incomplete genomes are published, in many cases it may not be possible to compute genome-based phylogenetic trees due to large gaps in the assembled sequences. In addition, comparison of complete genomes may not even be desirable due to the presence of horizontally acquired and homologous genes. A decision must therefore be made about which gene, or gene combinations, should be used to compute a tree. Deflated Cladistic Information based on Total Entropy (dCITE) is proposed as an easily computed metric for measuring the cladistic information in multiple sequence alignments representing a range of taxa, without the need to first compute the corresponding trees. dCITE scores can be used to rank candidate genes or decide whether input sequences provide insufficient cladistic information, making artefactual polytomies more likely. The dCITE method can be applied to protein, nucleotide or encoded phenotypic data, so can be used to select which data-type is most appropriate, given the choice. In a series of experiments the dCITE method was compared with related measures. Then, as a practical demonstration, the ideas developed in the paper were applied to a dataset representing species from the order Campylobacterales; trees based on sequence combinations, selected on the basis of their dCITE scores, were compared with a tree constructed to mimic Multi-Locus Sequence Typing (MLST) combinations of fragments. We see that the greater the dCITE score the more likely it is that the computed phylogenetic tree will be free of artefactual polytomies. Secondly, cladistic information saturates, beyond which little additional cladistic information can be obtained by adding additional sequences. Finally, sequences with high cladistic information produce more consistent trees for the same taxa.

dCITE: Measuring Necessary Cladistic Information Can Help You Reduce Polytomy Artefacts in Trees

PubMed Central

2016-01-01

Biologists regularly create phylogenetic trees to better understand the evolutionary origins of their species of interest, and often use genomes as their data source. However, as more and more incomplete genomes are published, in many cases it may not be possible to compute genome-based phylogenetic trees due to large gaps in the assembled sequences. In addition, comparison of complete genomes may not even be desirable due to the presence of horizontally acquired and homologous genes. A decision must therefore be made about which gene, or gene combinations, should be used to compute a tree. Deflated Cladistic Information based on Total Entropy (dCITE) is proposed as an easily computed metric for measuring the cladistic information in multiple sequence alignments representing a range of taxa, without the need to first compute the corresponding trees. dCITE scores can be used to rank candidate genes or decide whether input sequences provide insufficient cladistic information, making artefactual polytomies more likely. The dCITE method can be applied to protein, nucleotide or encoded phenotypic data, so can be used to select which data-type is most appropriate, given the choice. In a series of experiments the dCITE method was compared with related measures. Then, as a practical demonstration, the ideas developed in the paper were applied to a dataset representing species from the order Campylobacterales; trees based on sequence combinations, selected on the basis of their dCITE scores, were compared with a tree constructed to mimic Multi-Locus Sequence Typing (MLST) combinations of fragments. We see that the greater the dCITE score the more likely it is that the computed phylogenetic tree will be free of artefactual polytomies. Secondly, cladistic information saturates, beyond which little additional cladistic information can be obtained by adding additional sequences. Finally, sequences with high cladistic information produce more consistent trees for the same taxa. PMID:27898695

GenomeGems: evaluation of genetic variability from deep sequencing data

PubMed Central

2012-01-01

Background Detection of disease-causing mutations using Deep Sequencing technologies possesses great challenges. In particular, organizing the great amount of sequences generated so that mutations, which might possibly be biologically relevant, are easily identified is a difficult task. Yet, for this assignment only limited automatic accessible tools exist. Findings We developed GenomeGems to gap this need by enabling the user to view and compare Single Nucleotide Polymorphisms (SNPs) from multiple datasets and to load the data onto the UCSC Genome Browser for an expanded and familiar visualization. As such, via automatic, clear and accessible presentation of processed Deep Sequencing data, our tool aims to facilitate ranking of genomic SNP calling. GenomeGems runs on a local Personal Computer (PC) and is freely available at http://www.tau.ac.il/~nshomron/GenomeGems. Conclusions GenomeGems enables researchers to identify potential disease-causing SNPs in an efficient manner. This enables rapid turnover of information and leads to further experimental SNP validation. The tool allows the user to compare and visualize SNPs from multiple experiments and to easily load SNP data onto the UCSC Genome browser for further detailed information. PMID:22748151

Finding approximate gene clusters with Gecko 3.

PubMed

Winter, Sascha; Jahn, Katharina; Wehner, Stefanie; Kuchenbecker, Leon; Marz, Manja; Stoye, Jens; Böcker, Sebastian

2016-11-16

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in <40 min. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Primer on High-Throughput Computing for Genomic Selection

PubMed Central

Wu, Xiao-Lin; Beissinger, Timothy M.; Bauck, Stewart; Woodward, Brent; Rosa, Guilherme J. M.; Weigel, Kent A.; Gatti, Natalia de Leon; Gianola, Daniel

2011-01-01

High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl, and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general-purpose computation on a graphics processing unit provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin–Madison, which can be leveraged for genomic selection, in terms of central processing unit capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general-purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of marker panels to realized genetic gain). Eventually, HTC may change our view of data analysis as well as decision-making in the post-genomic era of selection programs in animals and plants, or in the study of complex diseases in humans. PMID:22303303

A whole-genome shotgun approach for assembling and anchoring the hexaploid bread wheat genome

DOE PAGES

Chapman, Jarrod A.; Mascher, Martin; Buluc, Aydin; ...

2015-01-31

We report that polyploid species have long been thought to be recalcitrant to whole-genome assembly. By combining high-throughput sequencing, recent developments in parallel computing, and genetic mapping, we derive, de novo, a sequence assembly representing 9.1 Gbp of the highly repetitive 16 Gbp genome of hexaploid wheat, Triticum aestivum, and assign 7.1 Gb of this assembly to chromosomal locations. The genome representation and accuracy of our assembly is comparable or even exceeds that of a chromosome-by-chromosome shotgun assembly. Our assembly and mapping strategy uses only short read sequencing technology and is applicable to any species where it is possible tomore » construct a mapping population.« less

A whole-genome shotgun approach for assembling and anchoring the hexaploid bread wheat genome

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

Chapman, Jarrod A.; Mascher, Martin; Buluc, Aydin

We report that polyploid species have long been thought to be recalcitrant to whole-genome assembly. By combining high-throughput sequencing, recent developments in parallel computing, and genetic mapping, we derive, de novo, a sequence assembly representing 9.1 Gbp of the highly repetitive 16 Gbp genome of hexaploid wheat, Triticum aestivum, and assign 7.1 Gb of this assembly to chromosomal locations. The genome representation and accuracy of our assembly is comparable or even exceeds that of a chromosome-by-chromosome shotgun assembly. Our assembly and mapping strategy uses only short read sequencing technology and is applicable to any species where it is possible tomore » construct a mapping population.« less

Overview of the creative genome: effects of genome structure and sequence on the generation of variation and evolution.

PubMed

Caporale, Lynn Helena

2012-09-01

This overview of a special issue of Annals of the New York Academy of Sciences discusses uneven distribution of distinct types of variation across the genome, the dependence of specific types of variation upon distinct classes of DNA sequences and/or the induction of specific proteins, the circumstances in which distinct variation-generating systems are activated, and the implications of this work for our understanding of evolution and of cancer. Also discussed is the value of non text-based computational methods for analyzing information carried by DNA, early insights into organizational frameworks that affect genome behavior, and implications of this work for comparative genomics. © 2012 New York Academy of Sciences.

Efficient Server-Aided Secure Two-Party Function Evaluation with Applications to Genomic Computation

DTIC Science & Technology

2016-07-14

of the important properties of secure computation . In particular, it is known that full fairness cannot be achieved in the case of two-party com...Jakobsen, J. Nielsen, and C. Orlandi. A framework for outsourcing of secure computation . In ACM Workshop on Cloud Computing Security (CCSW), pages...Function Evaluation with Applications to Genomic Computation Abstract: Computation based on genomic data is becoming increasingly popular today, be it

Comparative Modeling of Proteins: A Method for Engaging Students' Interest in Bioinformatics Tools

ERIC Educational Resources Information Center

Badotti, Fernanda; Barbosa, Alan Sales; Reis, André Luiz Martins; do Valle, Ítalo Faria; Ambrósio, Lara; Bitar, Mainá

2014-01-01

The huge increase in data being produced in the genomic era has produced a need to incorporate computers into the research process. Sequence generation, its subsequent storage, interpretation, and analysis are now entirely computer-dependent tasks. Universities from all over the world have been challenged to seek a way of encouraging students to…

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

PubMed Central

Schultz, André; Qutub, Amina A.

2016-01-01

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation. PMID:26942765

Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome.

PubMed

Kawaguchi, Risa; Kiryu, Hisanori

2016-05-06

RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems. Our novel software, "ParasoR", is designed to run on a computer cluster and enables the exact computation of various structural features of long RNA sequences under the constraint of maximal base-pairing distance. ParasoR divides dynamic programming (DP) matrices into smaller pieces, such that each piece can be computed by a separate computer node without losing the connectivity information between the pieces. ParasoR directly computes the ratios of DP variables to avoid the reduction of numerical precision caused by the cancellation of a large number of Boltzmann factors. The structural preferences of mRNAs computed by ParasoR shows a high concordance with those determined by high-throughput sequencing analyses. Using ParasoR, we investigated the global structural preferences of transcribed regions in the human genome. A genome-wide folding simulation indicated that transcribed regions are significantly more structural than intergenic regions after removing repeat sequences and k-mer frequency bias. In particular, we observed a highly significant preference for base pairing over entire intronic regions as compared to their antisense sequences, as well as to intergenic regions. A comparison between pre-mRNAs and mRNAs showed that coding regions become more accessible after splicing, indicating constraints for translational efficiency. Such changes are correlated with gene expression levels, as well as GC content, and are enriched among genes associated with cytoskeleton and kinase functions. We have shown that ParasoR is very useful for analyzing the structural properties of long RNA sequences such as mRNAs, pre-mRNAs, and long non-coding RNAs whose lengths can be more than a million bases in the human genome. In our analyses, transcribed regions including introns are indicated to be subject to various types of structural constraints that cannot be explained from simple sequence composition biases. ParasoR is freely available at https://github.com/carushi/ParasoR .

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes.

PubMed

Zhu, Huaiqiu; Hu, Gang-Qing; Yang, Yi-Fan; Wang, Jin; She, Zhen-Su

2007-03-16

Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

Integrated Approach to Reconstruction of Microbial Regulatory Networks

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

Rodionov, Dmitry A; Novichkov, Pavel S

2013-11-04

This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated inmore » RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.« less

Comparative Genomics of Oral Isolates of Streptococcus mutans by in silico Genome Subtraction Does Not Reveal Accessory DNA Associated with Severe Early Childhood Caries

PubMed Central

Argimón, Silvia; Konganti, Kranti; Chen, Hao; Alekseyenko, Alexander V.; Brown, Stuart; Caufield, Page W.

2014-01-01

Comparative genomics is a popular method for the identification of microbial virulence determinants, especially since the sequencing of a large number of whole bacterial genomes from pathogenic and non-pathogenic strains has become relatively inexpensive. The bioinformatics pipelines for comparative genomics usually include gene prediction and annotation and can require significant computer power. To circumvent this, we developed a rapid method for genome-scale in silico subtractive hybridization, based on blastn and independent of feature identification and annotation. Whole genome comparisons by in silico genome subtraction were performed to identify genetic loci specific to Streptococcus mutans strains associated with severe early childhood caries (S-ECC), compared to strains isolated from caries-free (CF) children. The genome similarity of the 20 S. mutans strains included in this study, calculated by Simrank k-mer sharing, ranged from 79.5 to 90.9%, confirming this is a genetically heterogeneous group of strains. We identified strain-specific genetic elements in 19 strains, with sizes ranging from 200 bp to 39 kb. These elements contained protein-coding regions with functions mostly associated with mobile DNA. We did not, however, identify any genetic loci consistently associated with dental caries, i.e., shared by all the S-ECC strains and absent in the CF strains. Conversely, we did not identify any genetic loci specific with the healthy group. Comparison of previously published genomes from pathogenic and carriage strains of Neisseria meningitidis with our in silico genome subtraction yielded the same set of genes specific to the pathogenic strains, thus validating our method. Our results suggest that S. mutans strains derived from caries active or caries free dentitions cannot be differentiated based on the presence or absence of specific genetic elements. Our in silico genome subtraction method is available as the Microbial Genome Comparison (MGC) tool, with a user-friendly JAVA graphical interface. PMID:24291226

Comparing genomes with rearrangements and segmental duplications.

PubMed

Shao, Mingfu; Moret, Bernard M E

2015-06-15

Large-scale evolutionary events such as genomic rearrange.ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational problem is to infer these events in the evolutionary history for given modern genomes, a task for which many algorithms have been proposed under various constraints. Algorithms that can handle both rearrangements and content-modifying events such as duplications and losses remain few and limited in their applicability. We study the comparison of two genomes under a model including general rearrangements (through double-cut-and-join) and segmental duplications. We formulate the comparison as an optimization problem and describe an exact algorithm to solve it by using an integer linear program. We also devise a sufficient condition and an efficient algorithm to identify optimal substructures, which can simplify the problem while preserving optimality. Using the optimal substructures with the integer linear program (ILP) formulation yields a practical and exact algorithm to solve the problem. We then apply our algorithm to assign in-paralogs and orthologs (a necessary step in handling duplications) and compare its performance with that of the state-of-the-art method MSOAR, using both simulations and real data. On simulated datasets, our method outperforms MSOAR by a significant margin, and on five well-annotated species, MSOAR achieves high accuracy, yet our method performs slightly better on each of the 10 pairwise comparisons. http://lcbb.epfl.ch/softwares/coser. © The Author 2015. Published by Oxford University Press.

MicroScope in 2017: an expanding and evolving integrated resource for community expertise of microbial genomes.

PubMed

Vallenet, David; Calteau, Alexandra; Cruveiller, Stéphane; Gachet, Mathieu; Lajus, Aurélie; Josso, Adrien; Mercier, Jonathan; Renaux, Alexandre; Rollin, Johan; Rouy, Zoe; Roche, David; Scarpelli, Claude; Médigue, Claudine

2017-01-04

The annotation of genomes from NGS platforms needs to be automated and fully integrated. However, maintaining consistency and accuracy in genome annotation is a challenging problem because millions of protein database entries are not assigned reliable functions. This shortcoming limits the knowledge that can be extracted from genomes and metabolic models. Launched in 2005, 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. Effective comparative analysis requires a consistent and complete view of biological data, and therefore, support for reviewing the quality of functional annotation is critical. MicroScope allows users to analyze microbial (meta)genomes together with post-genomic experiment results if any (i.e. transcriptomics, re-sequencing of evolved strains, mutant collections, phenotype data). It combines tools and graphical interfaces to analyze genomes and to perform the expert curation of gene functions in a comparative context. Starting with a short overview of the MicroScope system, this paper focuses on some major improvements of the Web interface, mainly for the submission of genomic data and on original tools and pipelines that have been developed and integrated in the platform: computation of pan-genomes and prediction of biosynthetic gene clusters. Today the resource contains data for more than 6000 microbial genomes, and among the 2700 personal accounts (65% of which are now from foreign countries), 14% of the users are performing expert annotations, on at least a weekly basis, contributing to improve the quality of microbial genome annotations. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Comparative genomic analysis by microbial COGs self-attraction rate.

PubMed

Santoni, Daniele; Romano-Spica, Vincenzo

2009-06-21

Whole genome analysis provides new perspectives to determine phylogenetic relationships among microorganisms. The availability of whole nucleotide sequences allows different levels of comparison among genomes by several approaches. In this work, self-attraction rates were considered for each cluster of orthologous groups of proteins (COGs) class in order to analyse gene aggregation levels in physical maps. Phylogenetic relationships among microorganisms were obtained by comparing self-attraction coefficients. Eighteen-dimensional vectors were computed for a set of 168 completely sequenced microbial genomes (19 archea, 149 bacteria). The components of the vector represent the aggregation rate of the genes belonging to each of 18 COGs classes. Genes involved in nonessential functions or related to environmental conditions showed the highest aggregation rates. On the contrary genes involved in basic cellular tasks showed a more uniform distribution along the genome, except for translation genes. Self-attraction clustering approach allowed classification of Proteobacteria, Bacilli and other species belonging to Firmicutes. Rearrangement and Lateral Gene Transfer events may influence divergences from classical taxonomy. Each set of COG classes' aggregation values represents an intrinsic property of the microbial genome. This novel approach provides a new point of view for whole genome analysis and bacterial characterization.

Development of self-compressing BLSOM for comprehensive analysis of big sequence data.

PubMed

Kikuchi, Akihito; Ikemura, Toshimichi; Abe, Takashi

2015-01-01

With the remarkable increase in genomic sequence data from various organisms, novel tools are needed for comprehensive analyses of available big sequence data. We previously developed a Batch-Learning Self-Organizing Map (BLSOM), which can cluster genomic fragment sequences according to phylotype solely dependent on oligonucleotide composition and applied to genome and metagenomic studies. BLSOM is suitable for high-performance parallel-computing and can analyze big data simultaneously, but a large-scale BLSOM needs a large computational resource. We have developed Self-Compressing BLSOM (SC-BLSOM) for reduction of computation time, which allows us to carry out comprehensive analysis of big sequence data without the use of high-performance supercomputers. The strategy of SC-BLSOM is to hierarchically construct BLSOMs according to data class, such as phylotype. The first-layer BLSOM was constructed with each of the divided input data pieces that represents the data subclass, such as phylotype division, resulting in compression of the number of data pieces. The second BLSOM was constructed with a total of weight vectors obtained in the first-layer BLSOMs. We compared SC-BLSOM with the conventional BLSOM by analyzing bacterial genome sequences. SC-BLSOM could be constructed faster than BLSOM and cluster the sequences according to phylotype with high accuracy, showing the method's suitability for efficient knowledge discovery from big sequence data.

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

GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes.

PubMed

van den Broek, Evert; van Lieshout, Stef; Rausch, Christian; Ylstra, Bauke; van de Wiel, Mark A; Meijer, Gerrit A; Fijneman, Remond J A; Abeln, Sanne

2016-01-01

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. 'GeneBreak' is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, 'GeneBreak' collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, 'GeneBreak', is implemented in R ( www.cran.r-project.org ) and is available from Bioconductor ( www.bioconductor.org/packages/release/bioc/html/GeneBreak.html ).

A fast indirect method to compute functions of genomic relationships concerning genotyped and ungenotyped individuals, for diversity management.

PubMed

Colleau, Jean-Jacques; Palhière, Isabelle; Rodríguez-Ramilo, Silvia T; Legarra, Andres

2017-12-01

Pedigree-based management of genetic diversity in populations, e.g., using optimal contributions, involves computation of the [Formula: see text] type yielding elements (relationships) or functions (usually averages) of relationship matrices. For pedigree-based relationships [Formula: see text], a very efficient method exists. When all the individuals of interest are genotyped, genomic management can be addressed using the genomic relationship matrix [Formula: see text]; however, to date, the computational problem of efficiently computing [Formula: see text] has not been well studied. When some individuals of interest are not genotyped, genomic management should consider the relationship matrix [Formula: see text] that combines genotyped and ungenotyped individuals; however, direct computation of [Formula: see text] is computationally very demanding, because construction of a possibly huge matrix is required. Our work presents efficient ways of computing [Formula: see text] and [Formula: see text], with applications on real data from dairy sheep and dairy goat breeding schemes. For genomic relationships, an efficient indirect computation with quadratic instead of cubic cost is [Formula: see text], where Z is a matrix relating animals to genotypes. For the relationship matrix [Formula: see text], we propose an indirect method based on the difference between vectors [Formula: see text], which involves computation of [Formula: see text] and of products such as [Formula: see text] and [Formula: see text], where [Formula: see text] is a working vector derived from [Formula: see text]. The latter computation is the most demanding but can be done using sparse Cholesky decompositions of matrix [Formula: see text], which allows handling very large genomic and pedigree data files. Studies based on simulations reported in the literature show that the trends of average relationships in [Formula: see text] and [Formula: see text] differ as genomic selection proceeds. When selection is based on genomic relationships but management is based on pedigree data, the true genetic diversity is overestimated. However, our tests on real data from sheep and goat obtained before genomic selection started do not show this. We present efficient methods to compute elements and statistics of the genomic relationships [Formula: see text] and of matrix [Formula: see text] that combines ungenotyped and genotyped individuals. These methods should be useful to monitor and handle genomic diversity.

Simple sequence repeats in Escherichia coli: abundance, distribution, composition, and polymorphism.

PubMed

Gur-Arie, R; Cohen, C J; Eitan, Y; Shelef, L; Hallerman, E M; Kashi, Y

2000-01-01

Computer-based genome-wide screening of the DNA sequence of Escherichia coli strain K12 revealed tens of thousands of tandem simple sequence repeat (SSR) tracts, with motifs ranging from 1 to 6 nucleotides. SSRs were well distributed throughout the genome. Mononucleotide SSRs were over-represented in noncoding regions and under-represented in open reading frames (ORFs). Nucleotide composition of mono- and dinucleotide SSRs, both in ORFs and in noncoding regions, differed from that of the genomic region in which they occurred, with 93% of all mononucleotide SSRs proving to be of A or T. Computer-based analysis of the fine position of every SSR locus in the noncoding portion of the genome relative to downstream ORFs showed SSRs located in areas that could affect gene regulation. DNA sequences at 14 arbitrarily chosen SSR tracts were compared among E. coli strains. Polymorphisms of SSR copy number were observed at four of seven mononucleotide SSR tracts screened, with all polymorphisms occurring in noncoding regions. SSR polymorphism could prove important as a genome-wide source of variation, both for practical applications (including rapid detection, strain identification, and detection of loci affecting key phenotypes) and for evolutionary adaptation of microbes.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE PAGES

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; ...

2016-11-24

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

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

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Whole genome sequencing of the fish pathogen Francisella noatunensis subsp. orientalis Toba04 gives novel insights into Francisella evolution and pathogenecity

PubMed Central

2012-01-01

Background Francisella is a genus of gram-negative bacterium highly virulent in fishes and human where F. tularensis is causing the serious disease tularaemia in human. Recently Francisella species have been reported to cause mortality in aquaculture species like Atlantic cod and tilapia. We have completed the sequencing and draft assembly of the Francisella noatunensis subsp. orientalisToba04 strain isolated from farmed Tilapia. Compared to other available Francisella genomes, it is most similar to the genome of Francisella philomiragia subsp. philomiragia, a free-living bacterium not virulent to human. Results The genome is rearranged compared to the available Francisella genomes even though we found no IS-elements in the genome. Nearly 16% percent of the predicted ORFs are pseudogenes. Computational pathway analysis indicates that a number of the metabolic pathways are disrupted due to pseudogenes. Comparing the novel genome with other available Francisella genomes, we found around 2.5% of unique genes present in Francisella noatunensis subsp. orientalis Toba04 and a list of genes uniquely present in the human-pathogenic Francisella subspecies. Most of these genes might have transferred from bacterial species through horizontal gene transfer. Comparative analysis between human and fish pathogen also provide insights into genes responsible for pathogenecity. Our analysis of pseudogenes indicates that the evolution of Francisella subspecies’s pseudogenes from Tilapia is old with large number of pseudogenes having more than one inactivating mutation. Conclusions The fish pathogen has lost non-essential genes some time ago. Evolutionary analysis of the Francisella genomes, strongly suggests that human and fish pathogenic Francisella species have evolved independently from free-living metabolically competent Francisella species. These findings will contribute to understanding the evolution of Francisella species and pathogenesis. PMID:23131096

Observation of quantum criticality with ultracold atoms in optical lattices

NASA Astrophysics Data System (ADS)

Zhang, Xibo

As biological problems are becoming more complex and data growing at a rate much faster than that of computer hardware, new and faster algorithms are required. This dissertation investigates computational problems arising in two of the fields: comparative genomics and epigenomics, and employs a variety of computational techniques to address the problems. One fundamental question in the studies of chromosome evolution is whether the rearrangement breakpoints are happening at random positions or along certain hotspots. We investigate the breakpoint reuse phenomenon, and show the analyses that support the more recently proposed fragile breakage model as opposed to the conventional random breakage models for chromosome evolution. The identification of syntenic regions between chromosomes forms the basis for studies of genome architectures, comparative genomics, and evolutionary genomics. The previous synteny block reconstruction algorithms could not be scaled to a large number of mammalian genomes being sequenced; neither did they address the issue of generating non-overlapping synteny blocks suitable for analyzing rearrangements and evolutionary history of large-scale duplications prevalent in plant genomes. We present a new unified synteny block generation algorithm based on A-Bruijn graph framework that overcomes these shortcomings. In the epigenome sequencing, a sample may contain a mixture of epigenomes and there is a need to resolve the distinct methylation patterns from the mixture. Many sequencing applications, such as haplotype inference for diploid or polyploid genomes, and metagenomic sequencing, share the similar objective: to infer a set of distinct assemblies from reads that are sequenced from a heterogeneous sample and subsequently aligned to a reference genome. We model the problem from both a combinatorial and a statistical angles. First, we describe a theoretical framework. A linear-time algorithm is then given to resolve a minimum number of assemblies that are consistent with all reads, substantially improving on previous algorithms. An efficient algorithm is also described to determine a set of assemblies that is consistent with a maximum subset of the reads, a previously untreated problem. We then prove that allowing nested reads or permitting mismatches between reads and their assemblies renders these problems NP-hard. Second, we describe a mixture model-based approach, and applied the model for the detection of allele-specific methylations.

Genomic cloud computing: legal and ethical points to consider

PubMed Central

Dove, Edward S; Joly, Yann; Tassé, Anne-Marie; Burton, Paul; Chisholm, Rex; Fortier, Isabel; Goodwin, Pat; Harris, Jennifer; Hveem, Kristian; Kaye, Jane; Kent, Alistair; Knoppers, Bartha Maria; Lindpaintner, Klaus; Little, Julian; Riegman, Peter; Ripatti, Samuli; Stolk, Ronald; Bobrow, Martin; Cambon-Thomsen, Anne; Dressler, Lynn; Joly, Yann; Kato, Kazuto; Knoppers, Bartha Maria; Rodriguez, Laura Lyman; McPherson, Treasa; Nicolás, Pilar; Ouellette, Francis; Romeo-Casabona, Carlos; Sarin, Rajiv; Wallace, Susan; Wiesner, Georgia; Wilson, Julia; Zeps, Nikolajs; Simkevitz, Howard; De Rienzo, Assunta; Knoppers, Bartha M

2015-01-01

The biggest challenge in twenty-first century data-intensive genomic science, is developing vast computer infrastructure and advanced software tools to perform comprehensive analyses of genomic data sets for biomedical research and clinical practice. Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems. Although cloud computing provides several benefits such as lower costs and greater efficiency, it also raises legal and ethical issues. In this article, we discuss three key ‘points to consider' (data control; data security, confidentiality and transfer; and accountability) based on a preliminary review of several publicly available cloud service providers' Terms of Service. These ‘points to consider' should be borne in mind by genomic research organizations when negotiating legal arrangements to store genomic data on a large commercial cloud service provider's servers. Diligent genomic cloud computing means leveraging security standards and evaluation processes as a means to protect data and entails many of the same good practices that researchers should always consider in securing their local infrastructure. PMID:25248396

Genomic cloud computing: legal and ethical points to consider.

PubMed

Dove, Edward S; Joly, Yann; Tassé, Anne-Marie; Knoppers, Bartha M

2015-10-01

The biggest challenge in twenty-first century data-intensive genomic science, is developing vast computer infrastructure and advanced software tools to perform comprehensive analyses of genomic data sets for biomedical research and clinical practice. Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems. Although cloud computing provides several benefits such as lower costs and greater efficiency, it also raises legal and ethical issues. In this article, we discuss three key 'points to consider' (data control; data security, confidentiality and transfer; and accountability) based on a preliminary review of several publicly available cloud service providers' Terms of Service. These 'points to consider' should be borne in mind by genomic research organizations when negotiating legal arrangements to store genomic data on a large commercial cloud service provider's servers. Diligent genomic cloud computing means leveraging security standards and evaluation processes as a means to protect data and entails many of the same good practices that researchers should always consider in securing their local infrastructure.

Mobilomics in Saccharomyces cerevisiae strains

PubMed Central

2013-01-01

Background Mobile Genetic Elements (MGEs) are selfish DNA integrated in the genomes. Their detection is mainly based on consensus–like searches by scanning the investigated genome against the sequence of an already identified MGE. Mobilomics aims at discovering all the MGEs in a genome and understanding their dynamic behavior: The data for this kind of investigation can be provided by comparative genomics of closely related organisms. The amount of data thus involved requires a strong computational effort, which should be alleviated. Results Our approach proposes to exploit the high similarity among homologous chromosomes of different strains of the same species, following a progressive comparative genomics philosophy. We introduce a software tool based on our new fast algorithm, called regender, which is able to identify the conserved regions between chromosomes. Our case study is represented by a unique recently available dataset of 39 different strains of S.cerevisiae, which regender is able to compare in few minutes. By exploring the non–conserved regions, where MGEs are mainly retrotransposons called Tys, and marking the candidate Tys based on their length, we are able to locate a priori and automatically all the already known Tys and map all the putative Tys in all the strains. The remaining putative mobile elements (PMEs) emerging from this intra–specific comparison are sharp markers of inter–specific evolution: indeed, many events of non–conservation among different yeast strains correspond to PMEs. A clustering based on the presence/absence of the candidate Tys in the strains suggests an evolutionary interconnection that is very similar to classic phylogenetic trees based on SNPs analysis, even though it is computed without using phylogenetic information. Conclusions The case study indicates that the proposed methodology brings two major advantages: (a) it does not require any template sequence for the wanted MGEs and (b) it can be applied to infer MGEs also for low coverage genomes with unresolved bases, where traditional approaches are largely ineffective. PMID:23514613

Extreme-Scale De Novo Genome Assembly

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

Georganas, Evangelos; Hofmeyr, Steven; Egan, Rob

De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern genomics. This work presents HipMER, a high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code. Genome assembly software has many components, each of which stresses different components of a computer system. This chapter explains the computational challenges involved in each step of the HipMer pipeline, the key distributed data structures, and communication costs in detail. We present performance results of assembling the human genome and themore » large hexaploid wheat genome on large supercomputers up to tens of thousands of cores.« less

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

PubMed

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP effects (SNP-BLUP model). When reducing marker density from WGS data to 30 K, SNP-BLUP tended to yield the highest accuracies, at least in the short term. Based on SVD of the genotype matrix, we developed a direct method for the calculation of BayesC estimates of marker effects. Although SVD- and MCMC-based marker effects differed slightly, their prediction accuracies were similar. Assuming that the SVD of the marker genotype matrix is already performed for other reasons (e.g. for SNP-BLUP), computation times for the BayesC predictions were comparable to those of SNP-BLUP.

Recovering complete and draft population genomes from metagenome datasets

DOE PAGES

Sangwan, Naseer; Xia, Fangfang; Gilbert, Jack A.

2016-03-08

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem ofmore » chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution.« less

Recovering complete and draft population genomes from metagenome datasets

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

Sangwan, Naseer; Xia, Fangfang; Gilbert, Jack A.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem ofmore » chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution.« less

In silico exploration of Red Sea Bacillus genomes for natural product biosynthetic gene clusters.

PubMed

Othoum, Ghofran; Bougouffa, Salim; Razali, Rozaimi; Bokhari, Ameerah; Alamoudi, Soha; Antunes, André; Gao, Xin; Hoehndorf, Robert; Arold, Stefan T; Gojobori, Takashi; Hirt, Heribert; Mijakovic, Ivan; Bajic, Vladimir B; Lafi, Feras F; Essack, Magbubah

2018-05-22

The increasing spectrum of multidrug-resistant bacteria is a major global public health concern, necessitating discovery of novel antimicrobial agents. Here, members of the genus Bacillus are investigated as a potentially attractive source of novel antibiotics due to their broad spectrum of antimicrobial activities. We specifically focus on a computational analysis of the distinctive biosynthetic potential of Bacillus paralicheniformis strains isolated from the Red Sea, an ecosystem exposed to adverse, highly saline and hot conditions. We report the complete circular and annotated genomes of two Red Sea strains, B. paralicheniformis Bac48 isolated from mangrove mud and B. paralicheniformis Bac84 isolated from microbial mat collected from Rabigh Harbor Lagoon in Saudi Arabia. Comparing the genomes of B. paralicheniformis Bac48 and B. paralicheniformis Bac84 with nine publicly available complete genomes of B. licheniformis and three genomes of B. paralicheniformis, revealed that all of the B. paralicheniformis strains in this study are more enriched in nonribosomal peptides (NRPs). We further report the first computationally identified trans-acyltransferase (trans-AT) nonribosomal peptide synthetase/polyketide synthase (PKS/ NRPS) cluster in strains of this species. B. paralicheniformis species have more genes associated with biosynthesis of antimicrobial bioactive compounds than other previously characterized species of B. licheniformis, which suggests that these species are better potential sources for novel antibiotics. Moreover, the genome of the Red Sea strain B. paralicheniformis Bac48 is more enriched in modular PKS genes compared to B. licheniformis strains and other B. paralicheniformis strains. This may be linked to adaptations that strains surviving in the Red Sea underwent to survive in the relatively hot and saline ecosystems.

A Comparative Analysis of the Lyve-SET Phylogenomics Pipeline for Genomic Epidemiology of Foodborne Pathogens

PubMed Central

Katz, Lee S.; Griswold, Taylor; Williams-Newkirk, Amanda J.; Wagner, Darlene; Petkau, Aaron; Sieffert, Cameron; Van Domselaar, Gary; Deng, Xiangyu; Carleton, Heather A.

2017-01-01

Modern epidemiology of foodborne bacterial pathogens in industrialized countries relies increasingly on whole genome sequencing (WGS) techniques. As opposed to profiling techniques such as pulsed-field gel electrophoresis, WGS requires a variety of computational methods. Since 2013, United States agencies responsible for food safety including the CDC, FDA, and USDA, have been performing whole-genome sequencing (WGS) on all Listeria monocytogenes found in clinical, food, and environmental samples. Each year, more genomes of other foodborne pathogens such as Escherichia coli, Campylobacter jejuni, and Salmonella enterica are being sequenced. Comparing thousands of genomes across an entire species requires a fast method with coarse resolution; however, capturing the fine details of highly related isolates requires a computationally heavy and sophisticated algorithm. Most L. monocytogenes investigations employing WGS depend on being able to identify an outbreak clade whose inter-genomic distances are less than an empirically determined threshold. When the difference between a few single nucleotide polymorphisms (SNPs) can help distinguish between genomes that are likely outbreak-associated and those that are less likely to be associated, we require a fine-resolution method. To achieve this level of resolution, we have developed Lyve-SET, a high-quality SNP pipeline. We evaluated Lyve-SET by retrospectively investigating 12 outbreak data sets along with four other SNP pipelines that have been used in outbreak investigation or similar scenarios. To compare these pipelines, several distance and phylogeny-based comparison methods were applied, which collectively showed that multiple pipelines were able to identify most outbreak clusters and strains. Currently in the US PulseNet system, whole genome multi-locus sequence typing (wgMLST) is the preferred primary method for foodborne WGS cluster detection and outbreak investigation due to its ability to name standardized genomic profiles, its central database, and its ability to be run in a graphical user interface. However, creating a functional wgMLST scheme requires extended up-front development and subject-matter expertise. When a scheme does not exist or when the highest resolution is needed, SNP analysis is used. Using three Listeria outbreak data sets, we demonstrated the concordance between Lyve-SET SNP typing and wgMLST. Availability: Lyve-SET can be found at https://github.com/lskatz/Lyve-SET. PMID:28348549

DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data.

PubMed

Gaspar, John M; Hart, Ronald P

2017-11-29

DNA methylation is an epigenetic modification that is studied at a single-base resolution with bisulfite treatment followed by high-throughput sequencing. After alignment of the sequence reads to a reference genome, methylation counts are analyzed to determine genomic regions that are differentially methylated between two or more biological conditions. Even though a variety of software packages is available for different aspects of the bioinformatics analysis, they often produce results that are biased or require excessive computational requirements. DMRfinder is a novel computational pipeline that identifies differentially methylated regions efficiently. Following alignment, DMRfinder extracts methylation counts and performs a modified single-linkage clustering of methylation sites into genomic regions. It then compares methylation levels using beta-binomial hierarchical modeling and Wald tests. Among its innovative attributes are the analyses of novel methylation sites and methylation linkage, as well as the simultaneous statistical analysis of multiple sample groups. To demonstrate its efficiency, DMRfinder is benchmarked against other computational approaches using a large published dataset. Contrasting two replicates of the same sample yielded minimal genomic regions with DMRfinder, whereas two alternative software packages reported a substantial number of false positives. Further analyses of biological samples revealed fundamental differences between DMRfinder and another software package, despite the fact that they utilize the same underlying statistical basis. For each step, DMRfinder completed the analysis in a fraction of the time required by other software. Among the computational approaches for identifying differentially methylated regions from high-throughput bisulfite sequencing datasets, DMRfinder is the first that integrates all the post-alignment steps in a single package. Compared to other software, DMRfinder is extremely efficient and unbiased in this process. DMRfinder is free and open-source software, available on GitHub ( github.com/jsh58/DMRfinder ); it is written in Python and R, and is supported on Linux.

Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing.

PubMed

Patel, Nirali M; Michelini, Vanessa V; Snell, Jeff M; Balu, Saianand; Hoyle, Alan P; Parker, Joel S; Hayward, Michele C; Eberhard, David A; Salazar, Ashley H; McNeillie, Patrick; Xu, Jia; Huettner, Claudia S; Koyama, Takahiko; Utro, Filippo; Rhrissorrakrai, Kahn; Norel, Raquel; Bilal, Erhan; Royyuru, Ajay; Parida, Laxmi; Earp, H Shelton; Grilley-Olson, Juneko E; Hayes, D Neil; Harvey, Stephen J; Sharpless, Norman E; Kim, William Y

2018-02-01

Using next-generation sequencing (NGS) to guide cancer therapy has created challenges in analyzing and reporting large volumes of genomic data to patients and caregivers. Specifically, providing current, accurate information on newly approved therapies and open clinical trials requires considerable manual curation performed mainly by human "molecular tumor boards" (MTBs). The purpose of this study was to determine the utility of cognitive computing as performed by Watson for Genomics (WfG) compared with a human MTB. One thousand eighteen patient cases that previously underwent targeted exon sequencing at the University of North Carolina (UNC) and subsequent analysis by the UNCseq informatics pipeline and the UNC MTB between November 7, 2011, and May 12, 2015, were analyzed with WfG, a cognitive computing technology for genomic analysis. Using a WfG-curated actionable gene list, we identified additional genomic events of potential significance (not discovered by traditional MTB curation) in 323 (32%) patients. The majority of these additional genomic events were considered actionable based upon their ability to qualify patients for biomarker-selected clinical trials. Indeed, the opening of a relevant clinical trial within 1 month prior to WfG analysis provided the rationale for identification of a new actionable event in nearly a quarter of the 323 patients. This automated analysis took <3 minutes per case. These results demonstrate that the interpretation and actionability of somatic NGS results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing could potentially improve patient care by providing a rapid, comprehensive approach for data analysis and consideration of up-to-date availability of clinical trials. The results of this study demonstrate that the interpretation and actionability of somatic next-generation sequencing results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing can significantly improve patient care by providing a fast, cost-effective, and comprehensive approach for data analysis in the delivery of precision medicine. Patients and physicians who are considering enrollment in clinical trials may benefit from the support of such tools applied to genomic data. © AlphaMed Press 2017.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

PubMed

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A; Burgueño, Juan; Bandeira E Sousa, Massaine; Crossa, José

2018-03-28

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines ([Formula: see text]) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. Copyright © 2018 Cuevas et al.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

PubMed Central

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A.; Burgueño, Juan; Bandeira e Sousa, Massaine; Crossa, José

2018-01-01

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. PMID:29476023

Examining the architecture of cellular computing through a comparative study with a computer

PubMed Central

Wang, Degeng; Gribskov, Michael

2005-01-01

The computer and the cell both use information embedded in simple coding, the binary software code and the quadruple genomic code, respectively, to support system operations. A comparative examination of their system architecture as well as their information storage and utilization schemes is performed. On top of the code, both systems display a modular, multi-layered architecture, which, in the case of a computer, arises from human engineering efforts through a combination of hardware implementation and software abstraction. Using the computer as a reference system, a simplistic mapping of the architectural components between the two is easily detected. This comparison also reveals that a cell abolishes the software–hardware barrier through genomic encoding for the constituents of the biochemical network, a cell's ‘hardware’ equivalent to the computer central processing unit (CPU). The information loading (gene expression) process acts as a major determinant of the encoded constituent's abundance, which, in turn, often determines the ‘bandwidth’ of a biochemical pathway. Cellular processes are implemented in biochemical pathways in parallel manners. In a computer, on the other hand, the software provides only instructions and data for the CPU. A process represents just sequentially ordered actions by the CPU and only virtual parallelism can be implemented through CPU time-sharing. Whereas process management in a computer may simply mean job scheduling, coordinating pathway bandwidth through the gene expression machinery represents a major process management scheme in a cell. In summary, a cell can be viewed as a super-parallel computer, which computes through controlled hardware composition. While we have, at best, a very fragmented understanding of cellular operation, we have a thorough understanding of the computer throughout the engineering process. The potential utilization of this knowledge to the benefit of systems biology is discussed. PMID:16849179

Examining the architecture of cellular computing through a comparative study with a computer.

PubMed

Wang, Degeng; Gribskov, Michael

2005-06-22

The computer and the cell both use information embedded in simple coding, the binary software code and the quadruple genomic code, respectively, to support system operations. A comparative examination of their system architecture as well as their information storage and utilization schemes is performed. On top of the code, both systems display a modular, multi-layered architecture, which, in the case of a computer, arises from human engineering efforts through a combination of hardware implementation and software abstraction. Using the computer as a reference system, a simplistic mapping of the architectural components between the two is easily detected. This comparison also reveals that a cell abolishes the software-hardware barrier through genomic encoding for the constituents of the biochemical network, a cell's "hardware" equivalent to the computer central processing unit (CPU). The information loading (gene expression) process acts as a major determinant of the encoded constituent's abundance, which, in turn, often determines the "bandwidth" of a biochemical pathway. Cellular processes are implemented in biochemical pathways in parallel manners. In a computer, on the other hand, the software provides only instructions and data for the CPU. A process represents just sequentially ordered actions by the CPU and only virtual parallelism can be implemented through CPU time-sharing. Whereas process management in a computer may simply mean job scheduling, coordinating pathway bandwidth through the gene expression machinery represents a major process management scheme in a cell. In summary, a cell can be viewed as a super-parallel computer, which computes through controlled hardware composition. While we have, at best, a very fragmented understanding of cellular operation, we have a thorough understanding of the computer throughout the engineering process. The potential utilization of this knowledge to the benefit of systems biology is discussed.

cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila

PubMed Central

2014-01-01

Background Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. Description It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. Conclusions cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes. PMID:25521507

Context based computational analysis and characterization of ARS consensus sequences (ACS) of Saccharomyces cerevisiae genome.

PubMed

Singh, Vinod Kumar; Krishnamachari, Annangarachari

2016-09-01

Genome-wide experimental studies in Saccharomyces cerevisiae reveal that autonomous replicating sequence (ARS) requires an essential consensus sequence (ACS) for replication activity. Computational studies identified thousands of ACS like patterns in the genome. However, only a few hundreds of these sites act as replicating sites and the rest are considered as dormant or evolving sites. In a bid to understand the sequence makeup of replication sites, a content and context-based analysis was performed on a set of replicating ACS sequences that binds to origin-recognition complex (ORC) denoted as ORC-ACS and non-replicating ACS sequences (nrACS), that are not bound by ORC. In this study, DNA properties such as base composition, correlation, sequence dependent thermodynamic and DNA structural profiles, and their positions have been considered for characterizing ORC-ACS and nrACS. Analysis reveals that ORC-ACS depict marked differences in nucleotide composition and context features in its vicinity compared to nrACS. Interestingly, an A-rich motif was also discovered in ORC-ACS sequences within its nucleosome-free region. Profound changes in the conformational features, such as DNA helical twist, inclination angle and stacking energy between ORC-ACS and nrACS were observed. Distribution of ACS motifs in the non-coding segments points to the locations of ORC-ACS which are found far away from the adjacent gene start position compared to nrACS thereby enabling an accessible environment for ORC-proteins. Our attempt is novel in considering the contextual view of ACS and its flanking region along with nucleosome positioning in the S. cerevisiae genome and may be useful for any computational prediction scheme.

LightAssembler: fast and memory-efficient assembly algorithm for high-throughput sequencing reads.

PubMed

El-Metwally, Sara; Zakaria, Magdi; Hamza, Taher

2016-11-01

The deluge of current sequenced data has exceeded Moore's Law, more than doubling every 2 years since the next-generation sequencing (NGS) technologies were invented. Accordingly, we will able to generate more and more data with high speed at fixed cost, but lack the computational resources to store, process and analyze it. With error prone high throughput NGS reads and genomic repeats, the assembly graph contains massive amount of redundant nodes and branching edges. Most assembly pipelines require this large graph to reside in memory to start their workflows, which is intractable for mammalian genomes. Resource-efficient genome assemblers combine both the power of advanced computing techniques and innovative data structures to encode the assembly graph efficiently in a computer memory. LightAssembler is a lightweight assembly algorithm designed to be executed on a desktop machine. It uses a pair of cache oblivious Bloom filters, one holding a uniform sample of [Formula: see text]-spaced sequenced [Formula: see text]-mers and the other holding [Formula: see text]-mers classified as likely correct, using a simple statistical test. LightAssembler contains a light implementation of the graph traversal and simplification modules that achieves comparable assembly accuracy and contiguity to other competing tools. Our method reduces the memory usage by [Formula: see text] compared to the resource-efficient assemblers using benchmark datasets from GAGE and Assemblathon projects. While LightAssembler can be considered as a gap-based sequence assembler, different gap sizes result in an almost constant assembly size and genome coverage. https://github.com/SaraEl-Metwally/LightAssembler CONTACT: sarah_almetwally4@mans.edu.egSupplementary information: 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.

Genome-Wide Analysis in Brazilians Reveals Highly Differentiated Native American Genome Regions

PubMed Central

Havt, Alexandre; Nayak, Uma; Pinkerton, Relana; Farber, Emily; Concannon, Patrick; Lima, Aldo A.; Guerrant, Richard L.

2017-01-01

Despite its population, geographic size, and emerging economic importance, disproportionately little genome-scale research exists into genetic factors that predispose Brazilians to disease, or the population genetics of risk. After identification of suitable proxy populations and careful analysis of tri-continental admixture in 1,538 North-Eastern Brazilians to estimate individual ancestry and ancestral allele frequencies, we computed 400,000 genome-wide locus-specific branch length (LSBL) Fst statistics of Brazilian Amerindian ancestry compared to European and African; and a similar set of differentiation statistics for their Amerindian component compared with the closest Asian 1000 Genomes population (surprisingly, Bengalis in Bangladesh). After ranking SNPs by these statistics, we identified the top 10 highly differentiated SNPs in five genome regions in the LSBL tests of Brazilian Amerindian ancestry compared to European and African; and the top 10 SNPs in eight regions comparing their Amerindian component to the closest Asian 1000 Genomes population. We found SNPs within or proximal to the genes CIITA (rs6498115), SMC6 (rs1834619), and KLHL29 (rs2288697) were most differentiated in the Amerindian-specific branch, while SNPs in the genes ADAMTS9 (rs7631391), DOCK2 (rs77594147), SLC28A1 (rs28649017), ARHGAP5 (rs7151991), and CIITA (rs45601437) were most highly differentiated in the Asian comparison. These genes are known to influence immune function, metabolic and anthropometry traits, and embryonic development. These analyses have identified candidate genes for selection within Amerindian ancestry, and by comparison of the two analyses, those for which the differentiation may have arisen during the migration from Asia to the Americas. PMID:28100790

Comparative genomic de-convolution of the cotton genome revealed a decaploid ancestor and widespread chromosomal fractionation.

PubMed

Wang, Xiyin; Guo, Hui; Wang, Jinpeng; Lei, Tianyu; Liu, Tao; Wang, Zhenyi; Li, Yuxian; Lee, Tae-Ho; Li, Jingping; Tang, Haibao; Jin, Dianchuan; Paterson, Andrew H

2016-02-01

The 'apparently' simple genomes of many angiosperms mask complex evolutionary histories. The reference genome sequence for cotton (Gossypium spp.) revealed a ploidy change of a complexity unprecedented to date, indeed that could not be distinguished as to its exact dosage. Herein, by developing several comparative, computational and statistical approaches, we revealed a 5× multiplication in the cotton lineage of an ancestral genome common to cotton and cacao, and proposed evolutionary models to show how such a decaploid ancestor formed. The c. 70% gene loss necessary to bring the ancestral decaploid to its current gene count appears to fit an approximate geometrical model; that is, although many genes may be lost by single-gene deletion events, some may be lost in groups of consecutive genes. Gene loss following cotton decaploidy has largely just reduced gene copy numbers of some homologous groups. We designed a novel approach to deconvolute layers of chromosome homology, providing definitive information on gene orthology and paralogy across broad evolutionary distances, both of fundamental value and serving as an important platform to support further studies in and beyond cotton and genomics communities. No claim to original US government works. New Phytologist © 2015 New Phytologist Trust.

Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data.

PubMed

Nabavi, Sheida

2016-08-15

With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues. To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies. The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.

Integrated design, execution, and analysis of arrayed and pooled CRISPR genome-editing experiments.

PubMed

Canver, Matthew C; Haeussler, Maximilian; Bauer, Daniel E; Orkin, Stuart H; Sanjana, Neville E; Shalem, Ophir; Yuan, Guo-Cheng; Zhang, Feng; Concordet, Jean-Paul; Pinello, Luca

2018-05-01

CRISPR (clustered regularly interspaced short palindromic repeats) genome-editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep-sequencing data resulting from genome-editing experiments. However, these tools are typically developed in isolation and oftentimes are not readily translatable into laboratory-based experiments. Here, we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome-editing experiment. This protocol provides instructions for sgRNA design with CRISPOR (computational tool for the design, evaluation, and cloning of sgRNA sequences), experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso (computational tool for analysis of genome-editing outcomes from deep-sequencing data). This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4-5 weeks by non-experts, as well as computational data analysis that can be performed in 1-2 d by both computational and noncomputational biologists alike using web-based and/or command-line versions.

Minimal Absent Words in Four Human Genome Assemblies

PubMed Central

Garcia, Sara P.; Pinho, Armando J.

2011-01-01

Minimal absent words have been computed in genomes of organisms from all domains of life. Here, we aim to contribute to the catalogue of human genomic variation by investigating the variation in number and content of minimal absent words within a species, using four human genome assemblies. We compare the reference human genome GRCh37 assembly, the HuRef assembly of the genome of Craig Venter, the NA12878 assembly from cell line GM12878, and the YH assembly of the genome of a Han Chinese individual. We find the variation in number and content of minimal absent words between assemblies more significant for large and very large minimal absent words, where the biases of sequencing and assembly methodologies become more pronounced. Moreover, we find generally greater similarity between the human genome assemblies sequenced with capillary-based technologies (GRCh37 and HuRef) than between the human genome assemblies sequenced with massively parallel technologies (NA12878 and YH). Finally, as expected, we find the overall variation in number and content of minimal absent words within a species to be generally smaller than the variation between species. PMID:22220210

Ten years of maintaining and expanding a microbial genome and metagenome analysis system.

PubMed

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

2015-11-01

Launched in March 2005, the Integrated Microbial Genomes (IMG) system is a comprehensive data management system that supports multidimensional comparative analysis of genomic data. At the core of the IMG system is a data warehouse that contains genome and metagenome datasets sequenced at the Joint Genome Institute or provided by scientific users, as well as public genome datasets available at the National Center for Biotechnology Information Genbank sequence data archive. Genomes and metagenome datasets are processed using IMG's microbial genome and metagenome sequence data processing pipelines and are integrated into the data warehouse using IMG's data integration toolkits. Microbial genome and metagenome application specific data marts and user interfaces provide access to different subsets of IMG's data and analysis toolkits. This review article revisits IMG's original aims, highlights key milestones reached by the system during the past 10 years, and discusses the main challenges faced by a rapidly expanding system, in particular the complexity of maintaining such a system in an academic setting with limited budgets and computing and data management infrastructure. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using Markov chains of nucleotide sequences as a possible precursor to predict functional roles of human genome: a case study on inactive chromatin regions.

PubMed

Lee, K-E; Lee, E-J; Park, H-S

2016-08-30

Recent advances in computational epigenetics have provided new opportunities to evaluate n-gram probabilistic language models. In this paper, we describe a systematic genome-wide approach for predicting functional roles in inactive chromatin regions by using a sequence-based Markovian chromatin map of the human genome. We demonstrate that Markov chains of sequences can be used as a precursor to predict functional roles in heterochromatin regions and provide an example comparing two publicly available chromatin annotations of large-scale epigenomics projects: ENCODE project consortium and Roadmap Epigenomics consortium.

UFO: a web server for ultra-fast functional profiling of whole genome protein sequences.

PubMed

Meinicke, Peter

2009-09-02

Functional profiling is a key technique to characterize and compare the functional potential of entire genomes. The estimation of profiles according to an assignment of sequences to functional categories is a computationally expensive task because it requires the comparison of all protein sequences from a genome with a usually large database of annotated sequences or sequence families. Based on machine learning techniques for Pfam domain detection, the UFO web server for ultra-fast functional profiling allows researchers to process large protein sequence collections instantaneously. Besides the frequencies of Pfam and GO categories, the user also obtains the sequence specific assignments to Pfam domain families. In addition, a comparison with existing genomes provides dissimilarity scores with respect to 821 reference proteomes. Considering the underlying UFO domain detection, the results on 206 test genomes indicate a high sensitivity of the approach. In comparison with current state-of-the-art HMMs, the runtime measurements show a considerable speed up in the range of four orders of magnitude. For an average size prokaryotic genome, the computation of a functional profile together with its comparison typically requires about 10 seconds of processing time. For the first time the UFO web server makes it possible to get a quick overview on the functional inventory of newly sequenced organisms. The genome scale comparison with a large number of precomputed profiles allows a first guess about functionally related organisms. The service is freely available and does not require user registration or specification of a valid email address.

IMG/M: integrated genome and metagenome comparative data analysis system

DOE PAGES

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

2016-10-13

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

IMG/M: integrated genome and metagenome comparative data analysis system

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

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

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

CGI: Java Software for Mapping and Visualizing Data from Array-based Comparative Genomic Hybridization and Expression Profiling

PubMed Central

Gu, Joyce Xiuweu-Xu; Wei, Michael Yang; Rao, Pulivarthi H.; Lau, Ching C.; Behl, Sanjiv; Man, Tsz-Kwong

2007-01-01

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License. PMID:19936083

CGI: Java software for mapping and visualizing data from array-based comparative genomic hybridization and expression profiling.

PubMed

Gu, Joyce Xiuweu-Xu; Wei, Michael Yang; Rao, Pulivarthi H; Lau, Ching C; Behl, Sanjiv; Man, Tsz-Kwong

2007-10-06

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License.

IMG/M: integrated genome and metagenome comparative data analysis system

PubMed Central

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

2017-01-01

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

Computing prokaryotic gene ubiquity: rescuing the core from extinction.

PubMed

Charlebois, Robert L; Doolittle, W Ford

2004-12-01

The genomic core concept has found several uses in comparative and evolutionary genomics. Defined as the set of all genes common to (ubiquitous among) all genomes in a phylogenetically coherent group, core size decreases as the number and phylogenetic diversity of the relevant group increases. Here, we focus on methods for defining the size and composition of the core of all genes shared by sequenced genomes of prokaryotes (Bacteria and Archaea). There are few (almost certainly less than 50) genes shared by all of the 147 genomes compared, surely insufficient to conduct all essential functions. Sequencing and annotation errors are responsible for the apparent absence of some genes, while very limited but genuine disappearances (from just one or a few genomes) can account for several others. Core size will continue to decrease as more genome sequences appear, unless the requirement for ubiquity is relaxed. Such relaxation seems consistent with any reasonable biological purpose for seeking a core, but it renders the problem of definition more problematic. We propose an alternative approach (the phylogenetically balanced core), which preserves some of the biological utility of the core concept. Cores, however delimited, preferentially contain informational rather than operational genes; we present a new hypothesis for why this might be so.

GStream: Improving SNP and CNV Coverage on Genome-Wide Association Studies

PubMed Central

Alonso, Arnald; Marsal, Sara; Tortosa, Raül; Canela-Xandri, Oriol; Julià, Antonio

2013-01-01

We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method. PMID:23844243

PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species ▿ ‡ #

PubMed Central

Gillespie, Joseph J.; Wattam, Alice R.; Cammer, Stephen A.; Gabbard, Joseph L.; Shukla, Maulik P.; Dalay, Oral; Driscoll, Timothy; Hix, Deborah; Mane, Shrinivasrao P.; Mao, Chunhong; Nordberg, Eric K.; Scott, Mark; Schulman, Julie R.; Snyder, Eric E.; Sullivan, Daniel E.; Wang, Chunxia; Warren, Andrew; Williams, Kelly P.; Xue, Tian; Seung Yoo, Hyun; Zhang, Chengdong; Zhang, Yan; Will, Rebecca; Kenyon, Ronald W.; Sobral, Bruno W.

2011-01-01

Funded by the National Institute of Allergy and Infectious Diseases, the Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. Specifically, PATRIC provides scientists with (i) a comprehensive bacterial genomics database, (ii) a plethora of associated data relevant to genomic analysis, and (iii) an extensive suite of computational tools and platforms for bioinformatics analysis. While the primary aim of PATRIC is to advance the knowledge underlying the biology of human pathogens, all publicly available genome-scale data for bacteria are compiled and continually updated, thereby enabling comparative analyses to reveal the basis for differences between infectious free-living and commensal species. Herein we summarize the major features available at PATRIC, dividing the resources into two major categories: (i) organisms, genomes, and comparative genomics and (ii) recurrent integration of community-derived associated data. Additionally, we present two experimental designs typical of bacterial genomics research and report on the execution of both projects using only PATRIC data and tools. These applications encompass a broad range of the data and analysis tools available, illustrating practical uses of PATRIC for the biologist. Finally, a summary of PATRIC's outreach activities, collaborative endeavors, and future research directions is provided. PMID:21896772

Metagenomics of prebiotic and probiotic supplemented broilers gastrointestinal tract microbiome

USDA-ARS?s Scientific Manuscript database

Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) is a recently developed computational approach for prediction of functional composition of a microbiome comparing marker gene data with a reference genome database. The procedure established significant link ...

Application of Nexus copy number software for CNV detection and analysis.

PubMed

Darvishi, Katayoon

2010-04-01

Among human structural genomic variation, copy number variants (CNVs) are the most frequently known component, comprised of gains/losses of DNA segments that are generally 1 kb in length or longer. Array-based comparative genomic hybridization (aCGH) has emerged as a powerful tool for detecting genomic copy number variants (CNVs). With the rapid increase in the density of array technology and with the adaptation of new high-throughput technology, a reliable and computationally scalable method for accurate mapping of recurring DNA copy number aberrations has become a main focus in research. Here we introduce Nexus Copy Number software, a platform-independent tool, to analyze the output files of all types of commercial and custom-made comparative genomic hybridization (CGH) and single-nucleotide polymorphism (SNP) arrays, such as those manufactured by Affymetrix, Agilent Technologies, Illumina, and Roche NimbleGen. It also supports data generated by various array image-analysis software tools such as GenePix, ImaGene, and BlueFuse. (c) 2010 by John Wiley & Sons, Inc.

SWPhylo - A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees.

PubMed

Yu, Xiaoyu; Reva, Oleg N

2018-01-01

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction

PubMed Central

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R.; Buenrostro-Mariscal, Raymundo

2017-01-01

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. PMID:28391241

SWPhylo – A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees

PubMed Central

Yu, Xiaoyu; Reva, Oleg N

2018-01-01

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA. PMID:29511354

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José Cricelio; Luna-Vázquez, Francisco Javier; Salinas-Ruiz, Josafhat; Herrera-Morales, José R; Buenrostro-Mariscal, Raymundo

2017-06-07

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments. Copyright © 2017 Montesinos-López et al.

Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea

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

Bowers, Robert M.; Kyrpides, Nikos C.; Stepanauskas, Ramunas

The number of genomes from uncultivated microbes will soon surpass the number of isolate genomes in public databases (Hugenholtz, Skarshewski, & Parks, 2016). Technological advancements in high-throughput sequencing and assembly, including single-cell genomics and the computational extraction of genomes from metagenomes (GFMs), are largely responsible. Here we propose community standards for reporting the Minimum Information about a Single-Cell Genome (MIxS-SCG) and Minimum Information about Genomes extracted From Metagenomes (MIxS-GFM) specific for Bacteria and Archaea. The standards have been developed in the context of the International Genomics Standards Consortium (GSC) community (Field et al., 2014) and can be viewed as amore » supplement to other GSC checklists including the Minimum Information about a Genome Sequence (MIGS), Minimum information about a Metagenomic Sequence(s) (MIMS) (Field et al., 2008) and Minimum Information about a Marker Gene Sequence (MIMARKS) (P. Yilmaz et al., 2011). Community-wide acceptance of MIxS-SCG and MIxS-GFM for Bacteria and Archaea will enable broad comparative analyses of genomes from the majority of taxa that remain uncultivated, improving our understanding of microbial function, ecology, and evolution.« less

Scanning the human genome at kilobase resolution.

PubMed

Chen, Jun; Kim, Yeong C; Jung, Yong-Chul; Xuan, Zhenyu; Dworkin, Geoff; Zhang, Yanming; Zhang, Michael Q; Wang, San Ming

2008-05-01

Normal genome variation and pathogenic genome alteration frequently affect small regions in the genome. Identifying those genomic changes remains a technical challenge. We report here the development of the DGS (Ditag Genome Scanning) technique for high-resolution analysis of genome structure. The basic features of DGS include (1) use of high-frequent restriction enzymes to fractionate the genome into small fragments; (2) collection of two tags from two ends of a given DNA fragment to form a ditag to represent the fragment; (3) application of the 454 sequencing system to reach a comprehensive ditag sequence collection; (4) determination of the genome origin of ditags by mapping to reference ditags from known genome sequences; (5) use of ditag sequences directly as the sense and antisense PCR primers to amplify the original DNA fragment. To study the relationship between ditags and genome structure, we performed a computational study by using the human genome reference sequences as a model, and analyzed the ditags experimentally collected from the well-characterized normal human DNA GM15510 and the leukemic human DNA of Kasumi-1 cells. Our studies show that DGS provides a kilobase resolution for studying genome structure with high specificity and high genome coverage. DGS can be applied to validate genome assembly, to compare genome similarity and variation in normal populations, and to identify genomic abnormality including insertion, inversion, deletion, translocation, and amplification in pathological genomes such as cancer genomes.

Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests.

PubMed

Yu, Zhaoxia; Demetriou, Michael; Gillen, Daniel L

2015-09-01

Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power. © 2015 WILEY PERIODICALS, INC.

GapBlaster-A Graphical Gap Filler for Prokaryote Genomes.

PubMed

de Sá, Pablo H C G; Miranda, Fábio; Veras, Adonney; de Melo, Diego Magalhães; Soares, Siomar; Pinheiro, Kenny; Guimarães, Luis; Azevedo, Vasco; Silva, Artur; Ramos, Rommel T J

2016-01-01

The advent of NGS (Next Generation Sequencing) technologies has resulted in an exponential increase in the number of complete genomes available in biological databases. This advance has allowed the development of several computational tools enabling analyses of large amounts of data in each of the various steps, from processing and quality filtering to gap filling and manual curation. The tools developed for gap closure are very useful as they result in more complete genomes, which will influence downstream analyses of genomic plasticity and comparative genomics. However, the gap filling step remains a challenge for genome assembly, often requiring manual intervention. Here, we present GapBlaster, a graphical application to evaluate and close gaps. GapBlaster was developed via Java programming language. The software uses contigs obtained in the assembly of the genome to perform an alignment against a draft of the genome/scaffold, using BLAST or Mummer to close gaps. Then, all identified alignments of contigs that extend through the gaps in the draft sequence are presented to the user for further evaluation via the GapBlaster graphical interface. GapBlaster presents significant results compared to other similar software and has the advantage of offering a graphical interface for manual curation of the gaps. GapBlaster program, the user guide and the test datasets are freely available at https://sourceforge.net/projects/gapblaster2015/. It requires Sun JDK 8 and Blast or Mummer.

Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers

PubMed Central

2010-01-01

Background The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. PMID:20969788

On the inversion-indel distance

PubMed Central

2013-01-01

Background The inversion distance, that is the distance between two unichromosomal genomes with the same content allowing only inversions of DNA segments, can be computed thanks to a pioneering approach of Hannenhalli and Pevzner in 1995. In 2000, El-Mabrouk extended the inversion model to allow the comparison of unichromosomal genomes with unequal contents, thus insertions and deletions of DNA segments besides inversions. However, an exact algorithm was presented only for the case in which we have insertions alone and no deletion (or vice versa), while a heuristic was provided for the symmetric case, that allows both insertions and deletions and is called the inversion-indel distance. In 2005, Yancopoulos, Attie and Friedberg started a new branch of research by introducing the generic double cut and join (DCJ) operation, that can represent several genome rearrangements (including inversions). Among others, the DCJ model gave rise to two important results. First, it has been shown that the inversion distance can be computed in a simpler way with the help of the DCJ operation. Second, the DCJ operation originated the DCJ-indel distance, that allows the comparison of genomes with unequal contents, considering DCJ, insertions and deletions, and can be computed in linear time. Results In the present work we put these two results together to solve an open problem, showing that, when the graph that represents the relation between the two compared genomes has no bad components, the inversion-indel distance is equal to the DCJ-indel distance. We also give a lower and an upper bound for the inversion-indel distance in the presence of bad components. PMID:24564182

Two- and three-input TALE-based AND logic computation in embryonic stem cells.

PubMed

Lienert, Florian; Torella, Joseph P; Chen, Jan-Hung; Norsworthy, Michael; Richardson, Ryan R; Silver, Pamela A

2013-11-01

Biological computing circuits can enhance our ability to control cellular functions and have potential applications in tissue engineering and medical treatments. Transcriptional activator-like effectors (TALEs) represent attractive components of synthetic gene regulatory circuits, as they can be designed de novo to target a given DNA sequence. We here demonstrate that TALEs can perform Boolean logic computation in mammalian cells. Using a split-intein protein-splicing strategy, we show that a functional TALE can be reconstituted from two inactive parts, thus generating two-input AND logic computation. We further demonstrate three-piece intein splicing in mammalian cells and use it to perform three-input AND computation. Using methods for random as well as targeted insertion of these relatively large genetic circuits, we show that TALE-based logic circuits are functional when integrated into the genome of mouse embryonic stem cells. Comparing construct variants in the same genomic context, we modulated the strength of the TALE-responsive promoter to improve the output of these circuits. Our work establishes split TALEs as a tool for building logic computation with the potential of controlling expression of endogenous genes or transgenes in response to a combination of cellular signals.

Upgrading short-read animal genome assemblies to chromosome level using comparative genomics and a universal probe set.

PubMed

Damas, Joana; O'Connor, Rebecca; Farré, Marta; Lenis, Vasileios Panagiotis E; Martell, Henry J; Mandawala, Anjali; Fowler, Katie; Joseph, Sunitha; Swain, Martin T; Griffin, Darren K; Larkin, Denis M

2017-05-01

Most recent initiatives to sequence and assemble new species' genomes de novo fail to achieve the ultimate endpoint to produce contigs, each representing one whole chromosome. Even the best-assembled genomes (using contemporary technologies) consist of subchromosomal-sized scaffolds. To circumvent this problem, we developed a novel approach that combines computational algorithms to merge scaffolds into chromosomal fragments, PCR-based scaffold verification, and physical mapping to chromosomes. Multigenome-alignment-guided probe selection led to the development of a set of universal avian BAC clones that permit rapid anchoring of multiple scaffolds to chromosomes on all avian genomes. As proof of principle, we assembled genomes of the pigeon ( Columbia livia ) and peregrine falcon ( Falco peregrinus ) to chromosome levels comparable, in continuity, to avian reference genomes. Both species are of interest for breeding, cultural, food, and/or environmental reasons. Pigeon has a typical avian karyotype (2n = 80), while falcon (2n = 50) is highly rearranged compared to the avian ancestor. By using chromosome breakpoint data, we established that avian interchromosomal breakpoints appear in the regions of low density of conserved noncoding elements (CNEs) and that the chromosomal fission sites are further limited to long CNE "deserts." This corresponds with fission being the rarest type of rearrangement in avian genome evolution. High-throughput multiple hybridization and rapid capture strategies using the current BAC set provide the basis for assembling numerous avian (and possibly other reptilian) species, while the overall strategy for scaffold assembly and mapping provides the basis for an approach that (provided metaphases can be generated) could be applied to any animal genome. © 2017 Damas et al.; Published by Cold Spring Harbor Laboratory Press.

Upgrading short-read animal genome assemblies to chromosome level using comparative genomics and a universal probe set

PubMed Central

O'Connor, Rebecca; Lenis, Vasileios Panagiotis E.; Martell, Henry J.; Mandawala, Anjali; Fowler, Katie; Joseph, Sunitha; Swain, Martin T.; Griffin, Darren K.; Larkin, Denis M.

2017-01-01

Most recent initiatives to sequence and assemble new species’ genomes de novo fail to achieve the ultimate endpoint to produce contigs, each representing one whole chromosome. Even the best-assembled genomes (using contemporary technologies) consist of subchromosomal-sized scaffolds. To circumvent this problem, we developed a novel approach that combines computational algorithms to merge scaffolds into chromosomal fragments, PCR-based scaffold verification, and physical mapping to chromosomes. Multigenome-alignment-guided probe selection led to the development of a set of universal avian BAC clones that permit rapid anchoring of multiple scaffolds to chromosomes on all avian genomes. As proof of principle, we assembled genomes of the pigeon (Columbia livia) and peregrine falcon (Falco peregrinus) to chromosome levels comparable, in continuity, to avian reference genomes. Both species are of interest for breeding, cultural, food, and/or environmental reasons. Pigeon has a typical avian karyotype (2n = 80), while falcon (2n = 50) is highly rearranged compared to the avian ancestor. By using chromosome breakpoint data, we established that avian interchromosomal breakpoints appear in the regions of low density of conserved noncoding elements (CNEs) and that the chromosomal fission sites are further limited to long CNE “deserts.” This corresponds with fission being the rarest type of rearrangement in avian genome evolution. High-throughput multiple hybridization and rapid capture strategies using the current BAC set provide the basis for assembling numerous avian (and possibly other reptilian) species, while the overall strategy for scaffold assembly and mapping provides the basis for an approach that (provided metaphases can be generated) could be applied to any animal genome. PMID:27903645

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.

GenomeVIP: a cloud platform for genomic variant discovery and interpretation

PubMed Central

Mashl, R. Jay; Scott, Adam D.; Huang, Kuan-lin; Wyczalkowski, Matthew A.; Yoon, Christopher J.; Niu, Beifang; DeNardo, Erin; Yellapantula, Venkata D.; Handsaker, Robert E.; Chen, Ken; Koboldt, Daniel C.; Ye, Kai; Fenyö, David; Raphael, Benjamin J.; Wendl, Michael C.; Ding, Li

2017-01-01

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets. PMID:28522612

Population Genomics of Fungal and Oomycete Pathogens.

PubMed

Grünwald, Niklaus J; McDonald, Bruce A; Milgroom, Michael G

2016-08-04

We are entering a new era in plant pathology in which whole-genome sequences of many individuals of a pathogen species are becoming readily available. Population genomics aims to discover genetic mechanisms underlying phenotypes associated with adaptive traits such as pathogenicity, virulence, fungicide resistance, and host specialization, as genome sequences or large numbers of single nucleotide polymorphisms become readily available from multiple individuals of the same species. This emerging field encompasses detailed genetic analyses of natural populations, comparative genomic analyses of closely related species, identification of genes under selection, and linkage analyses involving association studies in natural populations or segregating populations resulting from crosses. The era of pathogen population genomics will provide new opportunities and challenges, requiring new computational and analytical tools. This review focuses on conceptual and methodological issues as well as the approaches to answering questions in population genomics. The major steps start with defining relevant biological and evolutionary questions, followed by sampling, genotyping, and phenotyping, and ending in analytical methods and interpretations. We provide examples of recent applications of population genomics to fungal and oomycete plant pathogens.

Inferring transposons activity chronology by TRANScendence - TEs database and de-novo mining tool.

PubMed

Startek, Michał Piotr; Nogły, Jakub; Gromadka, Agnieszka; Grzebelus, Dariusz; Gambin, Anna

2017-10-16

The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest. Here we describe the computational tool for mining, classifying and storing TEs from newly sequenced genomes. This is an online, web-based, user-friendly service, enabling users to upload their own genomic data, and perform de-novo searches for TEs. The detected TEs are automatically analyzed, compared to reference databases, annotated, clustered into families, and stored in TEs repository. Also, the genome-wide nesting structure of found elements are detected and analyzed by new method for inferring evolutionary history of TEs. We illustrate the functionality of our tool by performing a full-scale analyses of TE landscape in Medicago truncatula genome. TRANScendence is an effective tool for the de-novo annotation and classification of transposable elements in newly-acquired genomes. Its streamlined interface makes it well-suited for evolutionary studies.

GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes.

PubMed

Arakawa, Kazuharu; Yamada, Yohei; Shinoda, Kosaku; Nakayama, Yoichi; Tomita, Masaru

2006-03-23

Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

Genome-wide identification of the subcellular localization of the Escherichia coli B proteome using experimental and computational methods.

PubMed

Han, Mee-Jung; Yun, Hongseok; Lee, Jeong Wook; Lee, Yu Hyun; Lee, Sang Yup; Yoo, Jong-Shin; Kim, Jin Young; Kim, Jihyun F; Hur, Cheol-Goo

2011-04-01

Escherichia coli K-12 and B strains have most widely been employed for scientific studies as well as industrial applications. Recently, the complete genome sequences of two representative descendants of E. coli B strains, REL606 and BL21(DE3), have been determined. Here, we report the subproteome reference maps of E. coli B REL606 by analyzing cytoplasmic, periplasmic, inner and outer membrane, and extracellular proteomes based on the genome information using experimental and computational approaches. Among the total of 3487 spots, 651 proteins including 410 non-redundant proteins were identified and characterized by 2-DE and LC-MS/MS; they include 440 cytoplasmic, 45 periplasmic, 50 inner membrane, 61 outer membrane, and 55 extracellular proteins. In addition, subcellular localizations of all 4205 ORFs of E. coli B were predicted by combined computational prediction methods. The subcellular localizations of 1812 (43.09%) proteins of currently unknown function were newly assigned. The results of computational prediction were also compared with the experimental results, showing that overall precision and recall were 92.16 and 92.16%, respectively. This work represents the most comprehensive analyses of the subproteomes of E. coli B, and will be useful as a reference for proteome profiling studies under various conditions. The complete proteome data are available online (http://ecolib.kaist.ac.kr). Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs.

PubMed

Lim, Chun Shen; Brown, Chris M

2017-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

PubMed Central

Lim, Chun Shen; Brown, Chris M.

2018-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community. PMID:29354101

Microsatellite Interruptions Stabilize Primate Genomes and Exist as Population-Specific Single Nucleotide Polymorphisms within Individual Human Genomes

PubMed Central

Ananda, Guruprasad; Hile, Suzanne E.; Breski, Amanda; Wang, Yanli; Kelkar, Yogeshwar; Makova, Kateryna D.; Eckert, Kristin A.

2014-01-01

Interruptions of microsatellite sequences impact genome evolution and can alter disease manifestation. However, human polymorphism levels at interrupted microsatellites (iMSs) are not known at a genome-wide scale, and the pathways for gaining interruptions are poorly understood. Using the 1000 Genomes Phase-1 variant call set, we interrogated mono-, di-, tri-, and tetranucleotide repeats up to 10 units in length. We detected ∼26,000–40,000 iMSs within each of four human population groups (African, European, East Asian, and American). We identified population-specific iMSs within exonic regions, and discovered that known disease-associated iMSs contain alleles present at differing frequencies among the populations. By analyzing longer microsatellites in primate genomes, we demonstrate that single interruptions result in a genome-wide average two- to six-fold reduction in microsatellite mutability, as compared with perfect microsatellites. Centrally located interruptions lowered mutability dramatically, by two to three orders of magnitude. Using a biochemical approach, we tested directly whether the mutability of a specific iMS is lower because of decreased DNA polymerase strand slippage errors. Modeling the adenomatous polyposis coli tumor suppressor gene sequence, we observed that a single base substitution interruption reduced strand slippage error rates five- to 50-fold, relative to a perfect repeat, during synthesis by DNA polymerases α, β, or η. Computationally, we demonstrate that iMSs arise primarily by base substitution mutations within individual human genomes. Our biochemical survey of human DNA polymerase α, β, δ, κ, and η error rates within certain microsatellites suggests that interruptions are created most frequently by low fidelity polymerases. Our combined computational and biochemical results demonstrate that iMSs are abundant in human genomes and are sources of population-specific genetic variation that may affect genome stability. The genome-wide identification of iMSs in human populations presented here has important implications for current models describing the impact of microsatellite polymorphisms on gene expression. PMID:25033203

ISOL@: an Italian SOLAnaceae genomics resource.

PubMed

Chiusano, Maria Luisa; D'Agostino, Nunzio; Traini, Alessandra; Licciardello, Concetta; Raimondo, Enrico; Aversano, Mario; Frusciante, Luigi; Monti, Luigi

2008-03-26

Present-day '-omics' technologies produce overwhelming amounts of data which include genome sequences, information on gene expression (transcripts and proteins) and on cell metabolic status. These data represent multiple aspects of a biological system and need to be investigated as a whole to shed light on the mechanisms which underpin the system functionality. The gathering and convergence of data generated by high-throughput technologies, the effective integration of different data-sources and the analysis of the information content based on comparative approaches are key methods for meaningful biological interpretations. In the frame of the International Solanaceae Genome Project, we propose here ISOLA, an Italian SOLAnaceae genomics resource. ISOLA (available at http://biosrv.cab.unina.it/isola) represents a trial platform and it is conceived as a multi-level computational environment.ISOLA currently consists of two main levels: the genome and the expression level. The cornerstone of the genome level is represented by the Solanum lycopersicum genome draft sequences generated by the International Tomato Genome Sequencing Consortium. Instead, the basic element of the expression level is the transcriptome information from different Solanaceae species, mainly in the form of species-specific comprehensive collections of Expressed Sequence Tags (ESTs). The cross-talk between the genome and the expression levels is based on data source sharing and on tools that enhance data quality, that extract information content from the levels' under parts and produce value-added biological knowledge. ISOLA is the result of a bioinformatics effort that addresses the challenges of the post-genomics era. It is designed to exploit '-omics' data based on effective integration to acquire biological knowledge and to approach a systems biology view. Beyond providing experimental biologists with a preliminary annotation of the tomato genome, this effort aims to produce a trial computational environment where different aspects and details are maintained as they are relevant for the analysis of the organization, the functionality and the evolution of the Solanaceae family.

Independent assessment and improvement of wheat genome sequence assemblies using Fosill jumping libraries.

PubMed

Lu, Fu-Hao; McKenzie, Neil; Kettleborough, George; Heavens, Darren; Clark, Matthew D; Bevan, Michael W

2018-05-01

The accurate sequencing and assembly of very large, often polyploid, genomes remains a challenging task, limiting long-range sequence information and phased sequence variation for applications such as plant breeding. The 15-Gb hexaploid bread wheat (Triticum aestivum) genome has been particularly challenging to sequence, and several different approaches have recently generated long-range assemblies. Mapping and understanding the types of assembly errors are important for optimising future sequencing and assembly approaches and for comparative genomics. Here we use a Fosill 38-kb jumping library to assess medium and longer-range order of different publicly available wheat genome assemblies. Modifications to the Fosill protocol generated longer Illumina sequences and enabled comprehensive genome coverage. Analyses of two independent Bacterial Artificial Chromosome (BAC)-based chromosome-scale assemblies, two independent Illumina whole genome shotgun assemblies, and a hybrid Single Molecule Real Time (SMRT-PacBio) and short read (Illumina) assembly were carried out. We revealed a surprising scale and variety of discrepancies using Fosill mate-pair mapping and validated several of each class. In addition, Fosill mate-pairs were used to scaffold a whole genome Illumina assembly, leading to a 3-fold increase in N50 values. Our analyses, using an independent means to validate different wheat genome assemblies, show that whole genome shotgun assemblies based solely on Illumina sequences are significantly more accurate by all measures compared to BAC-based chromosome-scale assemblies and hybrid SMRT-Illumina approaches. Although current whole genome assemblies are reasonably accurate and useful, additional improvements will be needed to generate complete assemblies of wheat genomes using open-source, computationally efficient, and cost-effective methods.

Mobile Genetic Elements: In Silico, In Vitro, In Vivo

PubMed Central

Arkhipova, Irina R.; Rice, Phoebe A.

2016-01-01

Mobile genetic elements (MGEs), also called transposable elements (TEs), represent universal components of most genomes and are intimately involved in nearly all aspects of genome organization, function, and evolution. However, there is currently a gap between fast-paced TE discovery in silico, stimulated by exponential growth of comparative genomic studies, and a limited number of experimental models amenable to more traditional in vitro and in vivo studies of structural, mechanistic, and regulatory properties of diverse MGEs. Experimental and computational scientists came together to bridge this gap at a recent conference, “Mobile Genetic Elements: in silico, in vitro, in vivo,” held at the Marine Biological Laboratory (MBL) in Woods Hole, MA, USA. PMID:26822117

Streamlined Genome Sequence Compression using Distributed Source Coding

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Chen, Feng; Cui, Lijuan; Cheng, Samuel

2014-01-01

We aim at developing a streamlined genome sequence compression algorithm to support alternative miniaturized sequencing devices, which have limited communication, storage, and computation power. Existing techniques that require heavy client (encoder side) cannot be applied. To tackle this challenge, we carefully examined distributed source coding theory and developed a customized reference-based genome compression protocol to meet the low-complexity need at the client side. Based on the variation between source and reference, our protocol will pick adaptively either syndrome coding or hash coding to compress subsequences of changing code length. Our experimental results showed promising performance of the proposed method when compared with the state-of-the-art algorithm (GRS). PMID:25520552

Scoring schemes of palindrome clusters for more sensitive prediction of replication origins in herpesviruses

PubMed Central

Chew, David S. H.; Choi, Kwok Pui; Leung, Ming-Ying

2005-01-01

Many empirical studies show that there are unusual clusters of palindromes, closely spaced direct and inverted repeats around the replication origins of herpesviruses. In this paper, we introduce two new scoring schemes to quantify the spatial abundance of palindromes in a genomic sequence. Based on these scoring schemes, a computational method to predict the locations of replication origins is developed. When our predictions are compared with 39 known or annotated replication origins in 19 herpesviruses, close to 80% of the replication origins are located within 2% of the genome length. A list of predicted locations of replication origins in all the known herpesviruses with complete genome sequences is reported. PMID:16141192

Assessing the evolutionary rate of positional orthologous genes in prokaryotes using synteny data

PubMed Central

Lemoine, Frédéric; Lespinet, Olivier; Labedan, Bernard

2007-01-01

Background Comparison of completely sequenced microbial genomes has revealed how fluid these genomes are. Detecting synteny blocks requires reliable methods to determining the orthologs among the whole set of homologs detected by exhaustive comparisons between each pair of completely sequenced genomes. This is a complex and difficult problem in the field of comparative genomics but will help to better understand the way prokaryotic genomes are evolving. Results We have developed a suite of programs that automate three essential steps to study conservation of gene order, and validated them with a set of 107 bacteria and archaea that cover the majority of the prokaryotic taxonomic space. We identified the whole set of shared homologs between two or more species and computed the evolutionary distance separating each pair of homologs. We applied two strategies to extract from the set of homologs a collection of valid orthologs shared by at least two genomes. The first computes the Reciprocal Smallest Distance (RSD) using the PAM distances separating pairs of homologs. The second method groups homologs in families and reconstructs each family's evolutionary tree, distinguishing bona fide orthologs as well as paralogs created after the last speciation event. Although the phylogenetic tree method often succeeds where RSD fails, the reverse could occasionally be true. Accordingly, we used the data obtained with either methods or their intersection to number the orthologs that are adjacent in for each pair of genomes, the Positional Orthologous Genes (POGs), and to further study their properties. Once all these synteny blocks have been detected, we showed that POGs are subject to more evolutionary constraints than orthologs outside synteny groups, whichever the taxonomic distance separating the compared organisms. Conclusion The suite of programs described in this paper allows a reliable detection of orthologs and is useful for evaluating gene order conservation in prokaryotes whichever their taxonomic distance. Thus, our approach will make easy the rapid identification of POGS in the next few years as we are expecting to be inundated with thousands of completely sequenced microbial genomes. PMID:18047665

GenoMycDB: a database for comparative analysis of mycobacterial genes and genomes.

PubMed

Catanho, Marcos; Mascarenhas, Daniel; Degrave, Wim; Miranda, Antonio Basílio de

2006-03-31

Several databases and computational tools have been created with the aim of organizing, integrating and analyzing the wealth of information generated by large-scale sequencing projects of mycobacterial genomes and those of other organisms. However, with very few exceptions, these databases and tools do not allow for massive and/or dynamic comparison of these data. GenoMycDB (http://www.dbbm.fiocruz.br/GenoMycDB) is a relational database built for large-scale comparative analyses of completely sequenced mycobacterial genomes, based on their predicted protein content. Its central structure is composed of the results obtained after pair-wise sequence alignments among all the predicted proteins coded by the genomes of six mycobacteria: Mycobacterium tuberculosis (strains H37Rv and CDC1551), M. bovis AF2122/97, M. avium subsp. paratuberculosis K10, M. leprae TN, and M. smegmatis MC2 155. The database stores the computed similarity parameters of every aligned pair, providing for each protein sequence the predicted subcellular localization, the assigned cluster of orthologous groups, the features of the corresponding gene, and links to several important databases. Tables containing pairs or groups of potential homologs between selected species/strains can be produced dynamically by user-defined criteria, based on one or multiple sequence similarity parameters. In addition, searches can be restricted according to the predicted subcellular localization of the protein, the DNA strand of the corresponding gene and/or the description of the protein. Massive data search and/or retrieval are available, and different ways of exporting the result are offered. GenoMycDB provides an on-line resource for the functional classification of mycobacterial proteins as well as for the analysis of genome structure, organization, and evolution.

Discovering novel subsystems using comparative genomics

PubMed Central

Ferrer, Luciana; Shearer, Alexander G.; Karp, Peter D.

2011-01-01

Motivation: Key problems for computational genomics include discovering novel pathways in genome data, and discovering functional interaction partners for genes to define new members of partially elucidated pathways. Results: We propose a novel method for the discovery of subsystems from annotated genomes. For each gene pair, a score measuring the likelihood that the two genes belong to a same subsystem is computed using genome context methods. Genes are then grouped based on these scores, and the resulting groups are filtered to keep only high-confidence groups. Since the method is based on genome context analysis, it relies solely on structural annotation of the genomes. The method can be used to discover new pathways, find missing genes from a known pathway, find new protein complexes or other kinds of functional groups and assign function to genes. We tested the accuracy of our method in Escherichia coli K-12. In one configuration of the system, we find that 31.6% of the candidate groups generated by our method match a known pathway or protein complex closely, and that we rediscover 31.2% of all known pathways and protein complexes of at least 4 genes. We believe that a significant proportion of the candidates that do not match any known group in E.coli K-12 corresponds to novel subsystems that may represent promising leads for future laboratory research. We discuss in-depth examples of these findings. Availability: Predicted subsystems are available at http://brg.ai.sri.com/pwy-discovery/journal.html. Contact: lferrer@ai.sri.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21775308

Identification of cis-suppression of human disease mutations by comparative genomics.

PubMed

Jordan, Daniel M; Frangakis, Stephan G; Golzio, Christelle; Cassa, Christopher A; Kurtzberg, Joanne; Davis, Erica E; Sunyaev, Shamil R; Katsanis, Nicholas

2015-08-13

Patterns of amino acid conservation have served as a tool for understanding protein evolution. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients. Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity.

eHive: an artificial intelligence workflow system for genomic analysis.

PubMed

Severin, Jessica; Beal, Kathryn; Vilella, Albert J; Fitzgerald, Stephen; Schuster, Michael; Gordon, Leo; Ureta-Vidal, Abel; Flicek, Paul; Herrero, Javier

2010-05-11

The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future. We present eHive, a new fault tolerant distributed processing system initially designed to support comparative genomic analysis, based on blackboard systems, network distributed autonomous agents, dataflow graphs and block-branch diagrams. In the eHive system a MySQL database serves as the central blackboard and the autonomous agent, a Perl script, queries the system and runs jobs as required. The system allows us to define dataflow and branching rules to suit all our production pipelines. We describe the implementation of three pipelines: (1) pairwise whole genome alignments, (2) multiple whole genome alignments and (3) gene trees with protein homology inference. Finally, we show the efficiency of the system in real case scenarios. eHive allows us to produce computationally demanding results in a reliable and efficient way with minimal supervision and high throughput. Further documentation is available at: http://www.ensembl.org/info/docs/eHive/.

Automatic glaucoma diagnosis through medical imaging informatics.

PubMed

Liu, Jiang; Zhang, Zhuo; Wong, Damon Wing Kee; Xu, Yanwu; Yin, Fengshou; Cheng, Jun; Tan, Ngan Meng; Kwoh, Chee Keong; Xu, Dong; Tham, Yih Chung; Aung, Tin; Wong, Tien Yin

2013-01-01

Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

PubMed Central

Peña-Castillo, Lourdes; Tasan, Murat; Myers, Chad L; Lee, Hyunju; Joshi, Trupti; Zhang, Chao; Guan, Yuanfang; Leone, Michele; Pagnani, Andrea; Kim, Wan Kyu; Krumpelman, Chase; Tian, Weidong; Obozinski, Guillaume; Qi, Yanjun; Mostafavi, Sara; Lin, Guan Ning; Berriz, Gabriel F; Gibbons, Francis D; Lanckriet, Gert; Qiu, Jian; Grant, Charles; Barutcuoglu, Zafer; Hill, David P; Warde-Farley, David; Grouios, Chris; Ray, Debajyoti; Blake, Judith A; Deng, Minghua; Jordan, Michael I; Noble, William S; Morris, Quaid; Klein-Seetharaman, Judith; Bar-Joseph, Ziv; Chen, Ting; Sun, Fengzhu; Troyanskaya, Olga G; Marcotte, Edward M; Xu, Dong; Hughes, Timothy R; Roth, Frederick P

2008-01-01

Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. Results: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. Conclusion: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized. PMID:18613946

A greedy, graph-based algorithm for the alignment of multiple homologous gene lists.

PubMed

Fostier, Jan; Proost, Sebastian; Dhoedt, Bart; Saeys, Yvan; Demeester, Piet; Van de Peer, Yves; Vandepoele, Klaas

2011-03-15

Many comparative genomics studies rely on the correct identification of homologous genomic regions using accurate alignment tools. In such case, the alphabet of the input sequences consists of complete genes, rather than nucleotides or amino acids. As optimal multiple sequence alignment is computationally impractical, a progressive alignment strategy is often employed. However, such an approach is susceptible to the propagation of alignment errors in early pairwise alignment steps, especially when dealing with strongly diverged genomic regions. In this article, we present a novel accurate and efficient greedy, graph-based algorithm for the alignment of multiple homologous genomic segments, represented as ordered gene lists. Based on provable properties of the graph structure, several heuristics are developed to resolve local alignment conflicts that occur due to gene duplication and/or rearrangement events on the different genomic segments. The performance of the algorithm is assessed by comparing the alignment results of homologous genomic segments in Arabidopsis thaliana to those obtained by using both a progressive alignment method and an earlier graph-based implementation. Especially for datasets that contain strongly diverged segments, the proposed method achieves a substantially higher alignment accuracy, and proves to be sufficiently fast for large datasets including a few dozens of eukaryotic genomes. http://bioinformatics.psb.ugent.be/software. The algorithm is implemented as a part of the i-ADHoRe 3.0 package.

Mathematics and Computer Science | Argonne National Laboratory

Science.gov Websites

Genomics and Systems Biology LCRCLaboratory Computing Resource Center MCSGMidwest Center for Structural Genomics NAISENorthwestern-Argonne Institute of Science & Engineering SBCStructural Biology Center

NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.

PubMed

Colen, Rivka; Foster, Ian; Gatenby, Robert; Giger, Mary Ellen; Gillies, Robert; Gutman, David; Heller, Matthew; Jain, Rajan; Madabhushi, Anant; Madhavan, Subha; Napel, Sandy; Rao, Arvind; Saltz, Joel; Tatum, James; Verhaak, Roeland; Whitman, Gary

2014-10-01

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Breaking the computational barriers of pairwise genome comparison.

PubMed

Torreno, Oscar; Trelles, Oswaldo

2015-08-11

Conventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community. We have developed C libraries for pairwise sequence comparison within diverse architectures, ranging from commodity systems to high performance and cloud computing environments. Exhaustive tests were performed using different datasets of closely- and distantly-related sequences that span from small viral genomes to large mammalian chromosomes. The tests demonstrated that our solution is capable of generating high quality results with a linear-time response and controlled memory consumption, being comparable or faster than the current state-of-the-art methods. We have addressed the problem of pairwise and all-versus-all comparison of large sequences in general, greatly increasing the limits on input data size. The approach described here is based on a modular out-of-core strategy that uses secondary storage to avoid reaching memory limits during the identification of High-scoring Segment Pairs (HSPs) between the sequences under comparison. Software engineering concepts were applied to avoid intermediate result re-calculation, to minimise the performance impact of input/output (I/O) operations and to modularise the process, thus enhancing application flexibility and extendibility. Our computationally-efficient approach allows tasks such as the massive comparison of complete genomes, evolutionary event detection, the identification of conserved synteny blocks and inter-genome distance calculations to be performed more effectively.

Plant Aquaporins: Genome-Wide Identification, Transcriptomics, Proteomics, and Advanced Analytical Tools.

PubMed

Deshmukh, Rupesh K; Sonah, Humira; Bélanger, Richard R

2016-01-01

Aquaporins (AQPs) are channel-forming integral membrane proteins that facilitate the movement of water and many other small molecules. Compared to animals, plants contain a much higher number of AQPs in their genome. Homology-based identification of AQPs in sequenced species is feasible because of the high level of conservation of protein sequences across plant species. Genome-wide characterization of AQPs has highlighted several important aspects such as distribution, genetic organization, evolution and conserved features governing solute specificity. From a functional point of view, the understanding of AQP transport system has expanded rapidly with the help of transcriptomics and proteomics data. The efficient analysis of enormous amounts of data generated through omic scale studies has been facilitated through computational advancements. Prediction of protein tertiary structures, pore architecture, cavities, phosphorylation sites, heterodimerization, and co-expression networks has become more sophisticated and accurate with increasing computational tools and pipelines. However, the effectiveness of computational approaches is based on the understanding of physiological and biochemical properties, transport kinetics, solute specificity, molecular interactions, sequence variations, phylogeny and evolution of aquaporins. For this purpose, tools like Xenopus oocyte assays, yeast expression systems, artificial proteoliposomes, and lipid membranes have been efficiently exploited to study the many facets that influence solute transport by AQPs. In the present review, we discuss genome-wide identification of AQPs in plants in relation with recent advancements in analytical tools, and their availability and technological challenges as they apply to AQPs. An exhaustive review of omics resources available for AQP research is also provided in order to optimize their efficient utilization. Finally, a detailed catalog of computational tools and analytical pipelines is offered as a resource for AQP research.

Plant Aquaporins: Genome-Wide Identification, Transcriptomics, Proteomics, and Advanced Analytical Tools

PubMed Central

Deshmukh, Rupesh K.; Sonah, Humira; Bélanger, Richard R.

2016-01-01

Aquaporins (AQPs) are channel-forming integral membrane proteins that facilitate the movement of water and many other small molecules. Compared to animals, plants contain a much higher number of AQPs in their genome. Homology-based identification of AQPs in sequenced species is feasible because of the high level of conservation of protein sequences across plant species. Genome-wide characterization of AQPs has highlighted several important aspects such as distribution, genetic organization, evolution and conserved features governing solute specificity. From a functional point of view, the understanding of AQP transport system has expanded rapidly with the help of transcriptomics and proteomics data. The efficient analysis of enormous amounts of data generated through omic scale studies has been facilitated through computational advancements. Prediction of protein tertiary structures, pore architecture, cavities, phosphorylation sites, heterodimerization, and co-expression networks has become more sophisticated and accurate with increasing computational tools and pipelines. However, the effectiveness of computational approaches is based on the understanding of physiological and biochemical properties, transport kinetics, solute specificity, molecular interactions, sequence variations, phylogeny and evolution of aquaporins. For this purpose, tools like Xenopus oocyte assays, yeast expression systems, artificial proteoliposomes, and lipid membranes have been efficiently exploited to study the many facets that influence solute transport by AQPs. In the present review, we discuss genome-wide identification of AQPs in plants in relation with recent advancements in analytical tools, and their availability and technological challenges as they apply to AQPs. An exhaustive review of omics resources available for AQP research is also provided in order to optimize their efficient utilization. Finally, a detailed catalog of computational tools and analytical pipelines is offered as a resource for AQP research. PMID:28066459

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

PubMed Central

Latendresse, Mario; Paley, Suzanne M.; Krummenacker, Markus; Ong, Quang D.; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M.; Caspi, Ron

2016-01-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. PMID:26454094

Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

PubMed

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-05-12

A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05). GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.

Trinity: Transcriptome Assembly for Genetic and Functional Analysis of Cancer | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The cancer transcriptome is shaped by genetic changes, variation in gene transcription, mRNA processing, editing and stability, and the cancer microbiome. Deciphering this variation and understanding its implications on tumorigenesis requires sophisticated computational analyses. Most RNA-Seq analyses rely on methods that first map short reads to a reference genome, and then compare them to annotated transcripts or assemble them. However, this strategy can be limited when the cancer genome is substantially different than the reference or for detecting sequences from the cancer microbiome.

Simple Sequence Repeats in Escherichia coli: Abundance, Distribution, Composition, and Polymorphism

PubMed Central

Gur-Arie, Riva; Cohen, Cyril J.; Eitan, Yuval; Shelef, Leora; Hallerman, Eric M.; Kashi, Yechezkel

2000-01-01

Computer-based genome-wide screening of the DNA sequence of Escherichia coli strain K12 revealed tens of thousands of tandem simple sequence repeat (SSR) tracts, with motifs ranging from 1 to 6 nucleotides. SSRs were well distributed throughout the genome. Mononucleotide SSRs were over-represented in noncoding regions and under-represented in open reading frames (ORFs). Nucleotide composition of mono- and dinucleotide SSRs, both in ORFs and in noncoding regions, differed from that of the genomic region in which they occurred, with 93% of all mononucleotide SSRs proving to be of A or T. Computer-based analysis of the fine position of every SSR locus in the noncoding portion of the genome relative to downstream ORFs showed SSRs located in areas that could affect gene regulation. DNA sequences at 14 arbitrarily chosen SSR tracts were compared among E. coli strains. Polymorphisms of SSR copy number were observed at four of seven mononucleotide SSR tracts screened, with all polymorphisms occurring in noncoding regions. SSR polymorphism could prove important as a genome-wide source of variation, both for practical applications (including rapid detection, strain identification, and detection of loci affecting key phenotypes) and for evolutionary adaptation of microbes.[The sequence data described in this paper have been submitted to the GenBank data library under accession numbers AF209020–209030 and AF209508–209518.] PMID:10645951

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers

PubMed Central

2009-01-01

Background Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle. Methods Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls. Results For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy. All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time. Conclusions The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended. PMID:20043835

Genomic prediction using an iterative conditional expectation algorithm for a fast BayesC-like model.

PubMed

Dong, Linsong; Wang, Zhiyong

2018-06-11

Genomic prediction is feasible for estimating genomic breeding values because of dense genome-wide markers and credible statistical methods, such as Genomic Best Linear Unbiased Prediction (GBLUP) and various Bayesian methods. Compared with GBLUP, Bayesian methods propose more flexible assumptions for the distributions of SNP effects. However, most Bayesian methods are performed based on Markov chain Monte Carlo (MCMC) algorithms, leading to computational efficiency challenges. Hence, some fast Bayesian approaches, such as fast BayesB (fBayesB), were proposed to speed up the calculation. This study proposed another fast Bayesian method termed fast BayesC (fBayesC). The prior distribution of fBayesC assumes that a SNP with probability γ has a non-zero effect which comes from a normal density with a common variance. The simulated data from QTLMAS XII workshop and actual data on large yellow croaker were used to compare the predictive results of fBayesB, fBayesC and (MCMC-based) BayesC. The results showed that when γ was set as a small value, such as 0.01 in the simulated data or 0.001 in the actual data, fBayesB and fBayesC yielded lower prediction accuracies (abilities) than BayesC. In the actual data, fBayesC could yield very similar predictive abilities as BayesC when γ ≥ 0.01. When γ = 0.01, fBayesB could also yield similar results as fBayesC and BayesC. However, fBayesB could not yield an explicit result when γ ≥ 0.1, but a similar situation was not observed for fBayesC. Moreover, the computational speed of fBayesC was significantly faster than that of BayesC, making fBayesC a promising method for genomic prediction.

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

Simon, Horst D.; Zorn, Manfred D.; Spengler, Sylvia J.

The pace of extraordinary advances in molecular biology has accelerated in the past decade due in large part to discoveries coming from genome projects on human and model organisms. The advances in the genome project so far, happening well ahead of schedule and under budget, have exceeded any dreams by its protagonists, let alone formal expectations. Biologists expect the next phase of the genome project to be even more startling in terms of dramatic breakthroughs in our understanding of human biology, the biology of health and of disease. Only today can biologists begin to envision the necessary experimental, computational andmore » theoretical steps necessary to exploit genome sequence information for its medical impact, its contribution to biotechnology and economic competitiveness, and its ultimate contribution to environmental quality. High performance computing has become one of the critical enabling technologies, which will help to translate this vision of future advances in biology into reality. Biologists are increasingly becoming aware of the potential of high performance computing. The goal of this tutorial is to introduce the exciting new developments in computational biology and genomics to the high performance computing community.« less

Identification of functional elements and regulatory circuits by Drosophila modENCODE

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

Roy, Sushmita; Ernst, Jason; Kharchenko, Peter V.

2010-12-22

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- andmore » tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation. Several years after the complete genetic sequencing of many species, it is still unclear how to translate genomic information into a functional map of cellular and developmental programs. The Encyclopedia of DNA Elements (ENCODE) (1) and model organism ENCODE (modENCODE) (2) projects use diverse genomic assays to comprehensively annotate the Homo sapiens (human), Drosophila melanogaster (fruit fly), and Caenorhabditis elegans (worm) genomes, through systematic generation and computational integration of functional genomic data sets. Previous genomic studies in flies have made seminal contributions to our understanding of basic biological mechanisms and genome functions, facilitated by genetic, experimental, computational, and manual annotation of the euchromatic and heterochromatic genome (3), small genome size, short life cycle, and a deep knowledge of development, gene function, and chromosome biology. The functions of {approx}40% of the protein and nonprotein-coding genes [FlyBase 5.12 (4)] have been determined from cDNA collections (5, 6), manual curation of gene models (7), gene mutations and comprehensive genome-wide RNA interference screens (8-10), and comparative genomic analyses (11, 12). The Drosophila modENCODE project has generated more than 700 data sets that profile transcripts, histone modifications and physical nucleosome properties, general and specific transcription factors (TFs), and replication programs in cell lines, isolated tissues, and whole organisms across several developmental stages (Fig. 1). Here, we computationally integrate these data sets and report (i) improved and additional genome annotations, including full-length proteincoding genes and peptides as short as 21 amino acids; (ii) noncoding transcripts, including 132 candidate structural RNAs and 1608 nonstructural transcripts; (iii) additional Argonaute (Ago)-associated small RNA genes and pathways, including new microRNAs (miRNAs) encoded within protein-coding exons and endogenous small interfering RNAs (siRNAs) from 3-inch untranslated regions; (iv) chromatin 'states' defined by combinatorial patterns of 18 chromatin marks that are associated with distinct functions and properties; (v) regions of high TF occupancy and replication activity with likely epigenetic regulation; (vi)mixed TF and miRNA regulatory networks with hierarchical structure and enriched feed-forward loops; (vii) coexpression- and co-regulation-based functional annotations for nearly 3000 genes; (viii) stage- and tissue-specific regulators; and (ix) predictive models of gene expression levels and regulator function.« less

Computational biology for ageing

PubMed Central

Wieser, Daniela; Papatheodorou, Irene; Ziehm, Matthias; Thornton, Janet M.

2011-01-01

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions. PMID:21115530

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

PubMed Central

O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279

Cloud computing for genomic data analysis and collaboration.

PubMed

Langmead, Ben; Nellore, Abhinav

2018-04-01

Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences.

PubMed

Inda, Márcia A; van Batenburg, Marinus F; Roos, Marco; Belloum, Adam S Z; Vasunin, Dmitry; Wibisono, Adianto; van Kampen, Antoine H C; Breit, Timo M

2008-08-08

Chromosome location is often used as a scaffold to organize genomic information in both the living cell and molecular biological research. Thus, ever-increasing amounts of data about genomic features are stored in public databases and can be readily visualized by genome browsers. To perform in silico experimentation conveniently with this genomics data, biologists need tools to process and compare datasets routinely and explore the obtained results interactively. The complexity of such experimentation requires these tools to be based on an e-Science approach, hence generic, modular, and reusable. A virtual laboratory environment with workflows, workflow management systems, and Grid computation are therefore essential. Here we apply an e-Science approach to develop SigWin-detector, a workflow-based tool that can detect significantly enriched windows of (genomic) features in a (DNA) sequence in a fast and reproducible way. For proof-of-principle, we utilize a biological use case to detect regions of increased and decreased gene expression (RIDGEs and anti-RIDGEs) in human transcriptome maps. We improved the original method for RIDGE detection by replacing the costly step of estimation by random sampling with a faster analytical formula for computing the distribution of the null hypothesis being tested and by developing a new algorithm for computing moving medians. SigWin-detector was developed using the WS-VLAM workflow management system and consists of several reusable modules that are linked together in a basic workflow. The configuration of this basic workflow can be adapted to satisfy the requirements of the specific in silico experiment. As we show with the results from analyses in the biological use case on RIDGEs, SigWin-detector is an efficient and reusable Grid-based tool for discovering windows enriched for features of a particular type in any sequence of values. Thus, SigWin-detector provides the proof-of-principle for the modular e-Science based concept of integrative bioinformatics experimentation.

Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes

NASA Astrophysics Data System (ADS)

Endy, Drew; You, Lingchong; Yin, John; Molineux, Ian J.

2000-05-01

We created a simulation based on experimental data from bacteriophage T7 that computes the developmental cycle of the wild-type phage and also of mutants that have an altered genome order. We used the simulation to compute the fitness of more than 105 mutants. We tested these computations by constructing and experimentally characterizing T7 mutants in which we repositioned gene 1, coding for T7 RNA polymerase. Computed protein synthesis rates for ectopic gene 1 strains were in moderate agreement with observed rates. Computed phage-doubling rates were close to observations for two of four strains, but significantly overestimated those of the other two. Computations indicate that the genome organization of wild-type T7 is nearly optimal for growth: only 2.8% of random genome permutations were computed to grow faster, the highest 31% faster, than wild type. Specific discrepancies between computations and observations suggest that a better understanding of the translation efficiency of individual mRNAs and the functions of qualitatively "nonessential" genes will be needed to improve the T7 simulation. In silico representations of biological systems can serve to assess and advance our understanding of the underlying biology. Iteration between computation, prediction, and observation should increase the rate at which biological hypotheses are formulated and tested.

Microbial genomic island discovery, visualization and analysis.

PubMed

Bertelli, Claire; Tilley, Keith E; Brinkman, Fiona S L

2018-06-03

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Computational RNomics of Drosophilids

PubMed Central

Rose, Dominic; Hackermüller, Jörg; Washietl, Stefan; Reiche, Kristin; Hertel, Jana; Findeiß, Sven; Stadler, Peter F; Prohaska, Sonja J

2007-01-01

Background Recent experimental and computational studies have provided overwhelming evidence for a plethora of diverse transcripts that are unrelated to protein-coding genes. One subclass consists of those RNAs that require distinctive secondary structure motifs to exert their biological function and hence exhibit distinctive patterns of sequence conservation characteristic for positive selection on RNA secondary structure. The deep-sequencing of 12 drosophilid species coordinated by the NHGRI provides an ideal data set of comparative computational approaches to determine those genomic loci that code for evolutionarily conserved RNA motifs. This class of loci includes the majority of the known small ncRNAs as well as structured RNA motifs in mRNAs. We report here on a genome-wide survey using RNAz. Results We obtain 16 000 high quality predictions among which we recover the majority of the known ncRNAs. Taking a pessimistically estimated false discovery rate of 40% into account, this implies that at least some ten thousand loci in the Drosophila genome show the hallmarks of stabilizing selection action of RNA structure, and hence are most likely functional at the RNA level. A subset of RNAz predictions overlapping with TRF1 and BRF binding sites [Isogai et al., EMBO J. 26: 79–89 (2007)], which are plausible candidates of Pol III transcripts, have been studied in more detail. Among these sequences we identify several "clusters" of ncRNA candidates with striking structural similarities. Conclusion The statistical evaluation of the RNAz predictions in comparison with a similar analysis of vertebrate genomes [Washietl et al., Nat. Biotech. 23: 1383–1390 (2005)] shows that qualitatively similar fractions of structured RNAs are found in introns, UTRs, and intergenic regions. The intergenic RNA structures, however, are concentrated much more closely around known protein-coding loci, suggesting that flies have significantly smaller complement of independent structured ncRNAs compared to mammals. PMID:17996037

CGAT: a model for immersive personalized training in computational genomics

PubMed Central

Sims, David; Ponting, Chris P.

2016-01-01

How should the next generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity. CGAT was conceived around the twin goals of training future leaders in genome biology and medicine, and providing much needed capacity to UK science for analysing genome scale data sets. Here we outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics. PMID:25981124

Parallel computing in genomic research: advances and applications

PubMed Central

Ocaña, Kary; de Oliveira, Daniel

2015-01-01

Today’s genomic experiments have to process the so-called “biological big data” that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities. PMID:26604801

Parallel computing in genomic research: advances and applications.

PubMed

Ocaña, Kary; de Oliveira, Daniel

2015-01-01

Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities.

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

PubMed

Yazar, Seyhan; Gooden, George E C; Mackey, David A; Hewitt, Alex W

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Benchmarking Undedicated Cloud Computing Providers for Analysis of Genomic Datasets

PubMed Central

Yazar, Seyhan; Gooden, George E. C.; Mackey, David A.; Hewitt, Alex W.

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5–78.2) for E.coli and 53.5% (95% CI: 34.4–72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5–303.1) and 173.9% (95% CI: 134.6–213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE. PMID:25247298

GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing

PubMed Central

Spiliopoulou, Athina; Colombo, Marco; Orchard, Peter; Agakov, Felix; McKeigue, Paul

2017-01-01

We address the task of genotype imputation to a dense reference panel given genotype likelihoods computed from ultralow coverage sequencing as inputs. In this setting, the data have a high-level of missingness or uncertainty, and are thus more amenable to a probabilistic representation. Most existing imputation algorithms are not well suited for this situation, as they rely on prephasing for computational efficiency, and, without definite genotype calls, the prephasing task becomes computationally expensive. We describe GeneImp, a program for genotype imputation that does not require prephasing and is computationally tractable for whole-genome imputation. GeneImp does not explicitly model recombination, instead it capitalizes on the existence of large reference panels—comprising thousands of reference haplotypes—and assumes that the reference haplotypes can adequately represent the target haplotypes over short regions unaltered. We validate GeneImp based on data from ultralow coverage sequencing (0.5×), and compare its performance to the most recent version of BEAGLE that can perform this task. We show that GeneImp achieves imputation quality very close to that of BEAGLE, using one to two orders of magnitude less time, without an increase in memory complexity. Therefore, GeneImp is the first practical choice for whole-genome imputation to a dense reference panel when prephasing cannot be applied, for instance, in datasets produced via ultralow coverage sequencing. A related future application for GeneImp is whole-genome imputation based on the off-target reads from deep whole-exome sequencing. PMID:28348060

Computational Identification of Novel Genes: Current and Future Perspectives.

PubMed

Klasberg, Steffen; Bitard-Feildel, Tristan; Mallet, Ludovic

2016-01-01

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

Cloud Technology May Widen Genomic Bottleneck - TCGA

Cancer.gov

Computational biologist Dr. Ilya Shmulevich suggests that renting cloud computing power might widen the bottleneck for analyzing genomic data. Learn more about his experience with the Cloud in this TCGA in Action Case Study.

Informational laws of genome structures

PubMed Central

Bonnici, Vincenzo; Manca, Vincenzo

2016-01-01

In recent years, the analysis of genomes by means of strings of length k occurring in the genomes, called k-mers, has provided important insights into the basic mechanisms and design principles of genome structures. In the present study, we focus on the proper choice of the value of k for applying information theoretic concepts that express intrinsic aspects of genomes. The value k = lg2(n), where n is the genome length, is determined to be the best choice in the definition of some genomic informational indexes that are studied and computed for seventy genomes. These indexes, which are based on information entropies and on suitable comparisons with random genomes, suggest five informational laws, to which all of the considered genomes obey. Moreover, an informational genome complexity measure is proposed, which is a generalized logistic map that balances entropic and anti-entropic components of genomes and is related to their evolutionary dynamics. Finally, applications to computational synthetic biology are briefly outlined. PMID:27354155

Informational laws of genome structures

NASA Astrophysics Data System (ADS)

Bonnici, Vincenzo; Manca, Vincenzo

2016-06-01

In recent years, the analysis of genomes by means of strings of length k occurring in the genomes, called k-mers, has provided important insights into the basic mechanisms and design principles of genome structures. In the present study, we focus on the proper choice of the value of k for applying information theoretic concepts that express intrinsic aspects of genomes. The value k = lg2(n), where n is the genome length, is determined to be the best choice in the definition of some genomic informational indexes that are studied and computed for seventy genomes. These indexes, which are based on information entropies and on suitable comparisons with random genomes, suggest five informational laws, to which all of the considered genomes obey. Moreover, an informational genome complexity measure is proposed, which is a generalized logistic map that balances entropic and anti-entropic components of genomes and is related to their evolutionary dynamics. Finally, applications to computational synthetic biology are briefly outlined.

Now and next-generation sequencing techniques: future of sequence analysis using cloud computing.

PubMed

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed "cloud computing") has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows.

4P: fast computing of population genetics statistics from large DNA polymorphism panels

PubMed Central

Benazzo, Andrea; Panziera, Alex; Bertorelle, Giorgio

2015-01-01

Massive DNA sequencing has significantly increased the amount of data available for population genetics and molecular ecology studies. However, the parallel computation of simple statistics within and between populations from large panels of polymorphic sites is not yet available, making the exploratory analyses of a set or subset of data a very laborious task. Here, we present 4P (parallel processing of polymorphism panels), a stand-alone software program for the rapid computation of genetic variation statistics (including the joint frequency spectrum) from millions of DNA variants in multiple individuals and multiple populations. It handles a standard input file format commonly used to store DNA variation from empirical or simulation experiments. The computational performance of 4P was evaluated using large SNP (single nucleotide polymorphism) datasets from human genomes or obtained by simulations. 4P was faster or much faster than other comparable programs, and the impact of parallel computing using multicore computers or servers was evident. 4P is a useful tool for biologists who need a simple and rapid computer program to run exploratory population genetics analyses in large panels of genomic data. It is also particularly suitable to analyze multiple data sets produced in simulation studies. Unix, Windows, and MacOs versions are provided, as well as the source code for easier pipeline implementations. PMID:25628874

Bridging the Resolution Gap in Structural Modeling of 3D Genome Organization

PubMed Central

Marti-Renom, Marc A.; Mirny, Leonid A.

2011-01-01

Over the last decade, and especially after the advent of fluorescent in situ hybridization imaging and chromosome conformation capture methods, the availability of experimental data on genome three-dimensional organization has dramatically increased. We now have access to unprecedented details of how genomes organize within the interphase nucleus. Development of new computational approaches to leverage this data has already resulted in the first three-dimensional structures of genomic domains and genomes. Such approaches expand our knowledge of the chromatin folding principles, which has been classically studied using polymer physics and molecular simulations. Our outlook describes computational approaches for integrating experimental data with polymer physics, thereby bridging the resolution gap for structural determination of genomes and genomic domains. PMID:21779160

NGS-based approach to determine the presence of HPV and their sites of integration in human cancer genome.

PubMed

Chandrani, P; Kulkarni, V; Iyer, P; Upadhyay, P; Chaubal, R; Das, P; Mulherkar, R; Singh, R; Dutt, A

2015-06-09

Human papilloma virus (HPV) accounts for the most common cause of all virus-associated human cancers. Here, we describe the first graphic user interface (GUI)-based automated tool 'HPVDetector', for non-computational biologists, exclusively for detection and annotation of the HPV genome based on next-generation sequencing data sets. We developed a custom-made reference genome that comprises of human chromosomes along with annotated genome of 143 HPV types as pseudochromosomes. The tool runs on a dual mode as defined by the user: a 'quick mode' to identify presence of HPV types and an 'integration mode' to determine genomic location for the site of integration. The input data can be a paired-end whole-exome, whole-genome or whole-transcriptome data set. The HPVDetector is available in public domain for download: http://www.actrec.gov.in/pi-webpages/AmitDutt/HPVdetector/HPVDetector.html. On the basis of our evaluation of 116 whole-exome, 23 whole-transcriptome and 2 whole-genome data, we were able to identify presence of HPV in 20 exomes and 4 transcriptomes of cervical and head and neck cancer tumour samples. Using the inbuilt annotation module of HPVDetector, we found predominant integration of viral gene E7, a known oncogene, at known 17q21, 3q27, 7q35, Xq28 and novel sites of integration in the human genome. Furthermore, co-infection with high-risk HPVs such as 16 and 31 were found to be mutually exclusive compared with low-risk HPV71. HPVDetector is a simple yet precise and robust tool for detecting HPV from tumour samples using variety of next-generation sequencing platforms including whole genome, whole exome and transcriptome. Two different modes (quick detection and integration mode) along with a GUI widen the usability of HPVDetector for biologists and clinicians with minimal computational knowledge.

Exploring Other Genomes: Bacteria.

ERIC Educational Resources Information Center

Flannery, Maura C.

2001-01-01

Points out the importance of genomes other than the human genome project and provides information on the identified bacterial genomes Pseudomonas aeuroginosa, Leprosy, Cholera, Meningitis, Tuberculosis, Bubonic Plague, and plant pathogens. Considers the computer's use in genome studies. (Contains 14 references.) (YDS)

Heuristics for the inversion median problem

PubMed Central

2010-01-01

Background The study of genome rearrangements has become a mainstay of phylogenetics and comparative genomics. Fundamental in such a study is the median problem: given three genomes find a fourth that minimizes the sum of the evolutionary distances between itself and the given three. Many exact algorithms and heuristics have been developed for the inversion median problem, of which the best known is MGR. Results We present a unifying framework for median heuristics, which enables us to clarify existing strategies and to place them in a partial ordering. Analysis of this framework leads to a new insight: the best strategies continue to refer to the input data rather than reducing the problem to smaller instances. Using this insight, we develop a new heuristic for inversion medians that uses input data to the end of its computation and leverages our previous work with DCJ medians. Finally, we present the results of extensive experimentation showing that our new heuristic outperforms all others in accuracy and, especially, in running time: the heuristic typically returns solutions within 1% of optimal and runs in seconds to minutes even on genomes with 25'000 genes--in contrast, MGR can take days on instances of 200 genes and cannot be used beyond 1'000 genes. Conclusion Finding good rearrangement medians, in particular inversion medians, had long been regarded as the computational bottleneck in whole-genome studies. Our new heuristic for inversion medians, ASM, which dominates all others in our framework, puts that issue to rest by providing near-optimal solutions within seconds to minutes on even the largest genomes. PMID:20122203

Evolution of epigenetic regulation in vertebrate genomes

PubMed Central

Lowdon, Rebecca F.; Jang, Hyo Sik; Wang, Ting

2016-01-01

Empirical models of sequence evolution have spurred progress in the field of evolutionary genetics for decades. We are now realizing the importance and complexity of the eukaryotic epigenome. While epigenome analysis has been applied to genomes from single cell eukaryotes to human, comparative analyses are still relatively few, and computational algorithms to quantify epigenome evolution remain scarce. Accordingly, a quantitative model of epigenome evolution remains to be established. Here we review the comparative epigenomics literature and synthesize its overarching themes. We also suggest one mechanism, transcription factor binding site turnover, which relates sequence evolution to epigenetic conservation or divergence. Lastly, we propose a framework for how the field can move forward to build a coherent quantitative model of epigenome evolution. PMID:27080453

eHive: An Artificial Intelligence workflow system for genomic analysis

PubMed Central

2010-01-01

Background The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future. Results We present eHive, a new fault tolerant distributed processing system initially designed to support comparative genomic analysis, based on blackboard systems, network distributed autonomous agents, dataflow graphs and block-branch diagrams. In the eHive system a MySQL database serves as the central blackboard and the autonomous agent, a Perl script, queries the system and runs jobs as required. The system allows us to define dataflow and branching rules to suit all our production pipelines. We describe the implementation of three pipelines: (1) pairwise whole genome alignments, (2) multiple whole genome alignments and (3) gene trees with protein homology inference. Finally, we show the efficiency of the system in real case scenarios. Conclusions eHive allows us to produce computationally demanding results in a reliable and efficient way with minimal supervision and high throughput. Further documentation is available at: http://www.ensembl.org/info/docs/eHive/. PMID:20459813

Identification of cis-suppression of human disease mutations by comparative genomics

PubMed Central

Jordan, Daniel M.; Frangakis, Stephan G.; Golzio, Christelle; Cassa, Christopher A.; Kurtzberg, Joanne; Davis, Erica E.; Sunyaev, Shamil R.; Katsanis, Nicholas

2015-01-01

Patterns of amino acid conservation have served as a tool for understanding protein evolution1. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients2. Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes3,4 revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity5,6. PMID:26123021

Influenza Virus Database (IVDB): an integrated information resource and analysis platform for influenza virus research.

PubMed

Chang, Suhua; Zhang, Jiajie; Liao, Xiaoyun; Zhu, Xinxing; Wang, Dahai; Zhu, Jiang; Feng, Tao; Zhu, Baoli; Gao, George F; Wang, Jian; Yang, Huanming; Yu, Jun; Wang, Jing

2007-01-01

Frequent outbreaks of highly pathogenic avian influenza and the increasing data available for comparative analysis require a central database specialized in influenza viruses (IVs). We have established the Influenza Virus Database (IVDB) to integrate information and create an analysis platform for genetic, genomic, and phylogenetic studies of the virus. IVDB hosts complete genome sequences of influenza A virus generated by Beijing Institute of Genomics (BIG) and curates all other published IV sequences after expert annotation. Our Q-Filter system classifies and ranks all nucleotide sequences into seven categories according to sequence content and integrity. IVDB provides a series of tools and viewers for comparative analysis of the viral genomes, genes, genetic polymorphisms and phylogenetic relationships. A search system has been developed for users to retrieve a combination of different data types by setting search options. To facilitate analysis of global viral transmission and evolution, the IV Sequence Distribution Tool (IVDT) has been developed to display the worldwide geographic distribution of chosen viral genotypes and to couple genomic data with epidemiological data. The BLAST, multiple sequence alignment and phylogenetic analysis tools were integrated for online data analysis. Furthermore, IVDB offers instant access to pre-computed alignments and polymorphisms of IV genes and proteins, and presents the results as SNP distribution plots and minor allele distributions. IVDB is publicly available at http://influenza.genomics.org.cn.

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

PubMed

Dessimoz, Christophe; Boeckmann, Brigitte; Roth, Alexander C J; Gonnet, Gaston H

2006-01-01

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

Alu repeat discovery and characterization within human genomes

PubMed Central

Hormozdiari, Fereydoun; Alkan, Can; Ventura, Mario; Hajirasouliha, Iman; Malig, Maika; Hach, Faraz; Yorukoglu, Deniz; Dao, Phuong; Bakhshi, Marzieh; Sahinalp, S. Cenk; Eichler, Evan E.

2011-01-01

Human genomes are now being rapidly sequenced, but not all forms of genetic variation are routinely characterized. In this study, we focus on Alu retrotransposition events and seek to characterize differences in the pattern of mobile insertion between individuals based on the analysis of eight human genomes sequenced using next-generation sequencing. Applying a rapid read-pair analysis algorithm, we discover 4342 Alu insertions not found in the human reference genome and show that 98% of a selected subset (63/64) experimentally validate. Of these new insertions, 89% correspond to AluY elements, suggesting that they arose by retrotransposition. Eighty percent of the Alu insertions have not been previously reported and more novel events were detected in Africans when compared with non-African samples (76% vs. 69%). Using these data, we develop an experimental and computational screen to identify ancestry informative Alu retrotransposition events among different human populations. PMID:21131385

Merlin: Computer-Aided Oligonucleotide Design for Large Scale Genome Engineering with MAGE.

PubMed

Quintin, Michael; Ma, Natalie J; Ahmed, Samir; Bhatia, Swapnil; Lewis, Aaron; Isaacs, Farren J; Densmore, Douglas

2016-06-17

Genome engineering technologies now enable precise manipulation of organism genotype, but can be limited in scalability by their design requirements. Here we describe Merlin ( http://merlincad.org ), an open-source web-based tool to assist biologists in designing experiments using multiplex automated genome engineering (MAGE). Merlin provides methods to generate pools of single-stranded DNA oligonucleotides (oligos) for MAGE experiments by performing free energy calculation and BLAST scoring on a sliding window spanning the targeted site. These oligos are designed not only to improve recombination efficiency, but also to minimize off-target interactions. The application further assists experiment planning by reporting predicted allelic replacement rates after multiple MAGE cycles, and enables rapid result validation by generating primer sequences for multiplexed allele-specific colony PCR. Here we describe the Merlin oligo and primer design procedures and validate their functionality compared to OptMAGE by eliminating seven AvrII restriction sites from the Escherichia coli genome.

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

PubMed

Zhao, Shanrong; Prenger, Kurt; Smith, Lance; Messina, Thomas; Fan, Hongtao; Jaeger, Edward; Stephens, Susan

2013-06-27

Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html.

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing

PubMed Central

2013-01-01

Background Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Results Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Conclusions Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html. PMID:23802613

Computational approaches to identify functional genetic variants in cancer genomes

PubMed Central

Gonzalez-Perez, Abel; Mustonen, Ville; Reva, Boris; Ritchie, Graham R.S.; Creixell, Pau; Karchin, Rachel; Vazquez, Miguel; Fink, J. Lynn; Kassahn, Karin S.; Pearson, John V.; Bader, Gary; Boutros, Paul C.; Muthuswamy, Lakshmi; Ouellette, B.F. Francis; Reimand, Jüri; Linding, Rune; Shibata, Tatsuhiro; Valencia, Alfonso; Butler, Adam; Dronov, Serge; Flicek, Paul; Shannon, Nick B.; Carter, Hannah; Ding, Li; Sander, Chris; Stuart, Josh M.; Stein, Lincoln D.; Lopez-Bigas, Nuria

2014-01-01

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor, but only a minority drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype. PMID:23900255

Memory management in genome-wide association studies

PubMed Central

2009-01-01

Genome-wide association is a powerful tool for the identification of genes that underlie common diseases. Genome-wide association studies generate billions of genotypes and pose significant computational challenges for most users including limited computer memory. We applied a recently developed memory management tool to two analyses of North American Rheumatoid Arthritis Consortium studies and measured the performance in terms of central processing unit and memory usage. We conclude that our memory management approach is simple, efficient, and effective for genome-wide association studies. PMID:20018047

Determination of the Core of a Minimal Bacterial Gene Set†

PubMed Central

Gil, Rosario; Silva, Francisco J.; Peretó, Juli; Moya, Andrés

2004-01-01

The availability of a large number of complete genome sequences raises the question of how many genes are essential for cellular life. Trying to reconstruct the core of the protein-coding gene set for a hypothetical minimal bacterial cell, we have performed a computational comparative analysis of eight bacterial genomes. Six of the analyzed genomes are very small due to a dramatic genome size reduction process, while the other two, corresponding to free-living relatives, are larger. The available data from several systematic experimental approaches to define all the essential genes in some completely sequenced bacterial genomes were also considered, and a reconstruction of a minimal metabolic machinery necessary to sustain life was carried out. The proposed minimal genome contains 206 protein-coding genes with all the genetic information necessary for self-maintenance and reproduction in the presence of a full complement of essential nutrients and in the absence of environmental stress. The main features of such a minimal gene set, as well as the metabolic functions that must be present in the hypothetical minimal cell, are discussed. PMID:15353568

Privacy-preserving GWAS analysis on federated genomic datasets.

PubMed

Constable, Scott D; Tang, Yuzhe; Wang, Shuang; Jiang, Xiaoqian; Chapin, Steve

2015-01-01

The biomedical community benefits from the increasing availability of genomic data to support meaningful scientific research, e.g., Genome-Wide Association Studies (GWAS). However, high quality GWAS usually requires a large amount of samples, which can grow beyond the capability of a single institution. Federated genomic data analysis holds the promise of enabling cross-institution collaboration for effective GWAS, but it raises concerns about patient privacy and medical information confidentiality (as data are being exchanged across institutional boundaries), which becomes an inhibiting factor for the practical use. We present a privacy-preserving GWAS framework on federated genomic datasets. Our method is to layer the GWAS computations on top of secure multi-party computation (MPC) systems. This approach allows two parties in a distributed system to mutually perform secure GWAS computations, but without exposing their private data outside. We demonstrate our technique by implementing a framework for minor allele frequency counting and χ2 statistics calculation, one of typical computations used in GWAS. For efficient prototyping, we use a state-of-the-art MPC framework, i.e., Portable Circuit Format (PCF) 1. Our experimental results show promise in realizing both efficient and secure cross-institution GWAS computations.

Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation.

PubMed

Aguilar, I; Misztal, I; Legarra, A; Tsuruta, S

2011-12-01

Genomic evaluations can be calculated using a unified procedure that combines phenotypic, pedigree and genomic information. Implementation of such a procedure requires the inverse of the relationship matrix based on pedigree and genomic relationships. The objective of this study was to investigate efficient computing options to create relationship matrices based on genomic markers and pedigree information as well as their inverses. SNP maker information was simulated for a panel of 40 K SNPs, with the number of genotyped animals up to 30 000. Matrix multiplication in the computation of the genomic relationship was by a simple 'do' loop, by two optimized versions of the loop, and by a specific matrix multiplication subroutine. Inversion was by a generalized inverse algorithm and by a LAPACK subroutine. With the most efficient choices and parallel processing, creation of matrices for 30 000 animals would take a few hours. Matrices required to implement a unified approach can be computed efficiently. Optimizations can be either by modifications of existing code or by the use of efficient automatic optimizations provided by open source or third-party libraries. © 2011 Blackwell Verlag GmbH.

Perspectives from the Avian Phylogenomics Project: Questions that Can Be Answered with Sequencing All Genomes of a Vertebrate Class.

PubMed

Jarvis, Erich D

2016-01-01

The rapid pace of advances in genome technology, with concomitant reductions in cost, makes it feasible that one day in our lifetime we will have available extant genomes of entire classes of species, including vertebrates. I recently helped cocoordinate the large-scale Avian Phylogenomics Project, which collected and sequenced genomes of 48 bird species representing most currently classified orders to address a range of questions in phylogenomics and comparative genomics. The consortium was able to answer questions not previously possible with just a few genomes. This success spurred on the creation of a project to sequence the genomes of at least one individual of all extant ∼10,500 bird species. The initiation of this project has led us to consider what questions now impossible to answer could be answered with all genomes, and could drive new questions now unimaginable. These include the generation of a highly resolved family tree of extant species, genome-wide association studies across species to identify genetic substrates of many complex traits, redefinition of species and the species concept, reconstruction of the genomes of common ancestors, and generation of new computational tools to address these questions. Here I present visions for the future by posing and answering questions regarding what scientists could potentially do with available genomes of an entire vertebrate class.

CGAT: a model for immersive personalized training in computational genomics.

PubMed

Sims, David; Ponting, Chris P; Heger, Andreas

2016-01-01

How should the next generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity. CGAT was conceived around the twin goals of training future leaders in genome biology and medicine, and providing much needed capacity to UK science for analysing genome scale data sets. Here we outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics. © The Author 2015. Published by Oxford University Press.

Cyber infrastructure for Fusarium: three integrated platforms supporting strain identification, phylogenetics, comparative genomics and knowledge sharing.

PubMed

Park, Bongsoo; Park, Jongsun; Cheong, Kyeong-Chae; Choi, Jaeyoung; Jung, Kyongyong; Kim, Donghan; Lee, Yong-Hwan; Ward, Todd J; O'Donnell, Kerry; Geiser, David M; Kang, Seogchan

2011-01-01

The fungal genus Fusarium includes many plant and/or animal pathogenic species and produces diverse toxins. Although accurate species identification is critical for managing such threats, it is difficult to identify Fusarium morphologically. Fortunately, extensive molecular phylogenetic studies, founded on well-preserved culture collections, have established a robust foundation for Fusarium classification. Genomes of four Fusarium species have been published with more being currently sequenced. The Cyber infrastructure for Fusarium (CiF; http://www.fusariumdb.org/) was built to support archiving and utilization of rapidly increasing data and knowledge and consists of Fusarium-ID, Fusarium Comparative Genomics Platform (FCGP) and Fusarium Community Platform (FCP). The Fusarium-ID archives phylogenetic marker sequences from most known species along with information associated with characterized isolates and supports strain identification and phylogenetic analyses. The FCGP currently archives five genomes from four species. Besides supporting genome browsing and analysis, the FCGP presents computed characteristics of multiple gene families and functional groups. The Cart/Favorite function allows users to collect sequences from Fusarium-ID and the FCGP and analyze them later using multiple tools without requiring repeated copying-and-pasting of sequences. The FCP is designed to serve as an online community forum for sharing and preserving accumulated experience and knowledge to support future research and education.

Cyber infrastructure for Fusarium: three integrated platforms supporting strain identification, phylogenetics, comparative genomics and knowledge sharing

PubMed Central

Park, Bongsoo; Park, Jongsun; Cheong, Kyeong-Chae; Choi, Jaeyoung; Jung, Kyongyong; Kim, Donghan; Lee, Yong-Hwan; Ward, Todd J.; O'Donnell, Kerry; Geiser, David M.; Kang, Seogchan

2011-01-01

The fungal genus Fusarium includes many plant and/or animal pathogenic species and produces diverse toxins. Although accurate species identification is critical for managing such threats, it is difficult to identify Fusarium morphologically. Fortunately, extensive molecular phylogenetic studies, founded on well-preserved culture collections, have established a robust foundation for Fusarium classification. Genomes of four Fusarium species have been published with more being currently sequenced. The Cyber infrastructure for Fusarium (CiF; http://www.fusariumdb.org/) was built to support archiving and utilization of rapidly increasing data and knowledge and consists of Fusarium-ID, Fusarium Comparative Genomics Platform (FCGP) and Fusarium Community Platform (FCP). The Fusarium-ID archives phylogenetic marker sequences from most known species along with information associated with characterized isolates and supports strain identification and phylogenetic analyses. The FCGP currently archives five genomes from four species. Besides supporting genome browsing and analysis, the FCGP presents computed characteristics of multiple gene families and functional groups. The Cart/Favorite function allows users to collect sequences from Fusarium-ID and the FCGP and analyze them later using multiple tools without requiring repeated copying-and-pasting of sequences. The FCP is designed to serve as an online community forum for sharing and preserving accumulated experience and knowledge to support future research and education. PMID:21087991

multi-dice: r package for comparative population genomic inference under hierarchical co-demographic models of independent single-population size changes.

PubMed

Xue, Alexander T; Hickerson, Michael J

2017-11-01

Population genetic data from multiple taxa can address comparative phylogeographic questions about community-scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co-demographic models that directly test multi-taxa hypotheses within a single, unified analysis. This approach has been applied to classical phylogeographic data sets such as mitochondrial barcodes as well as reduced-genome polymorphism data sets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD-seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest and composite likelihood optimization. Here, we introduce the r package multi-dice, a wrapper program that exploits existing simulation software for flexible execution of hierarchical model-based inference using the aSFS, which is derived from reduced genome data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding co-demographic pulses and specifying flexible hyperprior distributions. In sum, multi-dice provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a tool for comparative phylogeography and population genomics. © 2017 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Alignment-free detection of horizontal gene transfer between closely related bacterial genomes.

PubMed

Domazet-Lošo, Mirjana; Haubold, Bernhard

2011-09-01

Bacterial epidemics are often caused by strains that have acquired their increased virulence through horizontal gene transfer. Due to this association with disease, the detection of horizontal gene transfer continues to receive attention from microbiologists and bioinformaticians alike. Most software for detecting transfer events is based on alignments of sets of genes or of entire genomes. But despite great advances in the design of algorithms and computer programs, genome alignment remains computationally challenging. We have therefore developed an alignment-free algorithm for rapidly detecting horizontal gene transfer between closely related bacterial genomes. Our implementation of this algorithm is called alfy for "ALignment Free local homologY" and is freely available from http://guanine.evolbio.mpg.de/alfy/. In this comment we demonstrate the application of alfy to the genomes of Staphylococcus aureus. We also argue that-contrary to popular belief and in spite of increasing computer speed-algorithmic optimization is becoming more, not less, important if genome data continues to accumulate at the present rate.

Calculating orthologs in bacteria and Archaea: a divide and conquer approach.

PubMed

Halachev, Mihail R; Loman, Nicholas J; Pallen, Mark J

2011-01-01

Among proteins, orthologs are defined as those that are derived by vertical descent from a single progenitor in the last common ancestor of their host organisms. Our goal is to compute a complete set of protein orthologs derived from all currently available complete bacterial and archaeal genomes. Traditional approaches typically rely on all-against-all BLAST searching which is prohibitively expensive in terms of hardware requirements or computational time (requiring an estimated 18 months or more on a typical server). Here, we present xBASE-Orth, a system for ongoing ortholog annotation, which applies a "divide and conquer" approach and adopts a pragmatic scheme that trades accuracy for speed. Starting at species level, xBASE-Orth carefully constructs and uses pan-genomes as proxies for the full collections of coding sequences at each level as it progressively climbs the taxonomic tree using the previously computed data. This leads to a significant decrease in the number of alignments that need to be performed, which translates into faster computation, making ortholog computation possible on a global scale. Using xBASE-Orth, we analyzed an NCBI collection of 1,288 bacterial and 94 archaeal complete genomes with more than 4 million coding sequences in 5 weeks and predicted more than 700 million ortholog pairs, clustered in 175,531 orthologous groups. We have also identified sets of highly conserved bacterial and archaeal orthologs and in so doing have highlighted anomalies in genome annotation and in the proposed composition of the minimal bacterial genome. In summary, our approach allows for scalable and efficient computation of the bacterial and archaeal ortholog annotations. In addition, due to its hierarchical nature, it is suitable for incorporating novel complete genomes and alternative genome annotations. The computed ortholog data and a continuously evolving set of applications based on it are integrated in the xBASE database, available at http://www.xbase.ac.uk/.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology.

PubMed

Karp, Peter D; Latendresse, Mario; Paley, Suzanne M; Krummenacker, Markus; Ong, Quang D; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M; Caspi, Ron

2016-09-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

PubMed Central

2012-01-01

Background Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations. PMID:22462519

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

PubMed Central

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed “cloud computing”) has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows. PMID:23248640

The Adoption of Cloud Computing in the Field of Genomics Research: The Influence of Ethical and Legal Issues

PubMed Central

Charlebois, Kathleen; Palmour, Nicole; Knoppers, Bartha Maria

2016-01-01

This study aims to understand the influence of the ethical and legal issues on cloud computing adoption in the field of genomics research. To do so, we adapted Diffusion of Innovation (DoI) theory to enable understanding of how key stakeholders manage the various ethical and legal issues they encounter when adopting cloud computing. Twenty semi-structured interviews were conducted with genomics researchers, patient advocates and cloud service providers. Thematic analysis generated five major themes: 1) Getting comfortable with cloud computing; 2) Weighing the advantages and the risks of cloud computing; 3) Reconciling cloud computing with data privacy; 4) Maintaining trust and 5) Anticipating the cloud by creating the conditions for cloud adoption. Our analysis highlights the tendency among genomics researchers to gradually adopt cloud technology. Efforts made by cloud service providers to promote cloud computing adoption are confronted by researchers’ perpetual cost and security concerns, along with a lack of familiarity with the technology. Further underlying those fears are researchers’ legal responsibility with respect to the data that is stored on the cloud. Alternative consent mechanisms aimed at increasing patients’ control over the use of their data also provide a means to circumvent various institutional and jurisdictional hurdles that restrict access by creating siloed databases. However, the risk of creating new, cloud-based silos may run counter to the goal in genomics research to increase data sharing on a global scale. PMID:27755563

The Adoption of Cloud Computing in the Field of Genomics Research: The Influence of Ethical and Legal Issues.

PubMed

Charlebois, Kathleen; Palmour, Nicole; Knoppers, Bartha Maria

2016-01-01

This study aims to understand the influence of the ethical and legal issues on cloud computing adoption in the field of genomics research. To do so, we adapted Diffusion of Innovation (DoI) theory to enable understanding of how key stakeholders manage the various ethical and legal issues they encounter when adopting cloud computing. Twenty semi-structured interviews were conducted with genomics researchers, patient advocates and cloud service providers. Thematic analysis generated five major themes: 1) Getting comfortable with cloud computing; 2) Weighing the advantages and the risks of cloud computing; 3) Reconciling cloud computing with data privacy; 4) Maintaining trust and 5) Anticipating the cloud by creating the conditions for cloud adoption. Our analysis highlights the tendency among genomics researchers to gradually adopt cloud technology. Efforts made by cloud service providers to promote cloud computing adoption are confronted by researchers' perpetual cost and security concerns, along with a lack of familiarity with the technology. Further underlying those fears are researchers' legal responsibility with respect to the data that is stored on the cloud. Alternative consent mechanisms aimed at increasing patients' control over the use of their data also provide a means to circumvent various institutional and jurisdictional hurdles that restrict access by creating siloed databases. However, the risk of creating new, cloud-based silos may run counter to the goal in genomics research to increase data sharing on a global scale.

Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets

PubMed Central

Heath, Allison P; Greenway, Matthew; Powell, Raymond; Spring, Jonathan; Suarez, Rafael; Hanley, David; Bandlamudi, Chai; McNerney, Megan E; White, Kevin P; Grossman, Robert L

2014-01-01

Background As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Methods Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Results Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Conclusions Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics. PMID:24464852

A distributed system for fast alignment of next-generation sequencing data.

PubMed

Srimani, Jaydeep K; Wu, Po-Yen; Phan, John H; Wang, May D

2010-12-01

We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.

GeNemo: a search engine for web-based functional genomic data.

PubMed

Zhang, Yongqing; Cao, Xiaoyi; Zhong, Sheng

2016-07-08

A set of new data types emerged from functional genomic assays, including ChIP-seq, DNase-seq, FAIRE-seq and others. The results are typically stored as genome-wide intensities (WIG/bigWig files) or functional genomic regions (peak/BED files). These data types present new challenges to big data science. Here, we present GeNemo, a web-based search engine for functional genomic data. GeNemo searches user-input data against online functional genomic datasets, including the entire collection of ENCODE and mouse ENCODE datasets. Unlike text-based search engines, GeNemo's searches are based on pattern matching of functional genomic regions. This distinguishes GeNemo from text or DNA sequence searches. The user can input any complete or partial functional genomic dataset, for example, a binding intensity file (bigWig) or a peak file. GeNemo reports any genomic regions, ranging from hundred bases to hundred thousand bases, from any of the online ENCODE datasets that share similar functional (binding, modification, accessibility) patterns. This is enabled by a Markov Chain Monte Carlo-based maximization process, executed on up to 24 parallel computing threads. By clicking on a search result, the user can visually compare her/his data with the found datasets and navigate the identified genomic regions. GeNemo is available at www.genemo.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

High Throughput Computing Impact on Meta Genomics (Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

ScienceCinema

Gore, Brooklin

2018-02-01

This presentation includes a brief background on High Throughput Computing, correlating gene transcription factors, optical mapping, genotype to phenotype mapping via QTL analysis, and current work on next gen sequencing.

Rapid protein alignment in the cloud: HAMOND combines fast DIAMOND alignments with Hadoop parallelism.

PubMed

Yu, Jia; Blom, Jochen; Sczyrba, Alexander; Goesmann, Alexander

2017-09-10

The introduction of next generation sequencing has caused a steady increase in the amounts of data that have to be processed in modern life science. Sequence alignment plays a key role in the analysis of sequencing data e.g. within whole genome sequencing or metagenome projects. BLAST is a commonly used alignment tool that was the standard approach for more than two decades, but in the last years faster alternatives have been proposed including RapSearch, GHOSTX, and DIAMOND. Here we introduce HAMOND, an application that uses Apache Hadoop to parallelize DIAMOND computation in order to scale-out the calculation of alignments. HAMOND is fault tolerant and scalable by utilizing large cloud computing infrastructures like Amazon Web Services. HAMOND has been tested in comparative genomics analyses and showed promising results both in efficiency and accuracy. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

ReprDB and panDB: minimalist databases with maximal microbial representation.

PubMed

Zhou, Wei; Gay, Nicole; Oh, Julia

2018-01-18

Profiling of shotgun metagenomic samples is hindered by a lack of unified microbial reference genome databases that (i) assemble genomic information from all open access microbial genomes, (ii) have relatively small sizes, and (iii) are compatible to various metagenomic read mapping tools. Moreover, computational tools to rapidly compile and update such databases to accommodate the rapid increase in new reference genomes do not exist. As a result, database-guided analyses often fail to profile a substantial fraction of metagenomic shotgun sequencing reads from complex microbiomes. We report pipelines that efficiently traverse all open access microbial genomes and assemble non-redundant genomic information. The pipelines result in two species-resolution microbial reference databases of relatively small sizes: reprDB, which assembles microbial representative or reference genomes, and panDB, for which we developed a novel iterative alignment algorithm to identify and assemble non-redundant genomic regions in multiple sequenced strains. With the databases, we managed to assign taxonomic labels and genome positions to the majority of metagenomic reads from human skin and gut microbiomes, demonstrating a significant improvement over a previous database-guided analysis on the same datasets. reprDB and panDB leverage the rapid increases in the number of open access microbial genomes to more fully profile metagenomic samples. Additionally, the databases exclude redundant sequence information to avoid inflated storage or memory space and indexing or analyzing time. Finally, the novel iterative alignment algorithm significantly increases efficiency in pan-genome identification and can be useful in comparative genomic analyses.

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José C; Mota-Sanchez, David; Estrada-González, Fermín; Gillberg, Jussi; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin

2018-01-04

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets. Copyright © 2018 Montesinos-Lopez et al.

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems

PubMed Central

Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José C.; Mota-Sanchez, David; Estrada-González, Fermín; Gillberg, Jussi; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin

2018-01-01

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets. PMID:29097376

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation

PubMed Central

Kiefer, Christina; Fehlmann, Tobias; Backes, Christina

2017-01-01

Abstract Metagenomics-based studies of mixed microbial communities are impacting biotechnology, life sciences and medicine. Computational binning of metagenomic data is a powerful approach for the culture-independent recovery of population-resolved genomic sequences, i.e. from individual or closely related, constituent microorganisms. Existing binning solutions often require a priori characterized reference genomes and/or dedicated compute resources. Extending currently available reference-independent binning tools, we developed the BusyBee Web server for the automated deconvolution of metagenomic data into population-level genomic bins using assembled contigs (Illumina) or long reads (Pacific Biosciences, Oxford Nanopore Technologies). A reversible compression step as well as bootstrapped supervised binning enable quick turnaround times. The binning results are represented in interactive 2D scatterplots. Moreover, bin quality estimates, taxonomic annotations and annotations of antibiotic resistance genes are computed and visualized. Ground truth-based benchmarks of BusyBee Web demonstrate comparably high performance to state-of-the-art binning solutions for assembled contigs and markedly improved performance for long reads (median F1 scores: 70.02–95.21%). Furthermore, the applicability to real-world metagenomic datasets is shown. In conclusion, our reference-independent approach automatically bins assembled contigs or long reads, exhibits high sensitivity and precision, enables intuitive inspection of the results, and only requires FASTA-formatted input. The web-based application is freely accessible at: https://ccb-microbe.cs.uni-saarland.de/busybee. PMID:28472498

ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes.

PubMed

Otto, Thomas Dan; Catanho, Marcos; Tristão, Cristian; Bezerra, Márcia; Fernandes, Renan Mathias; Elias, Guilherme Steinberger; Scaglia, Alexandre Capeletto; Bovermann, Bill; Berstis, Viktors; Lifschitz, Sergio; de Miranda, Antonio Basílio; Degrave, Wim

2010-03-01

Many analyses in modern biological research are based on comparisons between biological sequences, resulting in functional, evolutionary and structural inferences. When large numbers of sequences are compared, heuristics are often used resulting in a certain lack of accuracy. In order to improve and validate results of such comparisons, we have performed radical all-against-all comparisons of 4 million protein sequences belonging to the RefSeq database, using an implementation of the Smith-Waterman algorithm. This extremely intensive computational approach was made possible with the help of World Community Grid, through the Genome Comparison Project. The resulting database, ProteinWorldDB, which contains coordinates of pairwise protein alignments and their respective scores, is now made available. Users can download, compare and analyze the results, filtered by genomes, protein functions or clusters. ProteinWorldDB is integrated with annotations derived from Swiss-Prot, Pfam, KEGG, NCBI Taxonomy database and gene ontology. The database is a unique and valuable asset, representing a major effort to create a reliable and consistent dataset of cross-comparisons of the whole protein content encoded in hundreds of completely sequenced genomes using a rigorous dynamic programming approach. The database can be accessed through http://proteinworlddb.org

Computational approach for elucidating interactions of cross-species miRNAs and their targets in Flaviviruses.

PubMed

Shinde, Santosh P; Banerjee, Amit Kumar; Arora, Neelima; Murty, U S N; Sripathi, Venkateswara Rao; Pal-Bhadra, Manika; Bhadra, Utpal

2015-03-01

Combating viral diseases has been a challenging task since time immemorial. Available molecular approaches are limited and not much effective for this daunting task. MicroRNA based therapies have shown promise in recent times. MicroRNAs are tiny non-coding RNAs that regulate translational repression of target mRNA in highly specific manner. In this study, we have determined the target regions for human and viral microRNAs in the conserved genomic regions of selected viruses of Flaviviridae family using miRanda and performed a comparative target selectivity analysis among them. Specific target regions were determined and they were compared extensively among themselves by exploring their position to determine the vicinity. Based on the multiplicity and cooperativity analysis, interaction maps were developed manually to represent the interactions between top-ranking miRNAs and genomes of the viruses considered in this study. Self-organizing map (SOM) was used to cluster the best-ranked microRNAs based on the vital physicochemical properties. This study will provide deep insight into the interrelation of the viral and human microRNAs interactions with the selected Flaviviridae genomes and will help to identify cross-species microRNA targets on the viral genome.

Group normalization for genomic data.

PubMed

Ghandi, Mahmoud; Beer, Michael A

2012-01-01

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets.

Group Normalization for Genomic Data

PubMed Central

Ghandi, Mahmoud; Beer, Michael A.

2012-01-01

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets. PMID:22912661

Identification of 15 candidate structured noncoding RNA motifs in fungi by comparative genomics.

PubMed

Li, Sanshu; Breaker, Ronald R

2017-10-13

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

BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons

PubMed Central

2011-01-01

Background Visualisation of genome comparisons is invaluable for helping to determine genotypic differences between closely related prokaryotes. New visualisation and abstraction methods are required in order to improve the validation, interpretation and communication of genome sequence information; especially with the increasing amount of data arising from next-generation sequencing projects. Visualising a prokaryote genome as a circular image has become a powerful means of displaying informative comparisons of one genome to a number of others. Several programs, imaging libraries and internet resources already exist for this purpose, however, most are either limited in the number of comparisons they can show, are unable to adequately utilise draft genome sequence data, or require a knowledge of command-line scripting for implementation. Currently, there is no freely available desktop application that enables users to rapidly visualise comparisons between hundreds of draft or complete genomes in a single image. Results BLAST Ring Image Generator (BRIG) can generate images that show multiple prokaryote genome comparisons, without an arbitrary limit on the number of genomes compared. The output image shows similarity between a central reference sequence and other sequences as a set of concentric rings, where BLAST matches are coloured on a sliding scale indicating a defined percentage identity. Images can also include draft genome assembly information to show read coverage, assembly breakpoints and collapsed repeats. In addition, BRIG supports the mapping of unassembled sequencing reads against one or more central reference sequences. Many types of custom data and annotations can be shown using BRIG, making it a versatile approach for visualising a range of genomic comparison data. BRIG is readily accessible to any user, as it assumes no specialist computational knowledge and will perform all required file parsing and BLAST comparisons automatically. Conclusions There is a clear need for a user-friendly program that can produce genome comparisons for a large number of prokaryote genomes with an emphasis on rapidly utilising unfinished or unassembled genome data. Here we present BRIG, a cross-platform application that enables the interactive generation of comparative genomic images via a simple graphical-user interface. BRIG is freely available for all operating systems at http://sourceforge.net/projects/brig/. PMID:21824423

BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons.

PubMed

Alikhan, Nabil-Fareed; Petty, Nicola K; Ben Zakour, Nouri L; Beatson, Scott A

2011-08-08

Visualisation of genome comparisons is invaluable for helping to determine genotypic differences between closely related prokaryotes. New visualisation and abstraction methods are required in order to improve the validation, interpretation and communication of genome sequence information; especially with the increasing amount of data arising from next-generation sequencing projects. Visualising a prokaryote genome as a circular image has become a powerful means of displaying informative comparisons of one genome to a number of others. Several programs, imaging libraries and internet resources already exist for this purpose, however, most are either limited in the number of comparisons they can show, are unable to adequately utilise draft genome sequence data, or require a knowledge of command-line scripting for implementation. Currently, there is no freely available desktop application that enables users to rapidly visualise comparisons between hundreds of draft or complete genomes in a single image. BLAST Ring Image Generator (BRIG) can generate images that show multiple prokaryote genome comparisons, without an arbitrary limit on the number of genomes compared. The output image shows similarity between a central reference sequence and other sequences as a set of concentric rings, where BLAST matches are coloured on a sliding scale indicating a defined percentage identity. Images can also include draft genome assembly information to show read coverage, assembly breakpoints and collapsed repeats. In addition, BRIG supports the mapping of unassembled sequencing reads against one or more central reference sequences. Many types of custom data and annotations can be shown using BRIG, making it a versatile approach for visualising a range of genomic comparison data. BRIG is readily accessible to any user, as it assumes no specialist computational knowledge and will perform all required file parsing and BLAST comparisons automatically. There is a clear need for a user-friendly program that can produce genome comparisons for a large number of prokaryote genomes with an emphasis on rapidly utilising unfinished or unassembled genome data. Here we present BRIG, a cross-platform application that enables the interactive generation of comparative genomic images via a simple graphical-user interface. BRIG is freely available for all operating systems at http://sourceforge.net/projects/brig/.

Biological and Environmental Research Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Biological and Environmental Research, March 28-31, 2016, Rockville, Maryland

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

Arkin, Adam; Bader, David C.; Coffey, Richard

Understanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOEmore » began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.« less

A Genomic Resource for the Development, Improvement, and Exploitation of Sorghum for Bioenergy

PubMed Central

Brenton, Zachary W.; Cooper, Elizabeth A.; Myers, Mathew T.; Boyles, Richard E.; Shakoor, Nadia; Zielinski, Kelsey J.; Rauh, Bradley L.; Bridges, William C.; Morris, Geoffrey P.; Kresovich, Stephen

2016-01-01

With high productivity and stress tolerance, numerous grass genera of the Andropogoneae have emerged as candidates for bioenergy production. To optimize these candidates, research examining the genetic architecture of yield, carbon partitioning, and composition is required to advance breeding objectives. Significant progress has been made developing genetic and genomic resources for Andropogoneae, and advances in comparative and computational genomics have enabled research examining the genetic basis of photosynthesis, carbon partitioning, composition, and sink strength. To provide a pivotal resource aimed at developing a comparative understanding of key bioenergy traits in the Andropogoneae, we have established and characterized an association panel of 390 racially, geographically, and phenotypically diverse Sorghum bicolor accessions with 232,303 genetic markers. Sorghum bicolor was selected because of its genomic simplicity, phenotypic diversity, significant genomic tools, and its agricultural productivity and resilience. We have demonstrated the value of sorghum as a functional model for candidate gene discovery for bioenergy Andropogoneae by performing genome-wide association analysis for two contrasting phenotypes representing key components of structural and non-structural carbohydrates. We identified potential genes, including a cellulase enzyme and a vacuolar transporter, associated with increased non-structural carbohydrates that could lead to bioenergy sorghum improvement. Although our analysis identified genes with potentially clear functions, other candidates did not have assigned functions, suggesting novel molecular mechanisms for carbon partitioning traits. These results, combined with our characterization of phenotypic and genetic diversity and the public accessibility of each accession and genomic data, demonstrate the value of this resource and provide a foundation for future improvement of sorghum and related grasses for bioenergy production. PMID:27356613

Mitochondrial Genome Sequence of the Legume Vicia faba

PubMed Central

Negruk, Valentine

2013-01-01

The number of plant mitochondrial genomes sequenced exceeds two dozen. However, for a detailed comparative study of different phylogenetic branches more plant mitochondrial genomes should be sequenced. This article presents sequencing data and comparative analysis of mitochondrial DNA (mtDNA) of the legume Vicia faba. The size of the V. faba circular mitochondrial master chromosome of cultivar Broad Windsor was estimated as 588,000 bp with a genome complexity of 387,745 bp and 52 conservative mitochondrial genes; 32 of them encoding proteins, 3 rRNA, and 17 tRNA genes. Six tRNA genes were highly homologous to chloroplast genome sequences. In addition to the 52 conservative genes, 114 unique open reading frames (ORFs) were found, 36 without significant homology to any known proteins and 29 with homology to the Medicago truncatula nuclear genome and to other plant mitochondrial ORFs, 49 ORFs were not homologous to M. truncatula but possessed sequences with significant homology to other plant mitochondrial or nuclear ORFs. In general, the unique ORFs revealed very low homology to known closely related legumes, but several sequence homologies were found between V. faba, Beta vulgaris, Nicotiana tabacum, Vitis vinifera, and even the monocots Oryza sativa and Zea mays. Most likely these ORFs arose independently during angiosperm evolution (Kubo and Mikami, 2007; Kubo and Newton, 2008). Computational analysis revealed in total about 45% of V. faba mtDNA sequence being homologous to the Medicago truncatula nuclear genome (more than to any sequenced plant mitochondrial genome), and 35% of this homology ranging from a few dozen to 12,806 bp are located on chromosome 1. Apparently, mitochondrial rrn5, rrn18, rps10, ATP synthase subunit alpha, cox2, and tRNA sequences are part of transcribed nuclear mosaic ORFs. PMID:23675376

Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing

PubMed Central

2012-01-01

Background Compared to classical genotyping, targeted next-generation sequencing (tNGS) can be custom-designed to interrogate entire genomic regions of interest, in order to detect novel as well as known variants. To bring down the per-sample cost, one approach is to pool barcoded NGS libraries before sample enrichment. Still, we lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can affect the quality of variant discovery. Therefore, we evaluated the impact of different software tools and analytical approaches on the discovery of single nucleotide polymorphisms (SNPs) in multiplexed tNGS data. To generate our own test model, we combined a sequence capture method with NGS in three experimental stages of increasing complexity (E. coli genes, multiplexed E. coli, and multiplexed HapMap BRCA1/2 regions). Results We successfully enriched barcoded NGS libraries instead of genomic DNA, achieving reproducible coverage profiles (Pearson correlation coefficients of up to 0.99) across multiplexed samples, with <10% strand bias. However, the SNP calling quality was substantially affected by the choice of tools and mapping strategy. With the aim of reducing computational requirements, we compared conventional whole-genome mapping and SNP-calling with a new faster approach: target-region mapping with subsequent ‘read-backmapping’ to the whole genome to reduce the false detection rate. Consequently, we developed a combined mapping pipeline, which includes standard tools (BWA, SAMtools, etc.), and tested it on public HiSeq2000 exome data from the 1000 Genomes Project. Our pipeline saved 12 hours of run time per Hiseq2000 exome sample and detected ~5% more SNPs than the conventional whole genome approach. This suggests that more potential novel SNPs may be discovered using both approaches than with just the conventional approach. Conclusions We recommend applying our general ‘two-step’ mapping approach for more efficient SNP discovery in tNGS. Our study has also shown the benefit of computing inter-sample SNP-concordances and inspecting read alignments in order to attain more confident results. PMID:22913592

Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks.

PubMed

Wang, Edwin; Zou, Jinfeng; Zaman, Naif; Beitel, Lenore K; Trifiro, Mark; Paliouras, Miltiadis

2013-08-01

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Savant Genome Browser 2: visualization and analysis for population-scale genomics.

PubMed

Fiume, Marc; Smith, Eric J M; Brook, Andrew; Strbenac, Dario; Turner, Brian; Mezlini, Aziz M; Robinson, Mark D; Wodak, Shoshana J; Brudno, Michael

2012-07-01

High-throughput sequencing (HTS) technologies are providing an unprecedented capacity for data generation, and there is a corresponding need for efficient data exploration and analysis capabilities. Although most existing tools for HTS data analysis are developed for either automated (e.g. genotyping) or visualization (e.g. genome browsing) purposes, such tools are most powerful when combined. For example, integration of visualization and computation allows users to iteratively refine their analyses by updating computational parameters within the visual framework in real-time. Here we introduce the second version of the Savant Genome Browser, a standalone program for visual and computational analysis of HTS data. Savant substantially improves upon its predecessor and existing tools by introducing innovative visualization modes and navigation interfaces for several genomic datatypes, and synergizing visual and automated analyses in a way that is powerful yet easy even for non-expert users. We also present a number of plugins that were developed by the Savant Community, which demonstrate the power of integrating visual and automated analyses using Savant. The Savant Genome Browser is freely available (open source) at www.savantbrowser.com.

Savant Genome Browser 2: visualization and analysis for population-scale genomics

PubMed Central

Smith, Eric J. M.; Brook, Andrew; Strbenac, Dario; Turner, Brian; Mezlini, Aziz M.; Robinson, Mark D.; Wodak, Shoshana J.; Brudno, Michael

2012-01-01

High-throughput sequencing (HTS) technologies are providing an unprecedented capacity for data generation, and there is a corresponding need for efficient data exploration and analysis capabilities. Although most existing tools for HTS data analysis are developed for either automated (e.g. genotyping) or visualization (e.g. genome browsing) purposes, such tools are most powerful when combined. For example, integration of visualization and computation allows users to iteratively refine their analyses by updating computational parameters within the visual framework in real-time. Here we introduce the second version of the Savant Genome Browser, a standalone program for visual and computational analysis of HTS data. Savant substantially improves upon its predecessor and existing tools by introducing innovative visualization modes and navigation interfaces for several genomic datatypes, and synergizing visual and automated analyses in a way that is powerful yet easy even for non-expert users. We also present a number of plugins that were developed by the Savant Community, which demonstrate the power of integrating visual and automated analyses using Savant. The Savant Genome Browser is freely available (open source) at www.savantbrowser.com. PMID:22638571

COMPUTATIONAL RESOURCES FOR BIOFUEL FEEDSTOCK SPECIES

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

Buell, Carol Robin; Childs, Kevin L

2013-05-07

While current production of ethanol as a biofuel relies on starch and sugar inputs, it is anticipated that sustainable production of ethanol for biofuel use will utilize lignocellulosic feedstocks. Candidate plant species to be used for lignocellulosic ethanol production include a large number of species within the Grass, Pine and Birch plant families. For these biofuel feedstock species, there are variable amounts of genome sequence resources available, ranging from complete genome sequences (e.g. sorghum, poplar) to transcriptome data sets (e.g. switchgrass, pine). These data sets are not only dispersed in location but also disparate in content. It will be essentialmore » to leverage and improve these genomic data sets for the improvement of biofuel feedstock production. The objectives of this project were to provide computational tools and resources for data-mining genome sequence/annotation and large-scale functional genomic datasets available for biofuel feedstock species. We have created a Bioenergy Feedstock Genomics Resource that provides a web-based portal or clearing house for genomic data for plant species relevant to biofuel feedstock production. Sequence data from a total of 54 plant species are included in the Bioenergy Feedstock Genomics Resource including model plant species that permit leveraging of knowledge across taxa to biofuel feedstock species.We have generated additional computational analyses of these data, including uniform annotation, to facilitate genomic approaches to improved biofuel feedstock production. These data have been centralized in the publicly available Bioenergy Feedstock Genomics Resource (http://bfgr.plantbiology.msu.edu/).« less

Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets.

PubMed

Heath, Allison P; Greenway, Matthew; Powell, Raymond; Spring, Jonathan; Suarez, Rafael; Hanley, David; Bandlamudi, Chai; McNerney, Megan E; White, Kevin P; Grossman, Robert L

2014-01-01

As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it. Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required. Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample. Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

PEPIS: A Pipeline for Estimating Epistatic Effects in Quantitative Trait Locus Mapping and Genome-Wide Association Studies.

PubMed

Zhang, Wenchao; Dai, Xinbin; Wang, Qishan; Xu, Shizhong; Zhao, Patrick X

2016-05-01

The term epistasis refers to interactions between multiple genetic loci. Genetic epistasis is important in regulating biological function and is considered to explain part of the 'missing heritability,' which involves marginal genetic effects that cannot be accounted for in genome-wide association studies. Thus, the study of epistasis is of great interest to geneticists. However, estimating epistatic effects for quantitative traits is challenging due to the large number of interaction effects that must be estimated, thus significantly increasing computing demands. Here, we present a new web server-based tool, the Pipeline for estimating EPIStatic genetic effects (PEPIS), for analyzing polygenic epistatic effects. The PEPIS software package is based on a new linear mixed model that has been used to predict the performance of hybrid rice. The PEPIS includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. To accommodate the demand for high-performance computation, the PEPIS utilizes C/C++ for mathematical matrix computing. In addition, the modules for kinship matrix calculations and main and epistatic-effect genome scanning employ parallel computing technology that effectively utilizes multiple computer nodes across our networked cluster, thus significantly improving the computational speed. For example, when analyzing the same immortalized F2 rice population genotypic data examined in a previous study, the PEPIS returned identical results at each analysis step with the original prototype R code, but the computational time was reduced from more than one month to about five minutes. These advances will help overcome the bottleneck frequently encountered in genome wide epistatic genetic effect analysis and enable accommodation of the high computational demand. The PEPIS is publically available at http://bioinfo.noble.org/PolyGenic_QTL/.

Accounting for discovery bias in genomic EPD

USDA-ARS?s Scientific Manuscript database

Genomics has contributed substantially to genetic improvement of beef cattle. The implementation is through computation of genomically enhanced expected progeny differences (GE-EPD), which are predictions of genetic merit of individual animals based on genomic information, pedigree, and data on the ...

Genome assembly reborn: recent computational challenges

PubMed Central

2009-01-01

Research into genome assembly algorithms has experienced a resurgence due to new challenges created by the development of next generation sequencing technologies. Several genome assemblers have been published in recent years specifically targeted at the new sequence data; however, the ever-changing technological landscape leads to the need for continued research. In addition, the low cost of next generation sequencing data has led to an increased use of sequencing in new settings. For example, the new field of metagenomics relies on large-scale sequencing of entire microbial communities instead of isolate genomes, leading to new computational challenges. In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain. PMID:19482960

GAPIT: genome association and prediction integrated tool.

PubMed

Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

2012-09-15

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

Multiple Phenotype Association Tests Using Summary Statistics in Genome-Wide Association Studies

PubMed Central

Liu, Zhonghua; Lin, Xihong

2017-01-01

Summary We study in this paper jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis. PMID:28653391

Multiple phenotype association tests using summary statistics in genome-wide association studies.

PubMed

Liu, Zhonghua; Lin, Xihong

2018-03-01

We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis. © 2017, The International Biometric Society.

Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads

PubMed Central

Schröder, Jan; Hsu, Arthur; Boyle, Samantha E.; Macintyre, Geoff; Cmero, Marek; Tothill, Richard W.; Johnstone, Ricky W.; Shackleton, Mark; Papenfuss, Anthony T.

2014-01-01

Motivation: Methods for detecting somatic genome rearrangements in tumours using next-generation sequencing are vital in cancer genomics. Available algorithms use one or more sources of evidence, such as read depth, paired-end reads or split reads to predict structural variants. However, the problem remains challenging due to the significant computational burden and high false-positive or false-negative rates. Results: In this article, we present Socrates (SOft Clip re-alignment To idEntify Structural variants), a highly efficient and effective method for detecting genomic rearrangements in tumours that uses only split-read data. Socrates has single-nucleotide resolution, identifies micro-homologies and untemplated sequence at break points, has high sensitivity and high specificity and takes advantage of parallelism for efficient use of resources. We demonstrate using simulated and real data that Socrates performs well compared with a number of existing structural variant detection tools. Availability and implementation: Socrates is released as open source and available from http://bioinf.wehi.edu.au/socrates. Contact: papenfuss@wehi.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24389656

PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

NASA Astrophysics Data System (ADS)

Deneke, Carlus; Rentzsch, Robert; Renard, Bernhard Y.

2017-01-01

The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results.

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption

PubMed Central

2015-01-01

Background The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. Methods We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. Results The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. Conclusions Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics. PMID:26733391

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption.

PubMed

Zhang, Yuchen; Dai, Wenrui; Jiang, Xiaoqian; Xiong, Hongkai; Wang, Shuang

2015-01-01

The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

The aquatic animals' transcriptome resource for comparative functional analysis.

PubMed

Chou, Chih-Hung; Huang, Hsi-Yuan; Huang, Wei-Chih; Hsu, Sheng-Da; Hsiao, Chung-Der; Liu, Chia-Yu; Chen, Yu-Hung; Liu, Yu-Chen; Huang, Wei-Yun; Lee, Meng-Lin; Chen, Yi-Chang; Huang, Hsien-Da

2018-05-09

Aquatic animals have great economic and ecological importance. Among them, non-model organisms have been studied regarding eco-toxicity, stress biology, and environmental adaptation. Due to recent advances in next-generation sequencing techniques, large amounts of RNA-seq data for aquatic animals are publicly available. However, currently there is no comprehensive resource exist for the analysis, unification, and integration of these datasets. This study utilizes computational approaches to build a new resource of transcriptomic maps for aquatic animals. This aquatic animal transcriptome map database dbATM provides de novo assembly of transcriptome, gene annotation and comparative analysis of more than twenty aquatic organisms without draft genome. To improve the assembly quality, three computational tools (Trinity, Oases and SOAPdenovo-Trans) were employed to enhance individual transcriptome assembly, and CAP3 and CD-HIT-EST software were then used to merge these three assembled transcriptomes. In addition, functional annotation analysis provides valuable clues to gene characteristics, including full-length transcript coding regions, conserved domains, gene ontology and KEGG pathways. Furthermore, all aquatic animal genes are essential for comparative genomics tasks such as constructing homologous gene groups and blast databases and phylogenetic analysis. In conclusion, we establish a resource for non model organism aquatic animals, which is great economic and ecological importance and provide transcriptomic information including functional annotation and comparative transcriptome analysis. The database is now publically accessible through the URL http://dbATM.mbc.nctu.edu.tw/ .

Toward mapping the biology of the genome.

PubMed

Chanock, Stephen

2012-09-01

This issue of Genome Research presents new results, methods, and tools from The ENCODE Project (ENCyclopedia of DNA Elements), which collectively represents an important step in moving beyond a parts list of the genome and promises to shape the future of genomic research. This collection sheds light on basic biological questions and frames the current debate over the optimization of tools and methodological challenges necessary to compare and interpret large complex data sets focused on how the genome is organized and regulated. In a number of instances, the authors have highlighted the strengths and limitations of current computational and technical approaches, providing the community with useful standards, which should stimulate development of new tools. In many ways, these papers will ripple through the scientific community, as those in pursuit of understanding the "regulatory genome" will heavily traverse the maps and tools. Similarly, the work should have a substantive impact on how genetic variation contributes to specific diseases and traits by providing a compendium of functional elements for follow-up study. The success of these papers should not only be measured by the scope of the scientific insights and tools but also by their ability to attract new talent to mine existing and future data.

Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment

PubMed Central

Kam, Alfred; Kwak, Daniel; Leung, Clarence; Wu, Chu; Zarour, Eleyine; Sarmenta, Luis; Blanchette, Mathieu; Waldispühl, Jérôme

2012-01-01

Background Comparative genomics, or the study of the relationships of genome structure and function across different species, offers a powerful tool for studying evolution, annotating genomes, and understanding the causes of various genetic disorders. However, aligning multiple sequences of DNA, an essential intermediate step for most types of analyses, is a difficult computational task. In parallel, citizen science, an approach that takes advantage of the fact that the human brain is exquisitely tuned to solving specific types of problems, is becoming increasingly popular. There, instances of hard computational problems are dispatched to a crowd of non-expert human game players and solutions are sent back to a central server. Methodology/Principal Findings We introduce Phylo, a human-based computing framework applying “crowd sourcing” techniques to solve the Multiple Sequence Alignment (MSA) problem. The key idea of Phylo is to convert the MSA problem into a casual game that can be played by ordinary web users with a minimal prior knowledge of the biological context. We applied this strategy to improve the alignment of the promoters of disease-related genes from up to 44 vertebrate species. Since the launch in November 2010, we received more than 350,000 solutions submitted from more than 12,000 registered users. Our results show that solutions submitted contributed to improving the accuracy of up to 70% of the alignment blocks considered. Conclusions/Significance We demonstrate that, combined with classical algorithms, crowd computing techniques can be successfully used to help improving the accuracy of MSA. More importantly, we show that an NP-hard computational problem can be embedded in casual game that can be easily played by people without significant scientific training. This suggests that citizen science approaches can be used to exploit the billions of “human-brain peta-flops” of computation that are spent every day playing games. Phylo is available at: http://phylo.cs.mcgill.ca. PMID:22412834

Long Read Alignment with Parallel MapReduce Cloud Platform

PubMed Central

Al-Absi, Ahmed Abdulhakim; Kang, Dae-Ki

2015-01-01

Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner's Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms. PMID:26839887

Long Read Alignment with Parallel MapReduce Cloud Platform.

PubMed

Al-Absi, Ahmed Abdulhakim; Kang, Dae-Ki

2015-01-01

Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner's Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms.

in silico Whole Genome Sequencer & Analyzer (iWGS): a computational pipeline to guide the design and analysis of de novo genome sequencing studies

USDA-ARS?s Scientific Manuscript database

The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with relatively small and simple genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding it...

Real-Time Pathogen Detection in the Era of Whole-Genome Sequencing and Big Data: Comparison of k-mer and Site-Based Methods for Inferring the Genetic Distances among Tens of Thousands of Salmonella Samples.

PubMed

Pettengill, James B; Pightling, Arthur W; Baugher, Joseph D; Rand, Hugh; Strain, Errol

2016-01-01

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging due to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). When analyzing empirical data (whole-genome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency.

PubMed

Aniceto, Rodrigo; Xavier, Rene; Guimarães, Valeria; Hondo, Fernanda; Holanda, Maristela; Walter, Maria Emilia; Lifschitz, Sérgio

2015-01-01

Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

An improved model for whole genome phylogenetic analysis by Fourier transform.

PubMed

Yin, Changchuan; Yau, Stephen S-T

2015-10-07

DNA sequence similarity comparison is one of the major steps in computational phylogenetic studies. The sequence comparison of closely related DNA sequences and genomes is usually performed by multiple sequence alignments (MSA). While the MSA method is accurate for some types of sequences, it may produce incorrect results when DNA sequences undergone rearrangements as in many bacterial and viral genomes. It is also limited by its computational complexity for comparing large volumes of data. Previously, we proposed an alignment-free method that exploits the full information contents of DNA sequences by Discrete Fourier Transform (DFT), but still with some limitations. Here, we present a significantly improved method for the similarity comparison of DNA sequences by DFT. In this method, we map DNA sequences into 2-dimensional (2D) numerical sequences and then apply DFT to transform the 2D numerical sequences into frequency domain. In the 2D mapping, the nucleotide composition of a DNA sequence is a determinant factor and the 2D mapping reduces the nucleotide composition bias in distance measure, and thus improving the similarity measure of DNA sequences. To compare the DFT power spectra of DNA sequences with different lengths, we propose an improved even scaling algorithm to extend shorter DFT power spectra to the longest length of the underlying sequences. After the DFT power spectra are evenly scaled, the spectra are in the same dimensionality of the Fourier frequency space, then the Euclidean distances of full Fourier power spectra of the DNA sequences are used as the dissimilarity metrics. The improved DFT method, with increased computational performance by 2D numerical representation, can be applicable to any DNA sequences of different length ranges. We assess the accuracy of the improved DFT similarity measure in hierarchical clustering of different DNA sequences including simulated and real datasets. The method yields accurate and reliable phylogenetic trees and demonstrates that the improved DFT dissimilarity measure is an efficient and effective similarity measure of DNA sequences. Due to its high efficiency and accuracy, the proposed DFT similarity measure is successfully applied on phylogenetic analysis for individual genes and large whole bacterial genomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Genome alignment with graph data structures: a comparison

PubMed Central

2014-01-01

Background Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference. Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment. Results We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures. Conclusion We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools. PMID:24712884

GEMINI: a computationally-efficient search engine for large gene expression datasets.

PubMed

DeFreitas, Timothy; Saddiki, Hachem; Flaherty, Patrick

2016-02-24

Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile. To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an [Formula: see text] expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 10(5) samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.

Estimating true evolutionary distances under the DCJ model.

PubMed

Lin, Yu; Moret, Bernard M E

2008-07-01

Modern techniques can yield the ordering and strandedness of genes on each chromosome of a genome; such data already exists for hundreds of organisms. The evolutionary mechanisms through which the set of the genes of an organism is altered and reordered are of great interest to systematists, evolutionary biologists, comparative genomicists and biomedical researchers. Perhaps the most basic concept in this area is that of evolutionary distance between two genomes: under a given model of genomic evolution, how many events most likely took place to account for the difference between the two genomes? We present a method to estimate the true evolutionary distance between two genomes under the 'double-cut-and-join' (DCJ) model of genome rearrangement, a model under which a single multichromosomal operation accounts for all genomic rearrangement events: inversion, transposition, translocation, block interchange and chromosomal fusion and fission. Our method relies on a simple structural characterization of a genome pair and is both analytically and computationally tractable. We provide analytical results to describe the asymptotic behavior of genomes under the DCJ model, as well as experimental results on a wide variety of genome structures to exemplify the very high accuracy (and low variance) of our estimator. Our results provide a tool for accurate phylogenetic reconstruction from multichromosomal gene rearrangement data as well as a theoretical basis for refinements of the DCJ model to account for biological constraints. All of our software is available in source form under GPL at http://lcbb.epfl.ch.

Use of qualitative environmental and phenotypic variables in the context of allele distribution models: detecting signatures of selection in the genome of Lake Victoria cichlids.

PubMed

Joost, Stéphane; Kalbermatten, Michael; Bezault, Etienne; Seehausen, Ole

2012-01-01

When searching for loci possibly under selection in the genome, an alternative to population genetics theoretical models is to establish allele distribution models (ADM) for each locus to directly correlate allelic frequencies and environmental variables such as precipitation, temperature, or sun radiation. Such an approach implementing multiple logistic regression models in parallel was implemented within a computing program named MATSAM: . Recently, this application was improved in order to support qualitative environmental predictors as well as to permit the identification of associations between genomic variation and individual phenotypes, allowing the detection of loci involved in the genetic architecture of polymorphic characters. Here, we present the corresponding methodological developments and compare the results produced by software implementing population genetics theoretical models (DFDIST: and BAYESCAN: ) and ADM (MATSAM: ) in an empirical context to detect signatures of genomic divergence associated with speciation in Lake Victoria cichlid fishes.

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.

PubMed

Yang, Laurence; Tan, Justin; O'Brien, Edward J; Monk, Jonathan M; Kim, Donghyuk; Li, Howard J; Charusanti, Pep; Ebrahim, Ali; Lloyd, Colton J; Yurkovich, James T; Du, Bin; Dräger, Andreas; Thomas, Alex; Sun, Yuekai; Saunders, Michael A; Palsson, Bernhard O

2015-08-25

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

Atlas2 Cloud: a framework for personal genome analysis in the cloud

PubMed Central

2012-01-01

Background Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. Results We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. Conclusions We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms. PMID:23134663

Atlas2 Cloud: a framework for personal genome analysis in the cloud.

PubMed

Evani, Uday S; Challis, Danny; Yu, Jin; Jackson, Andrew R; Paithankar, Sameer; Bainbridge, Matthew N; Jakkamsetti, Adinarayana; Pham, Peter; Coarfa, Cristian; Milosavljevic, Aleksandar; Yu, Fuli

2012-01-01

Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms.

Genomic predictions for crossbreds from all-breed data

USDA-ARS?s Scientific Manuscript database

Genomic predictions of transmitting ability (GPTAs) for crossbred animals were computed from marker effects of 5 dairy breeds weighted by each breed’s genomic contribution to the crossbreds. Estimates of genomic breed composition are labeled breed base representation (BBR) and are reported since May...

Navigating protected genomics data with UCSC Genome Browser in a Box.

PubMed

Haeussler, Maximilian; Raney, Brian J; Hinrichs, Angie S; Clawson, Hiram; Zweig, Ann S; Karolchik, Donna; Casper, Jonathan; Speir, Matthew L; Haussler, David; Kent, W James

2015-03-01

Genome Browser in a Box (GBiB) is a small virtual machine version of the popular University of California Santa Cruz (UCSC) Genome Browser that can be run on a researcher's own computer. Once GBiB is installed, a standard web browser is used to access the virtual server and add personal data files from the local hard disk. Annotation data are loaded on demand through the Internet from UCSC or can be downloaded to the local computer for faster access. Software downloads and installation instructions are freely available for non-commercial use at https://genome-store.ucsc.edu/. GBiB requires the installation of open-source software VirtualBox, available for all major operating systems, and the UCSC Genome Browser, which is open source and free for non-commercial use. Commercial use of GBiB and the Genome Browser requires a license (http://genome.ucsc.edu/license/). © The Author 2014. Published by Oxford University Press.

Gene context analysis in the Integrated Microbial Genomes (IMG) data management system.

PubMed

Mavromatis, Konstantinos; Chu, Ken; Ivanova, Natalia; Hooper, Sean D; Markowitz, Victor M; Kyrpides, Nikos C

2009-11-24

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.

Genomic signal processing methods for computation of alignment-free distances from DNA sequences.

PubMed

Borrayo, Ernesto; Mendizabal-Ruiz, E Gerardo; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Mendizabal, Adriana P; Morales, J Alejandro

2014-01-01

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments.

Genomic Signal Processing Methods for Computation of Alignment-Free Distances from DNA Sequences

PubMed Central

Borrayo, Ernesto; Mendizabal-Ruiz, E. Gerardo; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Mendizabal, Adriana P.; Morales, J. Alejandro

2014-01-01

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments. PMID:25393409

Modeling biological problems in computer science: a case study in genome assembly.

PubMed

Medvedev, Paul

2018-01-30

As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. In this tutorial, we use the example of the genome assembly problem to demonstrate how to go from a question in the biological realm to a solution in the computer science realm. We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research.

PubMed

Eppig, Janan T; Smith, Cynthia L; Blake, Judith A; Ringwald, Martin; Kadin, James A; Richardson, Joel E; Bult, Carol J

2017-01-01

The Mouse Genome Informatics (MGI), resource ( www.informatics.jax.org ) has existed for over 25 years, and over this time its data content, informatics infrastructure, and user interfaces and tools have undergone dramatic changes (Eppig et al., Mamm Genome 26:272-284, 2015). Change has been driven by scientific methodological advances, rapid improvements in computational software, growth in computer hardware capacity, and the ongoing collaborative nature of the mouse genomics community in building resources and sharing data. Here we present an overview of the current data content of MGI, describe its general organization, and provide examples using simple and complex searches, and tools for mining and retrieving sets of data.

Large-scale parallel genome assembler over cloud computing environment.

PubMed

Das, Arghya Kusum; Koppa, Praveen Kumar; Goswami, Sayan; Platania, Richard; Park, Seung-Jong

2017-06-01

The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.

Detecting Genomic Clustering of Risk Variants from Sequence Data: Cases vs. Controls

PubMed Central

Schaid, Daniel J.; Sinnwell, Jason P.; McDonnell, Shannon K.; Thibodeau, Stephen N.

2013-01-01

As the ability to measure dense genetic markers approaches the limit of the DNA sequence itself, taking advantage of possible clustering of genetic variants in, and around, a gene would benefit genetic association analyses, and likely provide biological insights. The greatest benefit might be realized when multiple rare variants cluster in a functional region. Several statistical tests have been developed, one of which is based on the popular Kulldorff scan statistic for spatial clustering of disease. We extended another popular spatial clustering method – Tango’s statistic – to genomic sequence data. An advantage of Tango’s method is that it is rapid to compute, and when single test statistic is computed, its distribution is well approximated by a scaled chi-square distribution, making computation of p-values very rapid. We compared the Type-I error rates and power of several clustering statistics, as well as the omnibus sequence kernel association test (SKAT). Although our version of Tango’s statistic, which we call “Kernel Distance” statistic, took approximately half the time to compute than the Kulldorff scan statistic, it had slightly less power than the scan statistic. Our results showed that the Ionita-Laza version of Kulldorff’s scan statistic had the greatest power over a range of clustering scenarios. PMID:23842950

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts.

PubMed

Lee, Donghyung; Bigdeli, T Bernard; Williamson, Vernell S; Vladimirov, Vladimir I; Riley, Brien P; Fanous, Ayman H; Bacanu, Silviu-Alin

2015-10-01

To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts. To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources. DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix. dlee4@vcu.edu Supplementary Data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Dynamics of genome size evolution in birds and mammals.

PubMed

Kapusta, Aurélie; Suh, Alexander; Feschotte, Cédric

2017-02-21

Genome size in mammals and birds shows remarkably little interspecific variation compared with other taxa. However, genome sequencing has revealed that many mammal and bird lineages have experienced differential rates of transposable element (TE) accumulation, which would be predicted to cause substantial variation in genome size between species. Thus, we hypothesize that there has been covariation between the amount of DNA gained by transposition and lost by deletion during mammal and avian evolution, resulting in genome size equilibrium. To test this model, we develop computational methods to quantify the amount of DNA gained by TE expansion and lost by deletion over the last 100 My in the lineages of 10 species of eutherian mammals and 24 species of birds. The results reveal extensive variation in the amount of DNA gained via lineage-specific transposition, but that DNA loss counteracted this expansion to various extents across lineages. Our analysis of the rate and size spectrum of deletion events implies that DNA removal in both mammals and birds has proceeded mostly through large segmental deletions (>10 kb). These findings support a unified "accordion" model of genome size evolution in eukaryotes whereby DNA loss counteracting TE expansion is a major determinant of genome size. Furthermore, we propose that extensive DNA loss, and not necessarily a dearth of TE activity, has been the primary force maintaining the greater genomic compaction of flying birds and bats relative to their flightless relatives.

The projection of a test genome onto a reference population and applications to humans and archaic hominins.

PubMed

Yang, Melinda A; Harris, Kelley; Slatkin, Montgomery

2014-12-01

We introduce a method for comparing a test genome with numerous genomes from a reference population. Sites in the test genome are given a weight, w, that depends on the allele frequency, x, in the reference population. The projection of the test genome onto the reference population is the average weight for each x, [Formula: see text]. The weight is assigned in such a way that, if the test genome is a random sample from the reference population, then [Formula: see text]. Using analytic theory, numerical analysis, and simulations, we show how the projection depends on the time of population splitting, the history of admixture, and changes in past population size. The projection is sensitive to small amounts of past admixture, the direction of admixture, and admixture from a population not sampled (a ghost population). We compute the projections of several human and two archaic genomes onto three reference populations from the 1000 Genomes project-Europeans, Han Chinese, and Yoruba-and discuss the consistency of our analysis with previously published results for European and Yoruba demographic history. Including higher amounts of admixture between Europeans and Yoruba soon after their separation and low amounts of admixture more recently can resolve discrepancies between the projections and demographic inferences from some previous studies. Copyright © 2014 by the Genetics Society of America.

Sparse representation and Bayesian detection of genome copy number alterations from microarray data.

PubMed

Pique-Regi, Roger; Monso-Varona, Jordi; Ortega, Antonio; Seeger, Robert C; Triche, Timothy J; Asgharzadeh, Shahab

2008-02-01

Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) that are associated with the development and behavior of tumors. Advances in microarray technology have allowed for greater resolution in detection of DNA copy number changes (amplifications or deletions) across the genome. However, the increase in number of measured signals and accompanying noise from the array probes present a challenge in accurate and fast identification of breakpoints that define CNA. This article proposes a novel detection technique that exploits the use of piece wise constant (PWC) vectors to represent genome copy number and sparse Bayesian learning (SBL) to detect CNA breakpoints. First, a compact linear algebra representation for the genome copy number is developed from normalized probe intensities. Second, SBL is applied and optimized to infer locations where copy number changes occur. Third, a backward elimination (BE) procedure is used to rank the inferred breakpoints; and a cut-off point can be efficiently adjusted in this procedure to control for the false discovery rate (FDR). The performance of our algorithm is evaluated using simulated and real genome datasets and compared to other existing techniques. Our approach achieves the highest accuracy and lowest FDR while improving computational speed by several orders of magnitude. The proposed algorithm has been developed into a free standing software application (GADA, Genome Alteration Detection Algorithm). http://biron.usc.edu/~piquereg/GADA

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

PubMed

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

Application of theoretical methods to increase succinate production in engineered strains.

PubMed

Valderrama-Gomez, M A; Kreitmayer, D; Wolf, S; Marin-Sanguino, A; Kremling, A

2017-04-01

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

Privacy in the Genomic Era

PubMed Central

NAVEED, MUHAMMAD; AYDAY, ERMAN; CLAYTON, ELLEN W.; FELLAY, JACQUES; GUNTER, CARL A.; HUBAUX, JEAN-PIERRE; MALIN, BRADLEY A.; WANG, XIAOFENG

2015-01-01

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward. PMID:26640318

Privacy in the Genomic Era.

PubMed

Naveed, Muhammad; Ayday, Erman; Clayton, Ellen W; Fellay, Jacques; Gunter, Carl A; Hubaux, Jean-Pierre; Malin, Bradley A; Wang, Xiaofeng

2015-09-01

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.

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/ .

A Genomic Resource for the Development, Improvement, and Exploitation of Sorghum for Bioenergy.

PubMed

Brenton, Zachary W; Cooper, Elizabeth A; Myers, Mathew T; Boyles, Richard E; Shakoor, Nadia; Zielinski, Kelsey J; Rauh, Bradley L; Bridges, William C; Morris, Geoffrey P; Kresovich, Stephen

2016-09-01

With high productivity and stress tolerance, numerous grass genera of the Andropogoneae have emerged as candidates for bioenergy production. To optimize these candidates, research examining the genetic architecture of yield, carbon partitioning, and composition is required to advance breeding objectives. Significant progress has been made developing genetic and genomic resources for Andropogoneae, and advances in comparative and computational genomics have enabled research examining the genetic basis of photosynthesis, carbon partitioning, composition, and sink strength. To provide a pivotal resource aimed at developing a comparative understanding of key bioenergy traits in the Andropogoneae, we have established and characterized an association panel of 390 racially, geographically, and phenotypically diverse Sorghum bicolor accessions with 232,303 genetic markers. Sorghum bicolor was selected because of its genomic simplicity, phenotypic diversity, significant genomic tools, and its agricultural productivity and resilience. We have demonstrated the value of sorghum as a functional model for candidate gene discovery for bioenergy Andropogoneae by performing genome-wide association analysis for two contrasting phenotypes representing key components of structural and non-structural carbohydrates. We identified potential genes, including a cellulase enzyme and a vacuolar transporter, associated with increased non-structural carbohydrates that could lead to bioenergy sorghum improvement. Although our analysis identified genes with potentially clear functions, other candidates did not have assigned functions, suggesting novel molecular mechanisms for carbon partitioning traits. These results, combined with our characterization of phenotypic and genetic diversity and the public accessibility of each accession and genomic data, demonstrate the value of this resource and provide a foundation for future improvement of sorghum and related grasses for bioenergy production. Copyright © 2016 by the Genetics Society of America.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

PubMed

Guo, Xuan; Meng, Yu; Yu, Ning; Pan, Yi

2014-04-10

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

How accurate is automated gap filling of metabolic models?

PubMed

Karp, Peter D; Weaver, Daniel; Latendresse, Mario

2018-06-19

Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions - anaerobic growth under four nutrients producing 53 biomass metabolites. The solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions. Our conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

PubMed Central

2014-01-01

Backgroud Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS. PMID:24717145

ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes

PubMed Central

Otto, Thomas Dan; Catanho, Marcos; Tristão, Cristian; Bezerra, Márcia; Fernandes, Renan Mathias; Elias, Guilherme Steinberger; Scaglia, Alexandre Capeletto; Bovermann, Bill; Berstis, Viktors; Lifschitz, Sergio; de Miranda, Antonio Basílio; Degrave, Wim

2010-01-01

Motivation: Many analyses in modern biological research are based on comparisons between biological sequences, resulting in functional, evolutionary and structural inferences. When large numbers of sequences are compared, heuristics are often used resulting in a certain lack of accuracy. In order to improve and validate results of such comparisons, we have performed radical all-against-all comparisons of 4 million protein sequences belonging to the RefSeq database, using an implementation of the Smith–Waterman algorithm. This extremely intensive computational approach was made possible with the help of World Community Grid™, through the Genome Comparison Project. The resulting database, ProteinWorldDB, which contains coordinates of pairwise protein alignments and their respective scores, is now made available. Users can download, compare and analyze the results, filtered by genomes, protein functions or clusters. ProteinWorldDB is integrated with annotations derived from Swiss-Prot, Pfam, KEGG, NCBI Taxonomy database and gene ontology. The database is a unique and valuable asset, representing a major effort to create a reliable and consistent dataset of cross-comparisons of the whole protein content encoded in hundreds of completely sequenced genomes using a rigorous dynamic programming approach. Availability: The database can be accessed through http://proteinworlddb.org Contact: otto@fiocruz.br PMID:20089515

A sensitive, support-vector-machine method for the detection of horizontal gene transfers in viral, archaeal and bacterial genomes.

PubMed

Tsirigos, Aristotelis; Rigoutsos, Isidore

2005-01-01

In earlier work, we introduced and discussed a generalized computational framework for identifying horizontal transfers. This framework relied on a gene's nucleotide composition, obviated the need for knowledge of codon boundaries and database searches, and was shown to perform very well across a wide range of archaeal and bacterial genomes when compared with previously published approaches, such as Codon Adaptation Index and C + G content. Nonetheless, two considerations remained outstanding: we wanted to further increase the sensitivity of detecting horizontal transfers and also to be able to apply the method to increasingly smaller genomes. In the discussion that follows, we present such a method, Wn-SVM, and show that it exhibits a very significant improvement in sensitivity compared with earlier approaches. Wn-SVM uses a one-class support-vector machine and can learn using rather small training sets. This property makes Wn-SVM particularly suitable for studying small-size genomes, similar to those of viruses, as well as the typically larger archaeal and bacterial genomes. We show experimentally that the new method results in a superior performance across a wide range of organisms and that it improves even upon our own earlier method by an average of 10% across all examined genomes. As a small-genome case study, we analyze the genome of the human cytomegalovirus and demonstrate that Wn-SVM correctly identifies regions that are known to be conserved and prototypical of all beta-herpesvirinae, regions that are known to have been acquired horizontally from the human host and, finally, regions that had not up to now been suspected to be horizontally transferred. Atypical region predictions for many eukaryotic viruses, including the alpha-, beta- and gamma-herpesvirinae, and 123 archaeal and bacterial genomes, have been made available online at http://cbcsrv.watson.ibm.com/HGT_SVM/.

GenomeCAT: a versatile tool for the analysis and integrative visualization of DNA copy number variants.

PubMed

Tebel, Katrin; Boldt, Vivien; Steininger, Anne; Port, Matthias; Ebert, Grit; Ullmann, Reinhard

2017-01-06

The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others. We developed GenomeCAT, a standalone Java application for the analysis and integrative visualization of CNVs. GenomeCAT is composed of three modules dedicated to the inspection of single cases, comparative analysis of multidimensional data and group comparisons aiming at the identification of recurrent aberrations in patients sharing the same phenotype, respectively. Its flexible import options ease the comparative analysis of own results derived from microarray or NGS platforms with data from literature or public depositories. Multidimensional data obtained from different experiment types can be merged into a common data matrix to enable common visualization and analysis. All results are stored in the integrated MySQL database, but can also be exported as tab delimited files for further statistical calculations in external programs. GenomeCAT offers a broad spectrum of visualization and analysis tools that assist in the evaluation of CNVs in the context of other experiment data and annotations. The use of GenomeCAT does not require any specialized computer skills. The various R packages implemented for data analysis are fully integrated into GenomeCATs graphical user interface and the installation process is supported by a wizard. The flexibility in terms of data import and export in combination with the ability to create a common data matrix makes the program also well suited as an interface between genomic data from heterogeneous sources and external software tools. Due to the modular architecture the functionality of GenomeCAT can be easily extended by further R packages or customized plug-ins to meet future requirements.

Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases.

PubMed

Kelemen, Arpad; Vasilakos, Athanasios V; Liang, Yulan

2009-09-01

Comprehensive evaluation of common genetic variations through association of single-nucleotide polymorphism (SNP) structure with common complex disease in the genome-wide scale is currently a hot area in human genome research due to the recent development of the Human Genome Project and HapMap Project. Computational science, which includes computational intelligence (CI), has recently become the third method of scientific enquiry besides theory and experimentation. There have been fast growing interests in developing and applying CI in disease mapping using SNP and haplotype data. Some of the recent studies have demonstrated the promise and importance of CI for common complex diseases in genomic association study using SNP/haplotype data, especially for tackling challenges, such as gene-gene and gene-environment interactions, and the notorious "curse of dimensionality" problem. This review provides coverage of recent developments of CI approaches for complex diseases in genetic association study with SNP/haplotype data.

Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline.

PubMed

Reid, Jeffrey G; Carroll, Andrew; Veeraraghavan, Narayanan; Dahdouli, Mahmoud; Sundquist, Andreas; English, Adam; Bainbridge, Matthew; White, Simon; Salerno, William; Buhay, Christian; Yu, Fuli; Muzny, Donna; Daly, Richard; Duyk, Geoff; Gibbs, Richard A; Boerwinkle, Eric

2014-01-29

Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.

Genomics and privacy: implications of the new reality of closed data for the field.

PubMed

Greenbaum, Dov; Sboner, Andrea; Mu, Xinmeng Jasmine; Gerstein, Mark

2011-12-01

Open source and open data have been driving forces in bioinformatics in the past. However, privacy concerns may soon change the landscape, limiting future access to important data sets, including personal genomics data. Here we survey this situation in some detail, describing, in particular, how the large scale of the data from personal genomic sequencing makes it especially hard to share data, exacerbating the privacy problem. We also go over various aspects of genomic privacy: first, there is basic identifiability of subjects having their genome sequenced. However, even for individuals who have consented to be identified, there is the prospect of very detailed future characterization of their genotype, which, unanticipated at the time of their consent, may be more personal and invasive than the release of their medical records. We go over various computational strategies for dealing with the issue of genomic privacy. One can "slice" and reformat datasets to allow them to be partially shared while securing the most private variants. This is particularly applicable to functional genomics information, which can be largely processed without variant information. For handling the most private data there are a number of legal and technological approaches-for example, modifying the informed consent procedure to acknowledge that privacy cannot be guaranteed, and/or employing a secure cloud computing environment. Cloud computing in particular may allow access to the data in a more controlled fashion than the current practice of downloading and computing on large datasets. Furthermore, it may be particularly advantageous for small labs, given that the burden of many privacy issues falls disproportionately on them in comparison to large corporations and genome centers. Finally, we discuss how education of future genetics researchers will be important, with curriculums emphasizing privacy and data security. However, teaching personal genomics with identifiable subjects in the university setting will, in turn, create additional privacy issues and social conundrums. © 2011 Greenbaum et al.

Center for Cancer Genomics | Office of Cancer Genomics

Cancer.gov

The Center for Cancer Genomics (CCG) was established to unify the National Cancer Institute's activities in cancer genomics, with the goal of advancing genomics research and translating findings into the clinic to improve the precise diagnosis and treatment of cancers. In addition to promoting genomic sequencing approaches, CCG aims to accelerate structural, functional and computational research to explore cancer mechanisms, discover new cancer targets, and develop new therapeutics.

Performance comparison of two efficient genomic selection methods (gsbay & MixP) applied in aquacultural organisms

NASA Astrophysics Data System (ADS)

Su, Hailin; Li, Hengde; Wang, Shi; Wang, Yangfan; Bao, Zhenmin

2017-02-01

Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools MixP and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop ( Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction (GBLUP) method which has been applied widely. Our results showed that both MixP and gsbay could accurately estimate single-nucleotide polymorphism (SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values (GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by MixP; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by MixP and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with MixP the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by MixP and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.

The Case for Cyberlearning: Genomics (and Dragons!) in the High School Biology Classroom

ERIC Educational Resources Information Center

Southworth, Meghan; Mokros, Jan; Dorsey, Chad; Smith, Randy

2010-01-01

GENIQUEST is a cyberlearning computer program that allows students to investigate biological data using a research-based instructional model. In this article, the authors make the case for using cyberlearning to teach students about the rapidly growing fields of genomics and computational biology. (Contains 2 figures and 1 online resource.)

Computational Simulation and Analysis of Mutations: Nucleotide Fixation, Allelic Age and Rare Genetic Variations in Population

ERIC Educational Resources Information Center

Qiu, Shuhao

2015-01-01

In order to investigate the complexity of mutations, a computational approach named Genome Evolution by Matrix Algorithms ("GEMA") has been implemented. GEMA models genomic changes, taking into account hundreds of mutations within each individual in a population. By modeling of entire human chromosomes, GEMA precisely mimics real…

ProDeGe: A computational protocol for fully automated decontamination of genomes

DOE PAGES

Tennessen, Kristin; Andersen, Evan; Clingenpeel, Scott; ...

2015-06-09

Single amplified genomes and genomes assembled from metagenomes have enabled the exploration of uncultured microorganisms at an unprecedented scale. However, both these types of products are plagued by contamination. Since these genomes are now being generated in a high-throughput manner and sequences from them are propagating into public databases to drive novel scientific discoveries, rigorous quality controls and decontamination protocols are urgently needed. Here, we present ProDeGe (Protocol for fully automated Decontamination of Genomes), the first computational protocol for fully automated decontamination of draft genomes. ProDeGe classifies sequences into two classes—clean and contaminant—using a combination of homology and feature-based methodologies.more » On average, 84% of sequence from the non-target organism is removed from the data set (specificity) and 84% of the sequence from the target organism is retained (sensitivity). Lastly, the procedure operates successfully at a rate of ~0.30 CPU core hours per megabase of sequence and can be applied to any type of genome sequence.« less

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.

ProDeGe: A computational protocol for fully automated decontamination of genomes

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

Tennessen, Kristin; Andersen, Evan; Clingenpeel, Scott

Single amplified genomes and genomes assembled from metagenomes have enabled the exploration of uncultured microorganisms at an unprecedented scale. However, both these types of products are plagued by contamination. Since these genomes are now being generated in a high-throughput manner and sequences from them are propagating into public databases to drive novel scientific discoveries, rigorous quality controls and decontamination protocols are urgently needed. Here, we present ProDeGe (Protocol for fully automated Decontamination of Genomes), the first computational protocol for fully automated decontamination of draft genomes. ProDeGe classifies sequences into two classes—clean and contaminant—using a combination of homology and feature-based methodologies.more » On average, 84% of sequence from the non-target organism is removed from the data set (specificity) and 84% of the sequence from the target organism is retained (sensitivity). Lastly, the procedure operates successfully at a rate of ~0.30 CPU core hours per megabase of sequence and can be applied to any type of genome sequence.« less

DNA Compass: a secure, client-side site for navigating personal genetic information

PubMed Central

Curnin, Charles; Gordon, Assaf; Erlich, Yaniv

2017-01-01

Abstract Motivation: Millions of individuals have access to raw genomic data using direct-to-consumer companies. The advent of large-scale sequencing projects, such as the Precision Medicine Initiative, will further increase the number of individuals with access to their own genomic information. However, querying genomic data requires a computer terminal and computational skill to analyze the data—an impediment for the general public. Results: DNA Compass is a website designed to empower the public by enabling simple navigation of personal genomic data. Users can query the status of their genomic variants for over 1658 markers or tens of millions of documented single nucleotide polymorphisms (SNPs). DNA Compass presents the relevant genotypes of the user side-by-side with explanatory scientific resources. The genotype data never leaves the user’s computer, a feature that provides improved security and performance. More than 12 000 unique users, mainly from the general genetic genealogy community, have already used DNA Compass, demonstrating its utility. Availability and Implementation: DNA Compass is freely available on https://compass.dna.land. Contact: yaniv@cs.columbia.edu PMID:28334237

Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation.

PubMed

Nakato, Ryuichiro; Shirahige, Katsuhiko

2017-03-01

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. Recent advances in sequencing technologies and analyses enable us to compare hundreds of samples simultaneously; such large-scale analysis has potential to reveal the high-dimensional interrelationship level for regulatory elements and annotate novel functional genomic regions de novo. Because many experimental considerations are relevant to the choice of a method in a ChIP-seq analysis, the overall design and quality management of the experiment are of critical importance. This review offers guiding principles of computation and sample preparation for ChIP-seq analyses, highlighting the validity and limitations of the state-of-the-art procedures at each step. We also discuss the latest challenges of single-cell analysis that will encourage a new era in this field. © The Author 2016. Published by Oxford University Press.

Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency

PubMed Central

Aniceto, Rodrigo; Xavier, Rene; Guimarães, Valeria; Hondo, Fernanda; Holanda, Maristela; Walter, Maria Emilia; Lifschitz, Sérgio

2015-01-01

Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB. PMID:26558254

GenePRIMP: A Gene Prediction Improvement Pipeline For Prokaryotic Genomes

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

Kyrpides, Nikos C.; Ivanova, Natalia N.; Pati, Amrita

2010-07-08

GenePRIMP (Gene Prediction Improvement Pipeline, Http://geneprimp.jgi-psf.org), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missing genes, and split genes. We show that manual curation of gene models using the anomaly reports generated by GenePRIMP improves their quality and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome sequencing and annotation technologies. Keywords in context: Gene model, Quality Control, Translation start sites, Automatic correction. Hardware requirements; PC, MAC; Operating System: UNIX/LINUX; Compiler/Version: Perl 5.8.5 or higher; Special requirements: NCBI Blast and nr installation; File Types:more » Source Code, Executable module(s), Sample problem input data; installation instructions other; programmer documentation. Location/transmission: http://geneprimp.jgi-psf.org/gp.tar.gz« less

Measuring Sister Chromatid Cohesion Protein Genome Occupancy in Drosophila melanogaster by ChIP-seq.

PubMed

Dorsett, Dale; Misulovin, Ziva

2017-01-01

This chapter presents methods to conduct and analyze genome-wide chromatin immunoprecipitation of the cohesin complex and the Nipped-B cohesin loading factor in Drosophila cells using high-throughput DNA sequencing (ChIP-seq). Procedures for isolation of chromatin, immunoprecipitation, and construction of sequencing libraries for the Ion Torrent Proton high throughput sequencer are detailed, and computational methods to calculate occupancy as input-normalized fold-enrichment are described. The results obtained by ChIP-seq are compared to those obtained by ChIP-chip (genomic ChIP using tiling microarrays), and the effects of sequencing depth on the accuracy are analyzed. ChIP-seq provides similar sensitivity and reproducibility as ChIP-chip, and identifies the same broad regions of occupancy. The locations of enrichment peaks, however, can differ between ChIP-chip and ChIP-seq, and low sequencing depth can splinter broad regions of occupancy into distinct peaks.

Comparative Omics-Driven Genome Annotation Refinement: Application across Yersiniae

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

Rutledge, Alexandra C.; Jones, Marcus B.; Chauhan, Sadhana

2012-03-27

Genome sequencing continues to be a rapidly evolving technology, yet most downstream aspects of genome annotation pipelines remain relatively stable or are even being abandoned. To date, the perceived value of manual curation for genome annotations is not offset by the real cost and time associated with the process. In order to balance the large number of sequences generated, the annotation process is now performed almost exclusively in an automated fashion for most genome sequencing projects. One possible way to reduce errors inherent to automated computational annotations is to apply data from 'omics' measurements (i.e. transcriptional and proteomic) to themore » un-annotated genome with a proteogenomic-based approach. This approach does require additional experimental and bioinformatics methods to include omics technologies; however, the approach is readily automatable and can benefit from rapid developments occurring in those research domains as well. The annotation process can be improved by experimental validation of transcription and translation and aid in the discovery of annotation errors. Here the concept of annotation refinement has been extended to include a comparative assessment of genomes across closely related species, as is becoming common in sequencing efforts. Transcriptomic and proteomic data derived from three highly similar pathogenic Yersiniae (Y. pestis CO92, Y. pestis pestoides F, and Y. pseudotuberculosis PB1/+) was used to demonstrate a comprehensive comparative omic-based annotation methodology. Peptide and oligo measurements experimentally validated the expression of nearly 40% of each strain's predicted proteome and revealed the identification of 28 novel and 68 previously incorrect protein-coding sequences (e.g., observed frameshifts, extended start sites, and translated pseudogenes) within the three current Yersinia genome annotations. Gene loss is presumed to play a major role in Y. pestis acquiring its niche as a virulent pathogen, thus the discovery of many translated pseudogenes underscores a need for functional analyses to investigate hypotheses related to divergence. Refinements included the discovery of a seemingly essential ribosomal protein, several virulence-associated factors, and a transcriptional regulator, among other proteins, most of which are annotated as hypothetical, that were missed during annotation.« less

Rice-Map: a new-generation rice genome browser.

PubMed

Wang, Jun; Kong, Lei; Zhao, Shuqi; Zhang, He; Tang, Liang; Li, Zhe; Gu, Xiaocheng; Luo, Jingchu; Gao, Ge

2011-03-30

The concurrent release of rice genome sequences for two subspecies (Oryza sativa L. ssp. japonica and Oryza sativa L. ssp. indica) facilitates rice studies at the whole genome level. Since the advent of high-throughput analysis, huge amounts of functional genomics data have been delivered rapidly, making an integrated online genome browser indispensable for scientists to visualize and analyze these data. Based on next-generation web technologies and high-throughput experimental data, we have developed Rice-Map, a novel genome browser for researchers to navigate, analyze and annotate rice genome interactively. More than one hundred annotation tracks (81 for japonica and 82 for indica) have been compiled and loaded into Rice-Map. These pre-computed annotations cover gene models, transcript evidences, expression profiling, epigenetic modifications, inter-species and intra-species homologies, genetic markers and other genomic features. In addition to these pre-computed tracks, registered users can interactively add comments and research notes to Rice-Map as User-Defined Annotation entries. By smoothly scrolling, dragging and zooming, users can browse various genomic features simultaneously at multiple scales. On-the-fly analysis for selected entries could be performed through dedicated bioinformatic analysis platforms such as WebLab and Galaxy. Furthermore, a BioMart-powered data warehouse "Rice Mart" is offered for advanced users to fetch bulk datasets based on complex criteria. Rice-Map delivers abundant up-to-date japonica and indica annotations, providing a valuable resource for both computational and bench biologists. Rice-Map is publicly accessible at http://www.ricemap.org/, with all data available for free downloading.

Functional Assays to Screen and Dissect Genomic Hits: Doubling Down on the National Investment in Genomic Research.

PubMed

Musunuru, Kiran; Bernstein, Daniel; Cole, F Sessions; Khokha, Mustafa K; Lee, Frank S; Lin, Shin; McDonald, Thomas V; Moskowitz, Ivan P; Quertermous, Thomas; Sankaran, Vijay G; Schwartz, David A; Silverman, Edwin K; Zhou, Xiaobo; Hasan, Ahmed A K; Luo, Xiao-Zhong James

2018-04-01

The National Institutes of Health have made substantial investments in genomic studies and technologies to identify DNA sequence variants associated with human disease phenotypes. The National Heart, Lung, and Blood Institute has been at the forefront of these commitments to ascertain genetic variation associated with heart, lung, blood, and sleep diseases and related clinical traits. Genome-wide association studies, exome- and genome-sequencing studies, and exome-genotyping studies of the National Heart, Lung, and Blood Institute-funded epidemiological and clinical case-control studies are identifying large numbers of genetic variants associated with heart, lung, blood, and sleep phenotypes. However, investigators face challenges in identification of genomic variants that are functionally disruptive among the myriad of computationally implicated variants. Studies to define mechanisms of genetic disruption encoded by computationally identified genomic variants require reproducible, adaptable, and inexpensive methods to screen candidate variant and gene function. High-throughput strategies will permit a tiered variant discovery and genetic mechanism approach that begins with rapid functional screening of a large number of computationally implicated variants and genes for discovery of those that merit mechanistic investigation. As such, improved variant-to-gene and gene-to-function screens-and adequate support for such studies-are critical to accelerating the translation of genomic findings. In this White Paper, we outline the variety of novel technologies, assays, and model systems that are making such screens faster, cheaper, and more accurate, referencing published work and ongoing work supported by the National Heart, Lung, and Blood Institute's R21/R33 Functional Assays to Screen Genomic Hits program. We discuss priorities that can accelerate the impressive but incomplete progress represented by big data genomic research. © 2018 American Heart Association, Inc.

Comparative multi-omics systems analysis of Escherichia coli strains B and K-12.

PubMed

Yoon, Sung Ho; Han, Mee-Jung; Jeong, Haeyoung; Lee, Choong Hoon; Xia, Xiao-Xia; Lee, Dae-Hee; Shim, Ji Hoon; Lee, Sang Yup; Oh, Tae Kwang; Kim, Jihyun F

2012-05-25

Elucidation of a genotype-phenotype relationship is critical to understand an organism at the whole-system level. Here, we demonstrate that comparative analyses of multi-omics data combined with a computational modeling approach provide a framework for elucidating the phenotypic characteristics of organisms whose genomes are sequenced. We present a comprehensive analysis of genome-wide measurements incorporating multifaceted holistic data - genome, transcriptome, proteome, and phenome - to determine the differences between Escherichia coli B and K-12 strains. A genome-scale metabolic network of E. coli B was reconstructed and used to identify genetic bases of the phenotypes unique to B compared with K-12 through in silico complementation testing. This systems analysis revealed that E. coli B is well-suited for production of recombinant proteins due to a greater capacity for amino acid biosynthesis, fewer proteases, and lack of flagella. Furthermore, E. coli B has an additional type II secretion system and a different cell wall and outer membrane composition predicted to be more favorable for protein secretion. In contrast, E. coli K-12 showed a higher expression of heat shock genes and was less susceptible to certain stress conditions. This integrative systems approach provides a high-resolution system-wide view and insights into why two closely related strains of E. coli, B and K-12, manifest distinct phenotypes. Therefore, systematic understanding of cellular physiology and metabolism of the strains is essential not only to determine culture conditions but also to design recombinant hosts.

Comparative multi-omics systems analysis of Escherichia coli strains B and K-12

PubMed Central

2012-01-01

Background Elucidation of a genotype-phenotype relationship is critical to understand an organism at the whole-system level. Here, we demonstrate that comparative analyses of multi-omics data combined with a computational modeling approach provide a framework for elucidating the phenotypic characteristics of organisms whose genomes are sequenced. Results We present a comprehensive analysis of genome-wide measurements incorporating multifaceted holistic data - genome, transcriptome, proteome, and phenome - to determine the differences between Escherichia coli B and K-12 strains. A genome-scale metabolic network of E. coli B was reconstructed and used to identify genetic bases of the phenotypes unique to B compared with K-12 through in silico complementation testing. This systems analysis revealed that E. coli B is well-suited for production of recombinant proteins due to a greater capacity for amino acid biosynthesis, fewer proteases, and lack of flagella. Furthermore, E. coli B has an additional type II secretion system and a different cell wall and outer membrane composition predicted to be more favorable for protein secretion. In contrast, E. coli K-12 showed a higher expression of heat shock genes and was less susceptible to certain stress conditions. Conclusions This integrative systems approach provides a high-resolution system-wide view and insights into why two closely related strains of E. coli, B and K-12, manifest distinct phenotypes. Therefore, systematic understanding of cellular physiology and metabolism of the strains is essential not only to determine culture conditions but also to design recombinant hosts. PMID:22632713

A comprehensive transcript index of the human genome generated using microarrays and computational approaches

PubMed Central

Schadt, Eric E; Edwards, Stephen W; GuhaThakurta, Debraj; Holder, Dan; Ying, Lisa; Svetnik, Vladimir; Leonardson, Amy; Hart, Kyle W; Russell, Archie; Li, Guoya; Cavet, Guy; Castle, John; McDonagh, Paul; Kan, Zhengyan; Chen, Ronghua; Kasarskis, Andrew; Margarint, Mihai; Caceres, Ramon M; Johnson, Jason M; Armour, Christopher D; Garrett-Engele, Philip W; Tsinoremas, Nicholas F; Shoemaker, Daniel D

2004-01-01

Background Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. Results The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. Conclusions These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized. PMID:15461792

The pig genome project has plenty to squeal about.

PubMed

Fan, B; Gorbach, D M; Rothschild, M F

2011-01-01

Significant progress on pig genetics and genomics research has been witnessed in recent years due to the integration of advanced molecular biology techniques, bioinformatics and computational biology, and the collaborative efforts of researchers in the swine genomics community. Progress on expanding the linkage map has slowed down, but the efforts have created a higher-resolution physical map integrating the clone map and BAC end sequence. The number of QTL mapped is still growing and most of the updated QTL mapping results are available through PigQTLdb. Additionally, expression studies using high-throughput microarrays and other gene expression techniques have made significant advancements. The number of identified non-coding RNAs is rapidly increasing and their exact regulatory functions are being explored. A publishable draft (build 10) of the swine genome sequence was available for the pig genomics community by the end of December 2010. Build 9 of the porcine genome is currently available with Ensembl annotation; manual annotation is ongoing. These drafts provide useful tools for such endeavors as comparative genomics and SNP scans for fine QTL mapping. A recent community-wide effort to create a 60K porcine SNP chip has greatly facilitated whole-genome association analyses, haplotype block construction and linkage disequilibrium mapping, which can contribute to whole-genome selection. The future 'systems biology' that integrates and optimizes the information from all research levels can enhance the pig community's understanding of the full complexity of the porcine genome. These recent technological advances and where they may lead are reviewed. Copyright © 2011 S. Karger AG, Basel.

Repair-mediated duplication by capture of proximal chromosomal DNA has shaped vertebrate genome evolution.

PubMed

Pace, John K; Sen, Shurjo K; Batzer, Mark A; Feschotte, Cédric

2009-05-01

DNA double-strand breaks (DSBs) are a common form of cellular damage that can lead to cell death if not repaired promptly. Experimental systems have shown that DSB repair in eukaryotic cells is often imperfect and may result in the insertion of extra chromosomal DNA or the duplication of existing DNA at the breakpoint. These events are thought to be a source of genomic instability and human diseases, but it is unclear whether they have contributed significantly to genome evolution. Here we developed an innovative computational pipeline that takes advantage of the repetitive structure of genomes to detect repair-mediated duplication events (RDs) that occurred in the germline and created insertions of at least 50 bp of genomic DNA. Using this pipeline we identified over 1,000 probable RDs in the human genome. Of these, 824 were intra-chromosomal, closely linked duplications of up to 619 bp bearing the hallmarks of the synthesis-dependent strand-annealing repair pathway. This mechanism has duplicated hundreds of sequences predicted to be functional in the human genome, including exons, UTRs, intron splice sites and transcription factor binding sites. Dating of the duplication events using comparative genomics and experimental validation revealed that the mechanism has operated continuously but with decreasing intensity throughout primate evolution. The mechanism has produced species-specific duplications in all primate species surveyed and is contributing to genomic variation among humans. Finally, we show that RDs have also occurred, albeit at a lower frequency, in non-primate mammals and other vertebrates, indicating that this mechanism has been an important force shaping vertebrate genome evolution.

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).

RGmatch: matching genomic regions to proximal genes in omics data integration.

PubMed

Furió-Tarí, Pedro; Conesa, Ana; Tarazona, Sonia

2016-11-22

The integrative analysis of multiple genomics data often requires that genome coordinates-based signals have to be associated with proximal genes. The relative location of a genomic region with respect to the gene (gene area) is important for functional data interpretation; hence algorithms that match regions to genes should be able to deliver insight into this information. In this work we review the tools that are publicly available for making region-to-gene associations. We also present a novel method, RGmatch, a flexible and easy-to-use Python tool that computes associations either at the gene, transcript, or exon level, applying a set of rules to annotate each region-gene association with the region location within the gene. RGmatch can be applied to any organism as long as genome annotation is available. Furthermore, we qualitatively and quantitatively compare RGmatch to other tools. RGmatch simplifies the association of a genomic region with its closest gene. At the same time, it is a powerful tool because the rules used to annotate these associations are very easy to modify according to the researcher's specific interests. Some important differences between RGmatch and other similar tools already in existence are RGmatch's flexibility, its wide range of user options, compatibility with any annotatable organism, and its comprehensive and user-friendly output.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

2017-02-01

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Genomics for Everyone

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

Chain, Patrick

Genomics — the genetic mapping and DNA sequencing of sets of genes or the complete genomes of organisms, along with related genome analysis and database work — is emerging as one of the transformative sciences of the 21st century. But current bioinformatics tools are not accessible to most biological researchers. Now, a new computational and web-based tool called EDGE Bioinformatics is working to fulfill the promise of democratizing genomics.

Supervised Machine Learning for Population Genetics: A New Paradigm

PubMed Central

Schrider, Daniel R.; Kern, Andrew D.

2018-01-01

As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. PMID:29331490

Neandertal admixture in Eurasia confirmed by maximum-likelihood analysis of three genomes.

PubMed

Lohse, Konrad; Frantz, Laurent A F

2014-04-01

Although there has been much interest in estimating histories of divergence and admixture from genomic data, it has proved difficult to distinguish recent admixture from long-term structure in the ancestral population. Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the possibility of interbreeding between Neandertals and modern humans in Eurasia. Here we derive the probability of full mutational configurations in nonrecombining sequence blocks under both admixture and ancestral structure scenarios. Dividing the genome into short blocks gives an efficient way to compute maximum-likelihood estimates of parameters. We apply this likelihood scheme to triplets of human and Neandertal genomes and compare the relative support for a model of admixture from Neandertals into Eurasian populations after their expansion out of Africa against a history of persistent structure in their common ancestral population in Africa. Our analysis allows us to conclusively reject a model of ancestral structure in Africa and instead reveals strong support for Neandertal admixture in Eurasia at a higher rate (3.4-7.3%) than suggested previously. Using analysis and simulations we show that our inference is more powerful than previous summary statistics and robust to realistic levels of recombination.

Neandertal Admixture in Eurasia Confirmed by Maximum-Likelihood Analysis of Three Genomes

PubMed Central

Lohse, Konrad; Frantz, Laurent A. F.

2014-01-01

Although there has been much interest in estimating histories of divergence and admixture from genomic data, it has proved difficult to distinguish recent admixture from long-term structure in the ancestral population. Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the possibility of interbreeding between Neandertals and modern humans in Eurasia. Here we derive the probability of full mutational configurations in nonrecombining sequence blocks under both admixture and ancestral structure scenarios. Dividing the genome into short blocks gives an efficient way to compute maximum-likelihood estimates of parameters. We apply this likelihood scheme to triplets of human and Neandertal genomes and compare the relative support for a model of admixture from Neandertals into Eurasian populations after their expansion out of Africa against a history of persistent structure in their common ancestral population in Africa. Our analysis allows us to conclusively reject a model of ancestral structure in Africa and instead reveals strong support for Neandertal admixture in Eurasia at a higher rate (3.4−7.3%) than suggested previously. Using analysis and simulations we show that our inference is more powerful than previous summary statistics and robust to realistic levels of recombination. PMID:24532731

HAL: a hierarchical format for storing and analyzing multiple genome alignments.

PubMed

Hickey, Glenn; Paten, Benedict; Earl, Dent; Zerbino, Daniel; Haussler, David

2013-05-15

Large multiple genome alignments and inferred ancestral genomes are ideal resources for comparative studies of molecular evolution, and advances in sequencing and computing technology are making them increasingly obtainable. These structures can provide a rich understanding of the genetic relationships between all subsets of species they contain. Current formats for storing genomic alignments, such as XMFA and MAF, are all indexed or ordered using a single reference genome, however, which limits the information that can be queried with respect to other species and clades. This loss of information grows with the number of species under comparison, as well as their phylogenetic distance. We present HAL, a compressed, graph-based hierarchical alignment format for storing multiple genome alignments and ancestral reconstructions. HAL graphs are indexed on all genomes they contain. Furthermore, they are organized phylogenetically, which allows for modular and parallel access to arbitrary subclades without fragmentation because of rearrangements that have occurred in other lineages. HAL graphs can be created or read with a comprehensive C++ API. A set of tools is also provided to perform basic operations, such as importing and exporting data, identifying mutations and coordinate mapping (liftover). All documentation and source code for the HAL API and tools are freely available at http://github.com/glennhickey/hal. hickey@soe.ucsc.edu or haussler@soe.ucsc.edu Supplementary data are available at Bioinformatics online.

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.

PubMed

Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; Zhang, Yuanyuan; Liu, Zhaowen

2016-01-01

Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset.

FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm

PubMed Central

Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; Zhang, Yuanyuan; Liu, Zhaowen

2016-01-01

Motivation Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Method In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. Results We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset. PMID:27014873

START: a system for flexible analysis of hundreds of genomic signal tracks in few lines of SQL-like queries.

PubMed

Zhu, Xinjie; Zhang, Qiang; Ho, Eric Dun; Yu, Ken Hung-On; Liu, Chris; Huang, Tim H; Cheng, Alfred Sze-Lok; Kao, Ben; Lo, Eric; Yip, Kevin Y

2017-09-22

A genomic signal track is a set of genomic intervals associated with values of various types, such as measurements from high-throughput experiments. Analysis of signal tracks requires complex computational methods, which often make the analysts focus too much on the detailed computational steps rather than on their biological questions. Here we propose Signal Track Query Language (STQL) for simple analysis of signal tracks. It is a Structured Query Language (SQL)-like declarative language, which means one only specifies what computations need to be done but not how these computations are to be carried out. STQL provides a rich set of constructs for manipulating genomic intervals and their values. To run STQL queries, we have developed the Signal Track Analytical Research Tool (START, http://yiplab.cse.cuhk.edu.hk/start/ ), a system that includes a Web-based user interface and a back-end execution system. The user interface helps users select data from our database of around 10,000 commonly-used public signal tracks, manage their own tracks, and construct, store and share STQL queries. The back-end system automatically translates STQL queries into optimized low-level programs and runs them on a computer cluster in parallel. We use STQL to perform 14 representative analytical tasks. By repeating these analyses using bedtools, Galaxy and custom Python scripts, we show that the STQL solution is usually the simplest, and the parallel execution achieves significant speed-up with large data files. Finally, we describe how a biologist with minimal formal training in computer programming self-learned STQL to analyze DNA methylation data we produced from 60 pairs of hepatocellular carcinoma (HCC) samples. Overall, STQL and START provide a generic way for analyzing a large number of genomic signal tracks in parallel easily.

Self-Directed Student Research through Analysis of Microarray Datasets: A Computer-Based Functional Genomics Practical Class for Masters-Level Students

ERIC Educational Resources Information Center

Grenville-Briggs, Laura J.; Stansfield, Ian

2011-01-01

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate…

Comparison of domestic and foreign genotypes by country and continent

USDA-ARS?s Scientific Manuscript database

Genomic evaluations for foreign animals are easily computed, and reliabilities are highest for animals well connected to the domestic reference population and managed in similar environments. Genomic and pedigree relationships, inbreeding, pedigree completeness, pedigree accuracy and genomic merit w...

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)

PubMed Central

Li, Isaac TS; Shum, Warren; Truong, Kevin

2007-01-01

Background To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. Results In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. Conclusion This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching. PMID:17555593

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA).

PubMed

Li, Isaac T S; Shum, Warren; Truong, Kevin

2007-06-07

To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.

Assessment of gene order computing methods for Alzheimer's disease

PubMed Central

2013-01-01

Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods. PMID:23369541

Accurate computation of survival statistics in genome-wide studies.

PubMed

Vandin, Fabio; Papoutsaki, Alexandra; Raphael, Benjamin J; Upfal, Eli

2015-05-01

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.

Accurate Computation of Survival Statistics in Genome-Wide Studies

PubMed Central

Vandin, Fabio; Papoutsaki, Alexandra; Raphael, Benjamin J.; Upfal, Eli

2015-01-01

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations. PMID:25950620

Whole-genome sequence, SNP chips and pedigree structure: building demographic profiles in domestic dog breeds to optimize genetic-trait mapping.

PubMed

Dreger, Dayna L; Rimbault, Maud; Davis, Brian W; Bhatnagar, Adrienne; Parker, Heidi G; Ostrander, Elaine A

2016-12-01

In the decade following publication of the draft genome sequence of the domestic dog, extraordinary advances with application to several fields have been credited to the canine genetic system. Taking advantage of closed breeding populations and the subsequent selection for aesthetic and behavioral characteristics, researchers have leveraged the dog as an effective natural model for the study of complex traits, such as disease susceptibility, behavior and morphology, generating unique contributions to human health and biology. When designing genetic studies using purebred dogs, it is essential to consider the unique demography of each population, including estimation of effective population size and timing of population bottlenecks. The analytical design approach for genome-wide association studies (GWAS) and analysis of whole-genome sequence (WGS) experiments are inextricable from demographic data. We have performed a comprehensive study of genomic homozygosity, using high-depth WGS data for 90 individuals, and Illumina HD SNP data from 800 individuals representing 80 breeds. These data were coupled with extensive pedigree data analyses for 11 breeds that, together, allowed us to compute breed structure, demography, and molecular measures of genome diversity. Our comparative analyses characterize the extent, formation and implication of breed-specific diversity as it relates to population structure. These data demonstrate the relationship between breed-specific genome dynamics and population architecture, and provide important considerations influencing the technological and cohort design of association and other genomic studies. © 2016. Published by The Company of Biologists Ltd.

Dynamics of genome size evolution in birds and mammals

PubMed Central

Feschotte, Cédric

2017-01-01

Genome size in mammals and birds shows remarkably little interspecific variation compared with other taxa. However, genome sequencing has revealed that many mammal and bird lineages have experienced differential rates of transposable element (TE) accumulation, which would be predicted to cause substantial variation in genome size between species. Thus, we hypothesize that there has been covariation between the amount of DNA gained by transposition and lost by deletion during mammal and avian evolution, resulting in genome size equilibrium. To test this model, we develop computational methods to quantify the amount of DNA gained by TE expansion and lost by deletion over the last 100 My in the lineages of 10 species of eutherian mammals and 24 species of birds. The results reveal extensive variation in the amount of DNA gained via lineage-specific transposition, but that DNA loss counteracted this expansion to various extents across lineages. Our analysis of the rate and size spectrum of deletion events implies that DNA removal in both mammals and birds has proceeded mostly through large segmental deletions (>10 kb). These findings support a unified “accordion” model of genome size evolution in eukaryotes whereby DNA loss counteracting TE expansion is a major determinant of genome size. Furthermore, we propose that extensive DNA loss, and not necessarily a dearth of TE activity, has been the primary force maintaining the greater genomic compaction of flying birds and bats relative to their flightless relatives. PMID:28179571

Whole-genome sequence, SNP chips and pedigree structure: building demographic profiles in domestic dog breeds to optimize genetic-trait mapping

PubMed Central

Dreger, Dayna L.; Rimbault, Maud; Davis, Brian W.; Bhatnagar, Adrienne; Parker, Heidi G.

2016-01-01

ABSTRACT In the decade following publication of the draft genome sequence of the domestic dog, extraordinary advances with application to several fields have been credited to the canine genetic system. Taking advantage of closed breeding populations and the subsequent selection for aesthetic and behavioral characteristics, researchers have leveraged the dog as an effective natural model for the study of complex traits, such as disease susceptibility, behavior and morphology, generating unique contributions to human health and biology. When designing genetic studies using purebred dogs, it is essential to consider the unique demography of each population, including estimation of effective population size and timing of population bottlenecks. The analytical design approach for genome-wide association studies (GWAS) and analysis of whole-genome sequence (WGS) experiments are inextricable from demographic data. We have performed a comprehensive study of genomic homozygosity, using high-depth WGS data for 90 individuals, and Illumina HD SNP data from 800 individuals representing 80 breeds. These data were coupled with extensive pedigree data analyses for 11 breeds that, together, allowed us to compute breed structure, demography, and molecular measures of genome diversity. Our comparative analyses characterize the extent, formation and implication of breed-specific diversity as it relates to population structure. These data demonstrate the relationship between breed-specific genome dynamics and population architecture, and provide important considerations influencing the technological and cohort design of association and other genomic studies. PMID:27874836

STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud.

PubMed

Karczewski, Konrad J; Fernald, Guy Haskin; Martin, Alicia R; Snyder, Michael; Tatonetti, Nicholas P; Dudley, Joel T

2014-01-01

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5-10 hours to process a full exome sequence and $30 and 3-8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2.

A Primer on Infectious Disease Bacterial Genomics

PubMed Central

Petkau, Aaron; Knox, Natalie; Graham, Morag; Van Domselaar, Gary

2016-01-01

SUMMARY The number of large-scale genomics projects is increasing due to the availability of affordable high-throughput sequencing (HTS) technologies. The use of HTS for bacterial infectious disease research is attractive because one whole-genome sequencing (WGS) run can replace multiple assays for bacterial typing, molecular epidemiology investigations, and more in-depth pathogenomic studies. The computational resources and bioinformatics expertise required to accommodate and analyze the large amounts of data pose new challenges for researchers embarking on genomics projects for the first time. Here, we present a comprehensive overview of a bacterial genomics projects from beginning to end, with a particular focus on the planning and computational requirements for HTS data, and provide a general understanding of the analytical concepts to develop a workflow that will meet the objectives and goals of HTS projects. PMID:28590251

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

Inter- and intra-specific pan-genomes of Borrelia burgdorferi sensu lato: genome stability and adaptive radiation

PubMed Central

2013-01-01

Background Lyme disease is caused by spirochete bacteria from the Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) species complex. To reconstruct the evolution of B. burgdorferi s.l. and identify the genomic basis of its human virulence, we compared the genomes of 23 B. burgdorferi s.l. isolates from Europe and the United States, including B. burgdorferi sensu stricto (B. burgdorferi s.s., 14 isolates), B. afzelii (2), B. garinii (2), B. “bavariensis” (1), B. spielmanii (1), B. valaisiana (1), B. bissettii (1), and B. “finlandensis” (1). Results Robust B. burgdorferi s.s. and B. burgdorferi s.l. phylogenies were obtained using genome-wide single-nucleotide polymorphisms, despite recombination. Phylogeny-based pan-genome analysis showed that the rate of gene acquisition was higher between species than within species, suggesting adaptive speciation. Strong positive natural selection drives the sequence evolution of lipoproteins, including chromosomally-encoded genes 0102 and 0404, cp26-encoded ospC and b08, and lp54-encoded dbpA, a07, a22, a33, a53, a65. Computer simulations predicted rapid adaptive radiation of genomic groups as population size increases. Conclusions Intra- and inter-specific pan-genome sizes of B. burgdorferi s.l. expand linearly with phylogenetic diversity. Yet gene-acquisition rates in B. burgdorferi s.l. are among the lowest in bacterial pathogens, resulting in high genome stability and few lineage-specific genes. Genome adaptation of B. burgdorferi s.l. is driven predominantly by copy-number and sequence variations of lipoprotein genes. New genomic groups are likely to emerge if the current trend of B. burgdorferi s.l. population expansion continues. PMID:24112474

Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences

PubMed Central

Huynen, Martijn; Snel, Berend; Lathe, Warren; Bork, Peer

2000-01-01

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes. PMID:10958638

Survey and analysis of simple sequence repeats in the Laccaria bicolor genome, with development of microsatellite markers

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

Labbe, Jessy L; Murat, Claude; Morin, Emmanuelle

It is becoming clear that simple sequence repeats (SSRs) play a significant role in fungal genome organization, and they are a large source of genetic markers for population genetics and meiotic maps. We identified SSRs in the Laccaria bicolor genome by in silico survey and analyzed their distribution in the different genomic regions. We also compared the abundance and distribution of SSRs in L. bicolor with those of the following fungal genomes: Phanerochaete chrysosporium, Coprinopsis cinerea, Ustilago maydis, Cryptococcus neoformans, Aspergillus nidulans, Magnaporthe grisea, Neurospora crassa and Saccharomyces cerevisiae. Using the MISA computer program, we detected 277,062 SSRs in themore » L. bicolor genome representing 8% of the assembled genomic sequence. Among the analyzed basidiomycetes, L. bicolor exhibited the highest SSR density although no correlation between relative abundance and the genome sizes was observed. In most genomes the short motifs (mono- to trinucleotides) were more abundant than the longer repeated SSRs. Generally, in each organism, the occurrence, relative abundance, and relative density of SSRs decreased as the repeat unit increased. Furthermore, each organism had its own common and longest SSRs. In the L. bicolor genome, most of the SSRs were located in intergenic regions (73.3%) and the highest SSR density was observed in transposable elements (TEs; 6,706 SSRs/Mb). However, 81% of the protein-coding genes contained SSRs in their exons, suggesting that SSR polymorphism may alter gene phenotypes. Within a L. bicolor offspring, sequence polymorphism of 78 SSRs was mainly detected in non-TE intergenic regions. Unlike previously developed microsatellite markers, these new ones are spread throughout the genome; these markers could have immediate applications in population genetics.« less

CpGAVAS, an integrated web server for the annotation, visualization, analysis, and GenBank submission of completely sequenced chloroplast genome sequences

PubMed Central

2012-01-01

Background The complete sequences of chloroplast genomes provide wealthy information regarding the evolutionary history of species. With the advance of next-generation sequencing technology, the number of completely sequenced chloroplast genomes is expected to increase exponentially, powerful computational tools annotating the genome sequences are in urgent need. Results We have developed a web server CPGAVAS. The server accepts a complete chloroplast genome sequence as input. First, it predicts protein-coding and rRNA genes based on the identification and mapping of the most similar, full-length protein, cDNA and rRNA sequences by integrating results from Blastx, Blastn, protein2genome and est2genome programs. Second, tRNA genes and inverted repeats (IR) are identified using tRNAscan, ARAGORN and vmatch respectively. Third, it calculates the summary statistics for the annotated genome. Fourth, it generates a circular map ready for publication. Fifth, it can create a Sequin file for GenBank submission. Last, it allows the extractions of protein and mRNA sequences for given list of genes and species. The annotation results in GFF3 format can be edited using any compatible annotation editing tools. The edited annotations can then be uploaded to CPGAVAS for update and re-analyses repeatedly. Using known chloroplast genome sequences as test set, we show that CPGAVAS performs comparably to another application DOGMA, while having several superior functionalities. Conclusions CPGAVAS allows the semi-automatic and complete annotation of a chloroplast genome sequence, and the visualization, editing and analysis of the annotation results. It will become an indispensible tool for researchers studying chloroplast genomes. The software is freely accessible from http://www.herbalgenomics.org/cpgavas. PMID:23256920

Restricted DCJ-indel model: sorting linear genomes with DCJ and indels

PubMed Central

2012-01-01

Background The double-cut-and-join (DCJ) is a model that is able to efficiently sort a genome into another, generalizing the typical mutations (inversions, fusions, fissions, translocations) to which genomes are subject, but allowing the existence of circular chromosomes at the intermediate steps. In the general model many circular chromosomes can coexist in some intermediate step. However, when the compared genomes are linear, it is more plausible to use the so-called restricted DCJ model, in which we proceed the reincorporation of a circular chromosome immediately after its creation. These two consecutive DCJ operations, which create and reincorporate a circular chromosome, mimic a transposition or a block-interchange. When the compared genomes have the same content, it is known that the genomic distance for the restricted DCJ model is the same as the distance for the general model. If the genomes have unequal contents, in addition to DCJ it is necessary to consider indels, which are insertions and deletions of DNA segments. Linear time algorithms were proposed to compute the distance and to find a sorting scenario in a general, unrestricted DCJ-indel model that considers DCJ and indels. Results In the present work we consider the restricted DCJ-indel model for sorting linear genomes with unequal contents. We allow DCJ operations and indels with the following constraint: if a circular chromosome is created by a DCJ, it has to be reincorporated in the next step (no other DCJ or indel can be applied between the creation and the reincorporation of a circular chromosome). We then develop a sorting algorithm and give a tight upper bound for the restricted DCJ-indel distance. Conclusions We have given a tight upper bound for the restricted DCJ-indel distance. The question whether this bound can be reduced so that both the general and the restricted DCJ-indel distances are equal remains open. PMID:23281630

Watson for Genomics: Moving Personalized Medicine Forward.

PubMed

Rhrissorrakrai, Kahn; Koyama, Takahiko; Parida, Laxmi

2016-08-01

The confluence of genomic technologies and cognitive computing has brought us to the doorstep of widespread usage of personalized medicine. Cognitive systems, such as Watson for Genomics (WG), integrate massive amounts of new omic data with the current body of knowledge to assist physicians in analyzing and acting on patient's genomic profiles. Copyright © 2016 Elsevier Inc. All rights reserved.

Illuminating the Black Box of Genome Sequence Assembly: A Free Online Tool to Introduce Students to Bioinformatics

ERIC Educational Resources Information Center

Taylor, D. Leland; Campbell, A. Malcolm; Heyer, Laurie J.

2013-01-01

Next-generation sequencing technologies have greatly reduced the cost of sequencing genomes. With the current sequencing technology, a genome is broken into fragments and sequenced, producing millions of "reads." A computer algorithm pieces these reads together in the genome assembly process. PHAST is a set of online modules…

Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the joint probability mass function of genotypes.

PubMed

Martínez, Carlos Alberto; Khare, Kshitij; Banerjee, Arunava; Elzo, Mauricio A

2017-03-21

It is important to consider heterogeneity of marker effects and allelic frequencies in across population genome-wide prediction studies. Moreover, all regression models used in genome-wide prediction overlook randomness of genotypes. In this study, a family of hierarchical Bayesian models to perform across population genome-wide prediction modeling genotypes as random variables and allowing population-specific effects for each marker was developed. Models shared a common structure and differed in the priors used and the assumption about residual variances (homogeneous or heterogeneous). Randomness of genotypes was accounted for by deriving the joint probability mass function of marker genotypes conditional on allelic frequencies and pedigree information. As a consequence, these models incorporated kinship and genotypic information that not only permitted to account for heterogeneity of allelic frequencies, but also to include individuals with missing genotypes at some or all loci without the need for previous imputation. This was possible because the non-observed fraction of the design matrix was treated as an unknown model parameter. For each model, a simpler version ignoring population structure, but still accounting for randomness of genotypes was proposed. Implementation of these models and computation of some criteria for model comparison were illustrated using two simulated datasets. Theoretical and computational issues along with possible applications, extensions and refinements were discussed. Some features of the models developed in this study make them promising for genome-wide prediction, the use of information contained in the probability distribution of genotypes is perhaps the most appealing. Further studies to assess the performance of the models proposed here and also to compare them with conventional models used in genome-wide prediction are needed. Copyright © 2017 Elsevier Ltd. All rights reserved.

ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus

PubMed Central

Karp, Peter D.; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-01-01

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida). PMID:26097686

ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus.

PubMed

Karp, Peter D; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-01-01

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida).

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

PubMed

Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R; Taylor, Jeremy F; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

2016-01-01

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

PubMed Central

Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R.; Taylor, Jeremy F.; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

2016-01-01

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal. PMID:27583971

Bioinformatics and genomic analysis of transposable elements in eukaryotic genomes.

PubMed

Janicki, Mateusz; Rooke, Rebecca; Yang, Guojun

2011-08-01

A major portion of most eukaryotic genomes are transposable elements (TEs). During evolution, TEs have introduced profound changes to genome size, structure, and function. As integral parts of genomes, the dynamic presence of TEs will continue to be a major force in reshaping genomes. Early computational analyses of TEs in genome sequences focused on filtering out "junk" sequences to facilitate gene annotation. When the high abundance and diversity of TEs in eukaryotic genomes were recognized, these early efforts transformed into the systematic genome-wide categorization and classification of TEs. The availability of genomic sequence data reversed the classical genetic approaches to discovering new TE families and superfamilies. Curated TE databases and their accurate annotation of genome sequences in turn facilitated the studies on TEs in a number of frontiers including: (1) TE-mediated changes of genome size and structure, (2) the influence of TEs on genome and gene functions, (3) TE regulation by host, (4) the evolution of TEs and their population dynamics, and (5) genomic scale studies of TE activity. Bioinformatics and genomic approaches have become an integral part of large-scale studies on TEs to extract information with pure in silico analyses or to assist wet lab experimental studies. The current revolution in genome sequencing technology facilitates further progress in the existing frontiers of research and emergence of new initiatives. The rapid generation of large-sequence datasets at record low costs on a routine basis is challenging the computing industry on storage capacity and manipulation speed and the bioinformatics community for improvement in algorithms and their implementations.

Genomics for Everyone

ScienceCinema

Chain, Patrick

2018-05-31

Genomics — the genetic mapping and DNA sequencing of sets of genes or the complete genomes of organisms, along with related genome analysis and database work — is emerging as one of the transformative sciences of the 21st century. But current bioinformatics tools are not accessible to most biological researchers. Now, a new computational and web-based tool called EDGE Bioinformatics is working to fulfill the promise of democratizing genomics.

Population genomics of fungal and oomycete pathogens

USDA-ARS?s Scientific Manuscript database

We are entering a new era in plant pathology where whole-genome sequences of many individuals of a pathogen species are becoming readily available. This era of pathogen population genomics will provide new opportunities and challenges, requiring new computational and analytical tools. Population gen...

An efficient graph theory based method to identify every minimal reaction set in a metabolic network

PubMed Central

2014-01-01

Background Development of cells with minimal metabolic functionality is gaining importance due to their efficiency in producing chemicals and fuels. Existing computational methods to identify minimal reaction sets in metabolic networks are computationally expensive. Further, they identify only one of the several possible minimal reaction sets. Results In this paper, we propose an efficient graph theory based recursive optimization approach to identify all minimal reaction sets. Graph theoretical insights offer systematic methods to not only reduce the number of variables in math programming and increase its computational efficiency, but also provide efficient ways to find multiple optimal solutions. The efficacy of the proposed approach is demonstrated using case studies from Escherichia coli and Saccharomyces cerevisiae. In case study 1, the proposed method identified three minimal reaction sets each containing 38 reactions in Escherichia coli central metabolic network with 77 reactions. Analysis of these three minimal reaction sets revealed that one of them is more suitable for developing minimal metabolism cell compared to other two due to practically achievable internal flux distribution. In case study 2, the proposed method identified 256 minimal reaction sets from the Saccharomyces cerevisiae genome scale metabolic network with 620 reactions. The proposed method required only 4.5 hours to identify all the 256 minimal reaction sets and has shown a significant reduction (approximately 80%) in the solution time when compared to the existing methods for finding minimal reaction set. Conclusions Identification of all minimal reactions sets in metabolic networks is essential since different minimal reaction sets have different properties that effect the bioprocess development. The proposed method correctly identified all minimal reaction sets in a both the case studies. The proposed method is computationally efficient compared to other methods for finding minimal reaction sets and useful to employ with genome-scale metabolic networks. PMID:24594118

DCJ-indel and DCJ-substitution distances with distinct operation costs

PubMed Central

2013-01-01

Background Classical approaches to compute the genomic distance are usually limited to genomes with the same content and take into consideration only rearrangements that change the organization of the genome (i.e. positions and orientation of pieces of DNA, number and type of chromosomes, etc.), such as inversions, translocations, fusions and fissions. These operations are generically represented by the double-cut and join (DCJ) operation. The distance between two genomes, in terms of number of DCJ operations, can be computed in linear time. In order to handle genomes with distinct contents, also insertions and deletions of fragments of DNA – named indels – must be allowed. More powerful than an indel is a substitution of a fragment of DNA by another fragment of DNA. Indels and substitutions are called content-modifying operations. It has been shown that both the DCJ-indel and the DCJ-substitution distances can also be computed in linear time, assuming that the same cost is assigned to any DCJ or content-modifying operation. Results In the present study we extend the DCJ-indel and the DCJ-substitution models, considering that the content-modifying cost is distinct from and upper bounded by the DCJ cost, and show that the distance in both models can still be computed in linear time. Although the triangular inequality can be disrupted in both models, we also show how to efficiently fix this problem a posteriori. PMID:23879938

Comparison of phasing strategies for whole human genomes

PubMed Central

Kirkness, Ewen; Schork, Nicholas J.

2018-01-01

Humans are a diploid species that inherit one set of chromosomes paternally and one homologous set of chromosomes maternally. Unfortunately, most human sequencing initiatives ignore this fact in that they do not directly delineate the nucleotide content of the maternal and paternal copies of the 23 chromosomes individuals possess (i.e., they do not ‘phase’ the genome) often because of the costs and complexities of doing so. We compared 11 different widely-used approaches to phasing human genomes using the publicly available ‘Genome-In-A-Bottle’ (GIAB) phased version of the NA12878 genome as a gold standard. The phasing strategies we compared included laboratory-based assays that prepare DNA in unique ways to facilitate phasing as well as purely computational approaches that seek to reconstruct phase information from general sequencing reads and constructs or population-level haplotype frequency information obtained through a reference panel of haplotypes. To assess the performance of the 11 approaches, we used metrics that included, among others, switch error rates, haplotype block lengths, the proportion of fully phase-resolved genes, phasing accuracy and yield between pairs of SNVs. Our comparisons suggest that a hybrid or combined approach that leverages: 1. population-based phasing using the SHAPEIT software suite, 2. either genome-wide sequencing read data or parental genotypes, and 3. a large reference panel of variant and haplotype frequencies, provides a fast and efficient way to produce highly accurate phase-resolved individual human genomes. We found that for population-based approaches, phasing performance is enhanced with the addition of genome-wide read data; e.g., whole genome shotgun and/or RNA sequencing reads. Further, we found that the inclusion of parental genotype data within a population-based phasing strategy can provide as much as a ten-fold reduction in phasing errors. We also considered a majority voting scheme for the construction of a consensus haplotype combining multiple predictions for enhanced performance and site coverage. Finally, we also identified DNA sequence signatures associated with the genomic regions harboring phasing switch errors, which included regions of low polymorphism or SNV density. PMID:29621242

The Diversity Present in 5140 Human Mitochondrial Genomes

PubMed Central

Pereira, Luísa; Freitas, Fernando; Fernandes, Verónica; Pereira, Joana B.; Costa, Marta D.; Costa, Stephanie; Máximo, Valdemar; Macaulay, Vincent; Rocha, Ricardo; Samuels, David C.

2009-01-01

We analyzed the current status (as of the end of August 2008) of human mitochondrial genomes deposited in GenBank, amounting to 5140 complete or coding-region sequences, in order to present an overall picture of the diversity present in the mitochondrial DNA of the global human population. To perform this task, we developed mtDNA-GeneSyn, a computer tool that identifies and exhaustedly classifies the diversity present in large genetic data sets. The diversity observed in the 5140 human mitochondrial genomes was compared with all possible transitions and transversions from the standard human mitochondrial reference genome. This comparison showed that tRNA and rRNA secondary structures have a large effect in limiting the diversity of the human mitochondrial sequences, whereas for the protein-coding genes there is a bias toward less variation at the second codon positions. The analysis of the observed amino acid variations showed a tolerance of variations that convert between the amino acids V, I, A, M, and T. This defines a group of amino acids with similar chemical properties that can interconvert by a single transition. PMID:19426953

Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)

PubMed Central

Skinnider, Michael A.; Dejong, Chris A.; Rees, Philip N.; Johnston, Chad W.; Li, Haoxin; Webster, Andrew L. H.; Wyatt, Morgan A.; Magarvey, Nathan A.

2015-01-01

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/. PMID:26442528

Recent developments in structural proteomics for protein structure determination.

PubMed

Liu, Hsuan-Liang; Hsu, Jyh-Ping

2005-05-01

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.

Integrative prescreening in analysis of multiple cancer genomic studies

PubMed Central

2012-01-01

Background In high throughput cancer genomic studies, results from the analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost. Results An integrative prescreening approach is developed for the analysis of multiple cancer genomic datasets. Simulation shows that the proposed integrative prescreening has better performance than alternatives, particularly including prescreening with individual datasets, an intensity approach and meta-analysis. We also analyze multiple microarray gene profiling studies on liver and pancreatic cancers using the proposed approach. Conclusions The proposed integrative prescreening provides an effective way to reduce the dimensionality in cancer genomic studies. It can be coupled with existing analysis methods to identify cancer markers. PMID:22799431

Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline

PubMed Central

2014-01-01

Background Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. Results To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. Conclusions By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples. PMID:24475911

Identification and profiling of novel microRNAs in the Brassica rapa genome based on small RNA deep sequencing

PubMed Central

2012-01-01

Background MicroRNAs (miRNAs) are one of the functional non-coding small RNAs involved in the epigenetic control of the plant genome. Although plants contain both evolutionary conserved miRNAs and species-specific miRNAs within their genomes, computational methods often only identify evolutionary conserved miRNAs. The recent sequencing of the Brassica rapa genome enables us to identify miRNAs and their putative target genes. In this study, we sought to provide a more comprehensive prediction of B. rapa miRNAs based on high throughput small RNA deep sequencing. Results We sequenced small RNAs from five types of tissue: seedlings, roots, petioles, leaves, and flowers. By analyzing 2.75 million unique reads that mapped to the B. rapa genome, we identified 216 novel and 196 conserved miRNAs that were predicted to target approximately 20% of the genome’s protein coding genes. Quantitative analysis of miRNAs from the five types of tissue revealed that novel miRNAs were expressed in diverse tissues but their expression levels were lower than those of the conserved miRNAs. Comparative analysis of the miRNAs between the B. rapa and Arabidopsis thaliana genomes demonstrated that redundant copies of conserved miRNAs in the B. rapa genome may have been deleted after whole genome triplication. Novel miRNA members seemed to have spontaneously arisen from the B. rapa and A. thaliana genomes, suggesting the species-specific expansion of miRNAs. We have made this data publicly available in a miRNA database of B. rapa called BraMRs. The database allows the user to retrieve miRNA sequences, their expression profiles, and a description of their target genes from the five tissue types investigated here. Conclusions This is the first report to identify novel miRNAs from Brassica crops using genome-wide high throughput techniques. The combination of computational methods and small RNA deep sequencing provides robust predictions of miRNAs in the genome. The finding of numerous novel miRNAs, many with few target genes and low expression levels, suggests the rapid evolution of miRNA genes. The development of a miRNA database, BraMRs, enables us to integrate miRNA identification, target prediction, and functional annotation of target genes. BraMRs will represent a valuable public resource with which to study the epigenetic control of B. rapa and other closely related Brassica species. The database is available at the following link: http://bramrs.rna.kr [1]. PMID:23163954

A Scheduling Algorithm for Computational Grids that Minimizes Centralized Processing in Genome Assembly of Next-Generation Sequencing Data

PubMed Central

Lima, Jakelyne; Cerdeira, Louise Teixeira; Bol, Erick; Schneider, Maria Paula Cruz; Silva, Artur; Azevedo, Vasco; Abelém, Antônio Jorge Gomes

2012-01-01

Improvements in genome sequencing techniques have resulted in generation of huge volumes of data. As a consequence of this progress, the genome assembly stage demands even more computational power, since the incoming sequence files contain large amounts of data. To speed up the process, it is often necessary to distribute the workload among a group of machines. However, this requires hardware and software solutions specially configured for this purpose. Grid computing try to simplify this process of aggregate resources, but do not always offer the best performance possible due to heterogeneity and decentralized management of its resources. Thus, it is necessary to develop software that takes into account these peculiarities. In order to achieve this purpose, we developed an algorithm aimed to optimize the functionality of de novo assembly software ABySS in order to optimize its operation in grids. We run ABySS with and without the algorithm we developed in the grid simulator SimGrid. Tests showed that our algorithm is viable, flexible, and scalable even on a heterogeneous environment, which improved the genome assembly time in computational grids without changing its quality. PMID:22461785

Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster

PubMed Central

Song, Yun S.

2012-01-01

Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity. PMID:23284288

Whole-genome CNV analysis: advances in computational approaches.

PubMed

Pirooznia, Mehdi; Goes, Fernando S; Zandi, Peter P

2015-01-01

Accumulating evidence indicates that DNA copy number variation (CNV) is likely to make a significant contribution to human diversity and also play an important role in disease susceptibility. Recent advances in genome sequencing technologies have enabled the characterization of a variety of genomic features, including CNVs. This has led to the development of several bioinformatics approaches to detect CNVs from next-generation sequencing data. Here, we review recent advances in CNV detection from whole genome sequencing. We discuss the informatics approaches and current computational tools that have been developed as well as their strengths and limitations. This review will assist researchers and analysts in choosing the most suitable tools for CNV analysis as well as provide suggestions for new directions in future development.

GenePRIMP: Improving Microbial Gene Prediction Quality

ScienceCinema

Pati, Amrita

2018-01-24

Amrita Pati of the DOE Joint Genome Institute's Genome Biology group talks about a computational pipeline that evaluates the accuracy of gene models in genomes and metagenomes at different stages of finishing at the "Sequencing, Finishing, Analysis in the Future" meeting in Santa Fe, NM.

Comparison and correlation of Simple Sequence Repeats distribution in genomes of Brucella species

PubMed Central

Kiran, Jangampalli Adi Pradeep; Chakravarthi, Veeraraghavulu Praveen; Kumar, Yellapu Nanda; Rekha, Somesula Swapna; Kruti, Srinivasan Shanthi; Bhaskar, Matcha

2011-01-01

Computational genomics is one of the important tools to understand the distribution of closely related genomes including simple sequence repeats (SSRs) in an organism, which gives valuable information regarding genetic variations. The central objective of the present study was to screen the SSRs distributed in coding and non-coding regions among different human Brucella species which are involved in a range of pathological disorders. Computational analysis of the SSRs in the Brucella indicates few deviations from expected random models. Statistical analysis also reveals that tri-nucleotide SSRs are overrepresented and tetranucleotide SSRs underrepresented in Brucella genomes. From the data, it can be suggested that over expressed tri-nucleotide SSRs in genomic and coding regions might be responsible in the generation of functional variation of proteins expressed which in turn may lead to different pathogenicity, virulence determinants, stress response genes, transcription regulators and host adaptation proteins of Brucella genomes. Abbreviations SSRs - Simple Sequence Repeats, ORFs - Open Reading Frames. PMID:21738309

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

How genome complexity can explain the difficulty of aligning reads to genomes.

PubMed

Phan, Vinhthuy; Gao, Shanshan; Tran, Quang; Vo, Nam S

2015-01-01

Although it is frequently observed that aligning short reads to genomes becomes harder if they contain complex repeat patterns, there has not been much effort to quantify the relationship between complexity of genomes and difficulty of short-read alignment. Existing measures of sequence complexity seem unsuitable for the understanding and quantification of this relationship. We investigated several measures of complexity and found that length-sensitive measures of complexity had the highest correlation to accuracy of alignment. In particular, the rate of distinct substrings of length k, where k is similar to the read length, correlated very highly to alignment performance in terms of precision and recall. We showed how to compute this measure efficiently in linear time, making it useful in practice to estimate quickly the difficulty of alignment for new genomes without having to align reads to them first. We showed how the length-sensitive measures could provide additional information for choosing aligners that would align consistently accurately on new genomes. We formally established a connection between genome complexity and the accuracy of short-read aligners. The relationship between genome complexity and alignment accuracy provides additional useful information for selecting suitable aligners for new genomes. Further, this work suggests that the complexity of genomes sometimes should be thought of in terms of specific computational problems, such as the alignment of short reads to genomes.

WordSeeker: concurrent bioinformatics software for discovering genome-wide patterns and word-based genomic signatures

PubMed Central

2010-01-01

Background An important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets. Methods This manuscript presents WordSeeker, an enumerative motif discovery toolkit that utilizes multi-core and distributed computational platforms to enable scalable analysis of genomic data. A controller task coordinates activities of worker nodes, each of which (1) enumerates a subset of the DNA word space and (2) scores words with a distributed Markov chain model. Results A comprehensive suite of performance tests was conducted to demonstrate the performance, speedup and efficiency of WordSeeker. The scalability of the toolkit enabled the analysis of the entire genome of Arabidopsis thaliana; the results of the analysis were integrated into The Arabidopsis Gene Regulatory Information Server (AGRIS). A public version of WordSeeker was deployed on the Glenn cluster at the Ohio Supercomputer Center. Conclusion WordSeeker effectively utilizes concurrent computing platforms to enable the identification of putative functional elements in genomic data sets. This capability facilitates the analysis of the large quantity of sequenced genomic data. PMID:21210985

Next-Generation Genomics Facility at C-CAMP: Accelerating Genomic Research in India

PubMed Central

S, Chandana; Russiachand, Heikham; H, Pradeep; S, Shilpa; M, Ashwini; S, Sahana; B, Jayanth; Atla, Goutham; Jain, Smita; Arunkumar, Nandini; Gowda, Malali

2014-01-01

Next-Generation Sequencing (NGS; http://www.genome.gov/12513162) is a recent life-sciences technological revolution that allows scientists to decode genomes or transcriptomes at a much faster rate with a lower cost. Genomic-based studies are in a relatively slow pace in India due to the non-availability of genomics experts, trained personnel and dedicated service providers. Using NGS there is a lot of potential to study India's national diversity (of all kinds). We at the Centre for Cellular and Molecular Platforms (C-CAMP) have launched the Next Generation Genomics Facility (NGGF) to provide genomics service to scientists, to train researchers and also work on national and international genomic projects. We have HiSeq1000 from Illumina and GS-FLX Plus from Roche454. The long reads from GS FLX Plus, and high sequence depth from HiSeq1000, are the best and ideal hybrid approaches for de novo and re-sequencing of genomes and transcriptomes. At our facility, we have sequenced around 70 different organisms comprising of more than 388 genomes and 615 transcriptomes – prokaryotes and eukaryotes (fungi, plants and animals). In addition we have optimized other unique applications such as small RNA (miRNA, siRNA etc), long Mate-pair sequencing (2 to 20 Kb), Coding sequences (Exome), Methylome (ChIP-Seq), Restriction Mapping (RAD-Seq), Human Leukocyte Antigen (HLA) typing, mixed genomes (metagenomes) and target amplicons, etc. Translating DNA sequence data from NGS sequencer into meaningful information is an important exercise. Under NGGF, we have bioinformatics experts and high-end computing resources to dissect NGS data such as genome assembly and annotation, gene expression, target enrichment, variant calling (SSR or SNP), comparative analysis etc. Our services (sequencing and bioinformatics) have been utilized by more than 45 organizations (academia and industry) both within India and outside, resulting several publications in peer-reviewed journals and several genomic/transcriptomic data is available at NCBI.

CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations.

PubMed

Wang, Xihong; Zheng, Zhuqing; Cai, Yudong; Chen, Ting; Li, Chao; Fu, Weiwei; Jiang, Yu

2017-12-01

The increasing amount of sequencing data available for a wide variety of species can be theoretically used for detecting copy number variations (CNVs) at the population level. However, the growing sample sizes and the divergent complexity of nonhuman genomes challenge the efficiency and robustness of current human-oriented CNV detection methods. Here, we present CNVcaller, a read-depth method for discovering CNVs in population sequencing data. The computational speed of CNVcaller was 1-2 orders of magnitude faster than CNVnator and Genome STRiP for complex genomes with thousands of unmapped scaffolds. CNV detection of 232 goats required only 1.4 days on a single compute node. Additionally, the Mendelian consistency of sheep trios indicated that CNVcaller mitigated the influence of high proportions of gaps and misassembled duplications in the nonhuman reference genome assembly. Furthermore, multiple evaluations using real sheep and human data indicated that CNVcaller achieved the best accuracy and sensitivity for detecting duplications. The fast generalized detection algorithms included in CNVcaller overcome prior computational barriers for detecting CNVs in large-scale sequencing data with complex genomic structures. Therefore, CNVcaller promotes population genetic analyses of functional CNVs in more species. © The Authors 2017. Published by Oxford University Press.

CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations

PubMed Central

Wang, Xihong; Zheng, Zhuqing; Cai, Yudong; Chen, Ting; Li, Chao; Fu, Weiwei

2017-01-01

Abstract Background The increasing amount of sequencing data available for a wide variety of species can be theoretically used for detecting copy number variations (CNVs) at the population level. However, the growing sample sizes and the divergent complexity of nonhuman genomes challenge the efficiency and robustness of current human-oriented CNV detection methods. Results Here, we present CNVcaller, a read-depth method for discovering CNVs in population sequencing data. The computational speed of CNVcaller was 1–2 orders of magnitude faster than CNVnator and Genome STRiP for complex genomes with thousands of unmapped scaffolds. CNV detection of 232 goats required only 1.4 days on a single compute node. Additionally, the Mendelian consistency of sheep trios indicated that CNVcaller mitigated the influence of high proportions of gaps and misassembled duplications in the nonhuman reference genome assembly. Furthermore, multiple evaluations using real sheep and human data indicated that CNVcaller achieved the best accuracy and sensitivity for detecting duplications. Conclusions The fast generalized detection algorithms included in CNVcaller overcome prior computational barriers for detecting CNVs in large-scale sequencing data with complex genomic structures. Therefore, CNVcaller promotes population genetic analyses of functional CNVs in more species. PMID:29220491

Most Used Codons per Amino Acid and per Genome in the Code of Man Compared to Other Organisms According to the Rotating Circular Genetic Code

PubMed Central

Castro-Chavez, Fernando

2011-01-01

My previous theoretical research shows that the rotating circular genetic code is a viable tool to make easier to distinguish the rules of variation applied to the amino acid exchange; it presents a precise and positional bio-mathematical balance of codons, according to the amino acids they codify. Here, I demonstrate that when using the conventional or classic circular genetic code, a clearer pattern for the human codon usage per amino acid and per genome emerges. The most used human codons per amino acid were the ones ending with the three hydrogen bond nucleotides: C for 12 amino acids and G for the remaining 8, plus one codon for arginine ending in A that was used approximately with the same frequency than the one ending in G for this same amino acid (plus *). The most used codons in man fall almost all the time at the rightmost position, clockwise, ending either in C or in G within the circular genetic code. The human codon usage per genome is compared to other organisms such as fruit flies (Drosophila melanogaster), squid (Loligo pealei), and many others. The biosemiotic codon usage of each genomic population or ‘Theme’ is equated to a ‘molecular language’. The C/U choice or difference, and the G/A difference in the third nucleotide of the most used codons per amino acid are illustrated by comparing the most used codons per genome in humans and squids. The human distribution in the third position of most used codons is a 12-8-2, C-G-A, nucleotide ending signature, while the squid distribution in the third position of most used codons was an odd, or uneven, distribution in the third position of its most used codons: 13-6-3, U-A-G, as its nucleotide ending signature. These findings may help to design computational tools to compare human genomes, to determine the exchangeability between compatible codons and amino acids, and for the early detection of incompatible changes leading to hereditary diseases. PMID:22997484

Computational and transcriptional evidence for microRNAs in the honey bee genome

PubMed Central

Weaver, Daniel B; Anzola, Juan M; Evans, Jay D; Reid, Jeffrey G; Reese, Justin T; Childs, Kevin L; Zdobnov, Evgeny M; Samanta, Manoj P; Miller, Jonathan; Elsik, Christine G

2007-01-01

Background Non-coding microRNAs (miRNAs) are key regulators of gene expression in eukaryotes. Insect miRNAs help regulate the levels of proteins involved with development, metabolism, and other life history traits. The recently sequenced honey bee genome provides an opportunity to detect novel miRNAs in both this species and others, and to begin to infer the roles of miRNAs in honey bee development. Results Three independent computational surveys of the assembled honey bee genome identified a total of 65 non-redundant candidate miRNAs, several of which appear to have previously unrecognized orthologs in the Drosophila genome. A subset of these candidate miRNAs were screened for expression by quantitative RT-PCR and/or genome tiling arrays and most predicted miRNAs were confirmed as being expressed in at least one honey bee tissue. Interestingly, the transcript abundance for several known and novel miRNAs displayed caste or age-related differences in honey bees. Genes in proximity to miRNAs in the bee genome are disproportionately associated with the Gene Ontology terms 'physiological process', 'nucleus' and 'response to stress'. Conclusion Computational approaches successfully identified miRNAs in the honey bee and indicated previously unrecognized miRNAs in the well-studied Drosophila melanogaster genome despite the 280 million year distance between these insects. Differentially transcribed miRNAs are likely to be involved in regulating honey bee development, and arguably in the extreme developmental switch between sterile worker bees and highly fertile queens. PMID:17543122

A Distance Measure for Genome Phylogenetic Analysis

NASA Astrophysics Data System (ADS)

Cao, Minh Duc; Allison, Lloyd; Dix, Trevor

Phylogenetic analyses of species based on single genes or parts of the genomes are often inconsistent because of factors such as variable rates of evolution and horizontal gene transfer. The availability of more and more sequenced genomes allows phylogeny construction from complete genomes that is less sensitive to such inconsistency. For such long sequences, construction methods like maximum parsimony and maximum likelihood are often not possible due to their intensive computational requirement. Another class of tree construction methods, namely distance-based methods, require a measure of distances between any two genomes. Some measures such as evolutionary edit distance of gene order and gene content are computational expensive or do not perform well when the gene content of the organisms are similar. This study presents an information theoretic measure of genetic distances between genomes based on the biological compression algorithm expert model. We demonstrate that our distance measure can be applied to reconstruct the consensus phylogenetic tree of a number of Plasmodium parasites from their genomes, the statistical bias of which would mislead conventional analysis methods. Our approach is also used to successfully construct a plausible evolutionary tree for the γ-Proteobacteria group whose genomes are known to contain many horizontally transferred genes.

Computational characterization of chromatin domain boundary-associated genomic elements

PubMed Central

Hong, Seungpyo

2017-01-01

Abstract Topologically associated domains (TADs) are 3D genomic structures with high internal interactions that play important roles in genome compaction and gene regulation. Their genomic locations and their association with CCCTC-binding factor (CTCF)-binding sites and transcription start sites (TSSs) were recently reported. However, the relationship between TADs and other genomic elements has not been systematically evaluated. This was addressed in the present study, with a focus on the enrichment of these genomic elements and their ability to predict the TAD boundary region. We found that consensus CTCF-binding sites were strongly associated with TAD boundaries as well as with the transcription factors (TFs) Zinc finger protein (ZNF)143 and Yin Yang (YY)1. TAD boundary-associated genomic elements include DNase I-hypersensitive sites, H3K36 trimethylation, TSSs, RNA polymerase II, and TFs such as Specificity protein 1, ZNF274 and SIX homeobox 5. Computational modeling with these genomic elements suggests that they have distinct roles in TAD boundary formation. We propose a structural model of TAD boundaries based on these findings that provides a basis for studying the mechanism of chromatin structure formation and gene regulation. PMID:28977568

Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

PubMed

Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred; Lötsch, Jörn

2018-06-01

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

Phylogenetic Analysis of Genome Rearrangements among Five Mammalian Orders

PubMed Central

Luo, Haiwei; Arndt, William; Zhang, Yiwei; Shi, Guanqun; Alekseyev, Max; Tang, Jijun; Hughes, Austin L.; Friedman, Robert

2015-01-01

Evolutionary relationships among placental mammalian orders have been controversial. Whole genome sequencing and new computational methods offer opportunities to resolve the relationships among 10 genomes belonging to the mammalian orders Primates, Rodentia, Carnivora, Perissodactyla and Artiodactyla. By application of the double cut and join distance metric, where gene order is the phylogenetic character, we computed genomic distances among the sampled mammalian genomes. With a marsupial outgroup, the gene order tree supported a topology in which Rodentia fell outside the cluster of Primates, Carnivora, Perissodactyla, and Artiodactyla. Results of breakpoint reuse rate and synteny block length analyses were consistent with the prediction of random breakage model, which provided a diagnostic test to support use of gene order as an appropriate phylogenetic character in this study. We the influence of rate differences among lineages and other factors that may contribute to different resolutions of mammalian ordinal relationships by different methods of phylogenetic reconstruction. PMID:22929217

STORMSeq: An Open-Source, User-Friendly Pipeline for Processing Personal Genomics Data in the Cloud

PubMed Central

Karczewski, Konrad J.; Fernald, Guy Haskin; Martin, Alicia R.; Snyder, Michael; Tatonetti, Nicholas P.; Dudley, Joel T.

2014-01-01

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5–10 hours to process a full exome sequence and $30 and 3–8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2. PMID:24454756

An integrative approach to energy, carbon, and redox metabolism in the cyanobacterium Synechocystis sp. PCC 6803

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

Overbeek, Ross; Fonstein, Veronika; Osterman, Andrei

2005-02-15

The team of the Fellowship for Interpretation of Genomes (FIG) under the leadership of Ross Overbeek, began working on this Project in November 2003. During the previous year, the Project was performed at Integrated Genomics Inc. A transition from the industrial environment to the public domain prompted us to adjust some aspects of the Project. Notwithstanding the challenges, we believe that these adjustments had a strong positive impact on our deliverables. Most importantly, the work of the research team led by R. Overbeek resulted in the deployment of a new open source genomic platform, the SEED (Specific Aim 1). Thismore » platform provided a foundation for the development of CyanoSEED a specialized portal to comparative analysis and metabolic reconstruction of all available cyanobacterial genomes (Specific Aim 3). The SEED represents a new generation of software for genome analysis. Briefly, it is a portable and extendable system, containing one of the largest and permanently growing collections of complete and partial genomes. The complete system with annotations and tools is freely available via browsing or via installation on a user's Mac or Linux computer. One of the important unique features of the SEED is the support of metabolic reconstruction and comparative genome analysis via encoding and projection of functional subsystems. During the project period, the FIG research team has validated the new software by developing a significant number of core subsystems, covering many aspects of central metabolism (Specific Aim 2), as well as metabolic areas specific for cyanobacteria and other photoautotrophic organisms (Specific Aim 3). In addition to providing a proof of technology and a starting point for further community-based efforts, these subsystems represent a valuable asset. An extensive coverage of central metabolism provides the bulk of information required for metabolic modeling in Synechocystis sp.PCC 6803. Detailed analysis of several subsystems covering energy, carbon, and redox metabolism in the Synechocystis sp. PCC 6803 and other cyanobacteria has been performed (Specific Aim 4). The main objectives for this year (adjusted to reflect a new, public domain, setting of the Project research team) were: Aim 1. To develop, test, and deploy a new open source system, the SEED, for integrating community-based annotation, and comparative analysis of all publicly available microbial genomes. Develop a comprehensive genomic database by integrating within SEED all publicly available complete and nearly complete genome sequences with special emphasis on genomes of cyanobacteria, phototrophic eukaryotes, and anoxygenic phototrophic bacteria--invaluable for comparative genomic studies of energy and carbon metabolism in Synechocystis sp. PCC 6803. Aim 2. To develop the SEED's biological content in the form of a collection of encoded Subsystems largely covering the conserved cellular machinery in prokaryotes (and central metabolic machinery in eukaryotes). Aim 3. To develop, utilizing core SEED technology, the CyanoSEED--a specialized WEB portal for community-based annotation, and comparative analysis of all publicly available cyanobacterial genomes. Encode the set of additional subsystems representing key metabolic transformations in cyanobacteria and other photoautotrophs. We envisioned this resource as complementary to other public access databases for comparative genomic analysis currently available to the cyanobacterial research community. Aim 4. Perform in-depth analysis of several subsystems covering energy, carbon, and redox metabolism in the Synechocystis sp. PCC 6803 and all other cyanobacteria with available genome sequences. Reveal inconsistencies and gaps in the current knowledge of these subsystems. Use functional and genome context analysis tools in CyanoSEED to predict, whenever possible, candidate genes for inferred functional roles. To disseminate freely these conjectures and predictions by publishing them on CyanoSEED (http://cyanoseed.thefig.info/) and the Subsystems Forum (http://brucella.uchicago.edu/SubsystemForum/) in order to facilitate experimental analysis by our collaborator on this Project and by other experimentalists working in various field of cyanobacterial physiology and biotechnology.« less

High-throughput sequencing of three Lemnoideae (duckweeds) chloroplast genomes from total DNA.

PubMed

Wang, Wenqin; Messing, Joachim

2011-01-01

Chloroplast genomes provide a wealth of information for evolutionary and population genetic studies. Chloroplasts play a particularly important role in the adaption for aquatic plants because they float on water and their major surface is exposed continuously to sunlight. The subfamily of Lemnoideae represents such a collection of aquatic species that because of photosynthesis represents one of the fastest growing plant species on earth. We sequenced the chloroplast genomes from three different genera of Lemnoideae, Spirodela polyrhiza, Wolffiella lingulata and Wolffia australiana by high-throughput DNA sequencing of genomic DNA using the SOLiD platform. Unfractionated total DNA contains high copies of plastid DNA so that sequences from the nucleus and mitochondria can easily be filtered computationally. Remaining sequence reads were assembled into contiguous sequences (contigs) using SOLiD software tools. Contigs were mapped to a reference genome of Lemna minor and gaps, selected by PCR, were sequenced on the ABI3730xl platform. This combinatorial approach yielded whole genomic contiguous sequences in a cost-effective manner. Over 1,000-time coverage of chloroplast from total DNA were reached by the SOLiD platform in a single spot on a quadrant slide without purification. Comparative analysis indicated that the chloroplast genome was conserved in gene number and organization with respect to the reference genome of L. minor. However, higher nucleotide substitution, abundant deletions and insertions occurred in non-coding regions of these genomes, indicating a greater genomic dynamics than expected from the comparison of other related species in the Pooideae. Noticeably, there was no transition bias over transversion in Lemnoideae. The data should have immediate applications in evolutionary biology and plant taxonomy with increased resolution and statistical power.

High-Throughput Sequencing of Three Lemnoideae (Duckweeds) Chloroplast Genomes from Total DNA

PubMed Central

Wang, Wenqin; Messing, Joachim

2011-01-01

Background Chloroplast genomes provide a wealth of information for evolutionary and population genetic studies. Chloroplasts play a particularly important role in the adaption for aquatic plants because they float on water and their major surface is exposed continuously to sunlight. The subfamily of Lemnoideae represents such a collection of aquatic species that because of photosynthesis represents one of the fastest growing plant species on earth. Methods We sequenced the chloroplast genomes from three different genera of Lemnoideae, Spirodela polyrhiza, Wolffiella lingulata and Wolffia australiana by high-throughput DNA sequencing of genomic DNA using the SOLiD platform. Unfractionated total DNA contains high copies of plastid DNA so that sequences from the nucleus and mitochondria can easily be filtered computationally. Remaining sequence reads were assembled into contiguous sequences (contigs) using SOLiD software tools. Contigs were mapped to a reference genome of Lemna minor and gaps, selected by PCR, were sequenced on the ABI3730xl platform. Conclusions This combinatorial approach yielded whole genomic contiguous sequences in a cost-effective manner. Over 1,000-time coverage of chloroplast from total DNA were reached by the SOLiD platform in a single spot on a quadrant slide without purification. Comparative analysis indicated that the chloroplast genome was conserved in gene number and organization with respect to the reference genome of L. minor. However, higher nucleotide substitution, abundant deletions and insertions occurred in non-coding regions of these genomes, indicating a greater genomic dynamics than expected from the comparison of other related species in the Pooideae. Noticeably, there was no transition bias over transversion in Lemnoideae. The data should have immediate applications in evolutionary biology and plant taxonomy with increased resolution and statistical power. PMID:21931804

Identification of Ohnolog Genes Originating from Whole Genome Duplication in Early Vertebrates, Based on Synteny Comparison across Multiple Genomes.

PubMed

Singh, Param Priya; Arora, Jatin; Isambert, Hervé

2015-07-01

Whole genome duplications (WGD) have now been firmly established in all major eukaryotic kingdoms. In particular, all vertebrates descend from two rounds of WGDs, that occurred in their jawless ancestor some 500 MY ago. Paralogs retained from WGD, also coined 'ohnologs' after Susumu Ohno, have been shown to be typically associated with development, signaling and gene regulation. Ohnologs, which amount to about 20 to 35% of genes in the human genome, have also been shown to be prone to dominant deleterious mutations and frequently implicated in cancer and genetic diseases. Hence, identifying ohnologs is central to better understand the evolution of vertebrates and their susceptibility to genetic diseases. Early computational analyses to identify vertebrate ohnologs relied on content-based synteny comparisons between the human genome and a single invertebrate outgroup genome or within the human genome itself. These approaches are thus limited by lineage specific rearrangements in individual genomes. We report, in this study, the identification of vertebrate ohnologs based on the quantitative assessment and integration of synteny conservation between six amniote vertebrates and six invertebrate outgroups. Such a synteny comparison across multiple genomes is shown to enhance the statistical power of ohnolog identification in vertebrates compared to earlier approaches, by overcoming lineage specific genome rearrangements. Ohnolog gene families can be browsed and downloaded for three statistical confidence levels or recompiled for specific, user-defined, significance criteria at http://ohnologs.curie.fr/. In the light of the importance of WGD on the genetic makeup of vertebrates, our analysis provides a useful resource for researchers interested in gaining further insights on vertebrate evolution and genetic diseases.

Identification of Ohnolog Genes Originating from Whole Genome Duplication in Early Vertebrates, Based on Synteny Comparison across Multiple Genomes

PubMed Central

Singh, Param Priya; Arora, Jatin; Isambert, Hervé

2015-01-01

Whole genome duplications (WGD) have now been firmly established in all major eukaryotic kingdoms. In particular, all vertebrates descend from two rounds of WGDs, that occurred in their jawless ancestor some 500 MY ago. Paralogs retained from WGD, also coined ‘ohnologs’ after Susumu Ohno, have been shown to be typically associated with development, signaling and gene regulation. Ohnologs, which amount to about 20 to 35% of genes in the human genome, have also been shown to be prone to dominant deleterious mutations and frequently implicated in cancer and genetic diseases. Hence, identifying ohnologs is central to better understand the evolution of vertebrates and their susceptibility to genetic diseases. Early computational analyses to identify vertebrate ohnologs relied on content-based synteny comparisons between the human genome and a single invertebrate outgroup genome or within the human genome itself. These approaches are thus limited by lineage specific rearrangements in individual genomes. We report, in this study, the identification of vertebrate ohnologs based on the quantitative assessment and integration of synteny conservation between six amniote vertebrates and six invertebrate outgroups. Such a synteny comparison across multiple genomes is shown to enhance the statistical power of ohnolog identification in vertebrates compared to earlier approaches, by overcoming lineage specific genome rearrangements. Ohnolog gene families can be browsed and downloaded for three statistical confidence levels or recompiled for specific, user-defined, significance criteria at http://ohnologs.curie.fr/. In the light of the importance of WGD on the genetic makeup of vertebrates, our analysis provides a useful resource for researchers interested in gaining further insights on vertebrate evolution and genetic diseases. PMID:26181593

Human genome and open source: balancing ethics and business.

PubMed

Marturano, Antonio

2011-01-01

The Human Genome Project has been completed thanks to a massive use of computer techniques, as well as the adoption of the open-source business and research model by the scientists involved. This model won over the proprietary model and allowed a quick propagation and feedback of research results among peers. In this paper, the author will analyse some ethical and legal issues emerging by the use of such computer model in the Human Genome property rights. The author will argue that the Open Source is the best business model, as it is able to balance business and human rights perspectives.

Use of Computational Functional Genomics in Drug Discovery and Repurposing for Analgesic Indications.

PubMed

Lötsch, Jörn; Kringel, Dario

2018-06-01

The novel research area of functional genomics investigates biochemical, cellular, or physiological properties of gene products with the goal of understanding the relationship between the genome and the phenotype. These developments have made analgesic drug research a data-rich discipline mastered only by making use of parallel developments in computer science, including the establishment of knowledge bases, mining methods for big data, machine-learning, and artificial intelligence, (Table ) which will be exemplarily introduced in the following. © 2018 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

The Human Genome Project: Information access, management, and regulation. Final report

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

McInerney, J.D.; Micikas, L.B.

The Human Genome Project is a large, internationally coordinated effort in biological research directed at creating a detailed map of human DNA. This report describes the access of information, management, and regulation of the project. The project led to the development of an instructional module titled The Human Genome Project: Biology, Computers, and Privacy, designed for use in high school biology classes. The module consists of print materials and both Macintosh and Windows versions of related computer software-Appendix A contains a copy of the print materials and discs containing the two versions of the software.

Real-Time Pathogen Detection in the Era of Whole-Genome Sequencing and Big Data: Comparison of k-mer and Site-Based Methods for Inferring the Genetic Distances among Tens of Thousands of Salmonella Samples

DOE PAGES

Pettengill, James B.; Pightling, Arthur W.; Baugher, Joseph D.; ...

2016-11-10

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging duemore » to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). Finally, when analyzing empirical data (wholegenome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases.« less

Real-Time Pathogen Detection in the Era of Whole-Genome Sequencing and Big Data: Comparison of k-mer and Site-Based Methods for Inferring the Genetic Distances among Tens of Thousands of Salmonella Samples

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

Pettengill, James B.; Pightling, Arthur W.; Baugher, Joseph D.

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging duemore » to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). Finally, when analyzing empirical data (wholegenome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases.« less

The post-genomic era of biological network alignment.

PubMed

Faisal, Fazle E; Meng, Lei; Crawford, Joseph; Milenković, Tijana

2015-12-01

Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches' biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.

''After the Genome 5 Conference'' to be held October 6-10, 1999 in Jackson Hole, Wyoming

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

Roger Brent

OAK B139 The postgenomic era is arriving faster than anyone had imagined--sometime during 2000 we'll have a large fraction of the human genome sequence. Heretofore, our understanding of function has come from non-industrial experiments whose conclusions were largely framed in human language. The advent of large amounts of sequence data, and of ''functional genomic'' data types such as mRNA expression data, have changed this picture. These data share the feature that individual observations and measurements are typically relatively low value adding. Such data is now being generated so rapidly that the amount of information contained in it will surpass themore » amount of biological information collected by traditional means. It is tantalizing to envision using genomic information to create a quantitative biology with a very strong data component. Unfortunately, we are very early in our understanding of how to ''compute on'' genomic information so as to extract biological knowledge from i t. In fact, some current efforts to come to grips with genomic information often resemble a computer savvy library science, where the most important issues concern categories, classification schemes, and information retrieval. When exploring new libraries, a measure of cataloging and inventory is surely inevitable. However, at some point we will need to move from library science to scholarship.We would like to achieve a quantitative and predictive understanding of biological function. We realize that making the bridge from knowledge of systems to the sets of abstractions that constitute computable entities is not easy. The After the Genome meetings were started in 1995 to help the biological community think about and prepare for the changes in biological research in the face of the oncoming flow of genomic information. The term ''After the Genome'' refers to a future in which complete inventories of the gene products of entire organisms become available.Since then, many more biologists have become cognizant of the issues raised by this future, and, in response, the organizers intend to distinguish this meeting from other ''postgenomic'' meetings by bringing together intellectuals from subject fields far outside of conventional biology with the expectation that this will help focus thinking beyond the immediate future. To this end, After the Genome 5 will bring together industrial and university researchers, including: (1) Physicists, chemists, and engineers who are devising and using new data gathering techniques, such as microarrays, protein mass spectrometry, and single molecule measurements (2) Computer scientists from fields as diverse as geology and wargames, who have experience moving from broad knowledge of systems to analysis that results in models and simulations (3) Neurobiologists and computer scientists who combine physiological experimentation and computer modeling to understand single cells and small networks of cells (4) Biologists who are trying to model genetic networks (5) All-around visionary thinkers (6) policy makers, to suggest how to convey any good ideas to organizations that can commit resources to them.« less

"After the Genome 5, Conference to be held October 6-10, 1999, Jackson Hole, Wyoming"

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

Brent, Roger

The postgenomic era is arriving faster than anyone had imagined-- sometime during 2000 we'll have a large fraction of the human genome sequence. Heretofore, our understanding of function has come from non-industrial experiments whose conclusions were largely framed in human language. The advent of large amounts of sequence data, and of "functional genomic" data types such as mRNA expression data, have changed this picture. These data share the feature that individual observations and measurements are typically relatively low value adding. Such data is now being generated so rapidly that the amount of information contained in it will surpass the amountmore » of biological information collected by traditional means. It is tantalizing to envision using genomic information to create a quantitative biology with a very strong data component. Unfortunately, we are very early in our understanding of how to "compute on" genomic information so as to extract biological knowledge from it. In fact, some current efforts to come to grips with genomic information often resemble a computer savvy library science, where the most important issues concern categories, classification schemes, and information retrieval. When exploring new libraries, a measure of cataloging and inventory is surely inevitable. However, at some point we will need to move from library science to scholarship. We would like to achieve a quantitative and predictive understanding of biological function. We realize that making the bridge from knowledge of systems to the sets of abstractions that constitute computable entities is not easy. The After the Genome meetings were started in 1995 to help the biological community think about and prepare for the changes in biological research in the face of the oncoming flow of genomic information. The term "After the Genome" refers to a future in which complete inventories of the gene products of entire organisms become available. Since then, many more biologists have become cognizant of the issues raised by this future, and, in response, the organizers intend to distinguish this meeting from other "postgenomic" meetings by bringing together intellectuals from subject fields far outside of conventional biology with the expectation that this will help focus thinking beyond the immediate future. To this end, After the Genome 5 will bring together industrial and university researchers, including: 1) Physicists, chemists, and engineers who are devising and using new data gathering techniques, such as microarrays, protein mass spectrometry, and single molecule measurements 2) Computer scientists from fields as diverse as geology and wargames, who have experience moving from broad knowledge of systems to analysis that results in models and simulations 3) Neurobiologists and computer scientists who combine physiological experimentation and computer modeling to understand single cells and small networks of cells 4) Biologists who are trying to model genetic networks 5) All- around visionary thinkers 7) policy makers, to suggest how to convey any good ideas to organizations that can commit resources to them.« less

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

BlueSNP: R package for highly scalable genome-wide association studies using Hadoop clusters.

PubMed

Huang, Hailiang; Tata, Sandeep; Prill, Robert J

2013-01-01

Computational workloads for genome-wide association studies (GWAS) are growing in scale and complexity outpacing the capabilities of single-threaded software designed for personal computers. The BlueSNP R package implements GWAS statistical tests in the R programming language and executes the calculations across computer clusters configured with Apache Hadoop, a de facto standard framework for distributed data processing using the MapReduce formalism. BlueSNP makes computationally intensive analyses, such as estimating empirical p-values via data permutation, and searching for expression quantitative trait loci over thousands of genes, feasible for large genotype-phenotype datasets. http://github.com/ibm-bioinformatics/bluesnp

Integrative Genomics and Computational Systems Medicine

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

McDermott, Jason E.; Huang, Yufei; Zhang, Bing

The exponential growth in generation of large amounts of genomic data from biological samples has driven the emerging field of systems medicine. This field is promising because it improves our understanding of disease processes at the systems level. However, the field is still in its young stage. There exists a great need for novel computational methods and approaches to effectively utilize and integrate various omics data.

New additions to the cancer precision medicine toolkit.

PubMed

Mardis, Elaine R

2018-04-13

New computational and database-driven tools are emerging to aid in the interpretation of cancer genomic data as its use becomes more common in clinical evidence-based cancer medicine. Two such open source tools, published recently in Genome Medicine, provide important advances to address the clinical cancer genomics data interpretation bottleneck.

Genome-wide computational prediction and analysis of core promoter elements across plant monocots and dicots

USDA-ARS?s Scientific Manuscript database

Transcription initiation, essential to gene expression regulation, involves recruitment of basal transcription factors to the core promoter elements (CPEs). The distribution of currently known CPEs across plant genomes is largely unknown. This is the first large scale genome-wide report on the compu...

75 FR 1064 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-08

... 20892, 301-435- 1033, [email protected] . Name of Committee: Genes, Genomes, and Genetics Integrated Review Group; Molecular Genetics B Study Section. Date: February 3-4, 2010. Time: 7 p.m. to 6 p.m. Agenda... Committee: Genes, Genomes, and Genetics Integrated Review Group; Genomics, Computational Biology and...

SSGP: SNP-set based genomic prediction to incorporate biological information

USDA-ARS?s Scientific Manuscript database

Genomic prediction has emerged as an effective approach in plant and animal breeding and in precision medicine. Much research has been devoted to an improved accuracy in genomic prediction, and one of the potential ways is to incorporate biological information. Due to the statistical and computation...

Producing genome structure populations with the dynamic and automated PGS software.

PubMed

Hua, Nan; Tjong, Harianto; Shin, Hanjun; Gong, Ke; Zhou, Xianghong Jasmine; Alber, Frank

2018-05-01

Chromosome conformation capture technologies such as Hi-C are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and interchromosomal interactions. We pioneered a probabilistic approach for the generation of a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome's organization in space and time. We provide a user-friendly software package, called PGS, which runs on local machines (for practice runs) and high-performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix, along with information about genome segmentation, and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command-line knowledge is sufficient for using this software. A typical running time of the pipeline is ∼3 d with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at topological-associated domain (TAD)-level resolution.

Genome Partitioner: A web tool for multi-level partitioning of large-scale DNA constructs for synthetic biology applications.

PubMed

Christen, Matthias; Del Medico, Luca; Christen, Heinz; Christen, Beat

2017-01-01

Recent advances in lower-cost DNA synthesis techniques have enabled new innovations in the field of synthetic biology. Still, efficient design and higher-order assembly of genome-scale DNA constructs remains a labor-intensive process. Given the complexity, computer assisted design tools that fragment large DNA sequences into fabricable DNA blocks are needed to pave the way towards streamlined assembly of biological systems. Here, we present the Genome Partitioner software implemented as a web-based interface that permits multi-level partitioning of genome-scale DNA designs. Without the need for specialized computing skills, biologists can submit their DNA designs to a fully automated pipeline that generates the optimal retrosynthetic route for higher-order DNA assembly. To test the algorithm, we partitioned a 783 kb Caulobacter crescentus genome design. We validated the partitioning strategy by assembling a 20 kb test segment encompassing a difficult to synthesize DNA sequence. Successful assembly from 1 kb subblocks into the 20 kb segment highlights the effectiveness of the Genome Partitioner for reducing synthesis costs and timelines for higher-order DNA assembly. The Genome Partitioner is broadly applicable to translate DNA designs into ready to order sequences that can be assembled with standardized protocols, thus offering new opportunities to harness the diversity of microbial genomes for synthetic biology applications. The Genome Partitioner web tool can be accessed at https://christenlab.ethz.ch/GenomePartitioner.

Genome measures used for quality control are dependent on gene function and ancestry.

PubMed

Wang, Jing; Raskin, Leon; Samuels, David C; Shyr, Yu; Guo, Yan

2015-02-01

The transition/transversion (Ti/Tv) ratio and heterozygous/nonreference-homozygous (het/nonref-hom) ratio have been commonly computed in genetic studies as a quality control (QC) measurement. Additionally, these two ratios are helpful in our understanding of the patterns of DNA sequence evolution. To thoroughly understand these two genomic measures, we performed a study using 1000 Genomes Project (1000G) released genotype data (N=1092). An additional two datasets (N=581 and N=6) were used to validate our findings from the 1000G dataset. We compared the two ratios among continental ancestry, genome regions and gene functionality. We found that the Ti/Tv ratio can be used as a quality indicator for single nucleotide polymorphisms inferred from high-throughput sequencing data. The Ti/Tv ratio varies greatly by genome region and functionality, but not by ancestry. The het/nonref-hom ratio varies greatly by ancestry, but not by genome regions and functionality. Furthermore, extreme guanine + cytosine content (either high or low) is negatively associated with the Ti/Tv ratio magnitude. Thus, when performing QC assessment using these two measures, care must be taken to apply the correct thresholds based on ancestry and genome region. Failure to take these considerations into account at the QC stage will bias any following analysis. yan.guo@vanderbilt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The effect of rare alleles on estimated genomic relationships from whole genome sequence data.

PubMed

Eynard, Sonia E; Windig, Jack J; Leroy, Grégoire; van Binsbergen, Rianne; Calus, Mario P L

2015-03-12

Relationships between individuals and inbreeding coefficients are commonly used for breeding decisions, but may be affected by the type of data used for their estimation. The proportion of variants with low Minor Allele Frequency (MAF) is larger in whole genome sequence (WGS) data compared to Single Nucleotide Polymorphism (SNP) chips. Therefore, WGS data provide true relationships between individuals and may influence breeding decisions and prioritisation for conservation of genetic diversity in livestock. This study identifies differences between relationships and inbreeding coefficients estimated using pedigree, SNP or WGS data for 118 Holstein bulls from the 1000 Bull genomes project. To determine the impact of rare alleles on the estimates we compared three scenarios of MAF restrictions: variants with a MAF higher than 5%, variants with a MAF higher than 1% and variants with a MAF between 1% and 5%. We observed significant differences between estimated relationships and, although less significantly, inbreeding coefficients from pedigree, SNP or WGS data, and between MAF restriction scenarios. Computed correlations between pedigree and genomic relationships, within groups with similar relationships, ranged from negative to moderate for both estimated relationships and inbreeding coefficients, but were high between estimates from SNP and WGS (0.49 to 0.99). Estimated relationships from genomic information exhibited higher variation than from pedigree. Inbreeding coefficients analysis showed that more complete pedigree records lead to higher correlation between inbreeding coefficients from pedigree and genomic data. Finally, estimates and correlations between additive genetic (A) and genomic (G) relationship matrices were lower, and variances of the relationships were larger when accounting for allele frequencies than without accounting for allele frequencies. Using pedigree data or genomic information, and including or excluding variants with a MAF below 5% showed significant differences in relationship and inbreeding coefficient estimates. Estimated relationships and inbreeding coefficients are the basis for selection decisions. Therefore, it can be expected that using WGS instead of SNP can affect selection decision. Inclusion of rare variants will give access to the variation they carry, which is of interest for conservation of genetic diversity.

Detection of genomic rearrangements in cucumber using genomecmp software

NASA Astrophysics Data System (ADS)

Kulawik, Maciej; Pawełkowicz, Magdalena Ewa; Wojcieszek, Michał; PlÄ der, Wojciech; Nowak, Robert M.

2017-08-01

Comparative genomic by increasing information about the genomes sequences available in the databases is a rapidly evolving science. A simple comparison of the general features of genomes such as genome size, number of genes, and chromosome number presents an entry point into comparative genomic analysis. Here we present the utility of the new tool genomecmp for finding rearrangements across the compared sequences and applications in plant comparative genomics.

Computing patient data in the cloud: practical and legal considerations for genetics and genomics research in Europe and internationally.

PubMed

Molnár-Gábor, Fruzsina; Lueck, Rupert; Yakneen, Sergei; Korbel, Jan O

2017-06-20

Biomedical research is becoming increasingly large-scale and international. Cloud computing enables the comprehensive integration of genomic and clinical data, and the global sharing and collaborative processing of these data within a flexibly scalable infrastructure. Clouds offer novel research opportunities in genomics, as they facilitate cohort studies to be carried out at unprecedented scale, and they enable computer processing with superior pace and throughput, allowing researchers to address questions that could not be addressed by studies using limited cohorts. A well-developed example of such research is the Pan-Cancer Analysis of Whole Genomes project, which involves the analysis of petabyte-scale genomic datasets from research centers in different locations or countries and different jurisdictions. Aside from the tremendous opportunities, there are also concerns regarding the utilization of clouds; these concerns pertain to perceived limitations in data security and protection, and the need for due consideration of the rights of patient donors and research participants. Furthermore, the increased outsourcing of information technology impedes the ability of researchers to act within the realm of existing local regulations owing to fundamental differences in the understanding of the right to data protection in various legal systems. In this Opinion article, we address the current opportunities and limitations of cloud computing and highlight the responsible use of federated and hybrid clouds that are set up between public and private partners as an adequate solution for genetics and genomics research in Europe, and under certain conditions between Europe and international partners. This approach could represent a sensible middle ground between fragmented individual solutions and a "one-size-fits-all" approach.

IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses.

PubMed

Paez-Espino, David; Chen, I-Min A; Palaniappan, Krishna; Ratner, Anna; Chu, Ken; Szeto, Ernest; Pillay, Manoj; Huang, Jinghua; Markowitz, Victor M; Nielsen, Torben; Huntemann, Marcel; K Reddy, T B; Pavlopoulos, Georgios A; Sullivan, Matthew B; Campbell, Barbara J; Chen, Feng; McMahon, Katherine; Hallam, Steve J; Denef, Vincent; Cavicchioli, Ricardo; Caffrey, Sean M; Streit, Wolfgang R; Webster, John; Handley, Kim M; Salekdeh, Ghasem H; Tsesmetzis, Nicolas; Setubal, Joao C; Pope, Phillip B; Liu, Wen-Tso; Rivers, Adam R; Ivanova, Natalia N; Kyrpides, Nikos C

2017-01-04

Viruses represent the most abundant life forms on the planet. Recent experimental and computational improvements have led to a dramatic increase in the number of viral genome sequences identified primarily from metagenomic samples. As a result of the expanding catalog of metagenomic viral sequences, there exists a need for a comprehensive computational platform integrating all these sequences with associated metadata and analytical tools. Here we present IMG/VR (https://img.jgi.doe.gov/vr/), the largest publicly available database of 3908 isolate reference DNA viruses with 264 413 computationally identified viral contigs from >6000 ecologically diverse metagenomic samples. Approximately half of the viral contigs are grouped into genetically distinct quasi-species clusters. Microbial hosts are predicted for 20 000 viral sequences, revealing nine microbial phyla previously unreported to be infected by viruses. Viral sequences can be queried using a variety of associated metadata, including habitat type and geographic location of the samples, or taxonomic classification according to hallmark viral genes. IMG/VR has a user-friendly interface that allows users to interrogate all integrated data and interact by comparing with external sequences, thus serving as an essential resource in the viral genomics community. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies.

PubMed

Standish, Kristopher A; Carland, Tristan M; Lockwood, Glenn K; Pfeiffer, Wayne; Tatineni, Mahidhar; Huang, C Chris; Lamberth, Sarah; Cherkas, Yauheniya; Brodmerkel, Carrie; Jaeger, Ed; Smith, Lance; Rajagopal, Gunaretnam; Curran, Mark E; Schork, Nicholas J

2015-09-22

Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost. We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study. We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging 'big data' problems in biomedical research brought on by the expansion of NGS technologies.

Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population.

PubMed

Hozé, C; Fritz, S; Phocas, F; Boichard, D; Ducrocq, V; Croiseau, P

2014-01-01

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A new tool called DISSECT for analysing large genomic data sets using a Big Data approach

PubMed Central

Canela-Xandri, Oriol; Law, Andy; Gray, Alan; Woolliams, John A.; Tenesa, Albert

2015-01-01

Large-scale genetic and genomic data are increasingly available and the major bottleneck in their analysis is a lack of sufficiently scalable computational tools. To address this problem in the context of complex traits analysis, we present DISSECT. DISSECT is a new and freely available software that is able to exploit the distributed-memory parallel computational architectures of compute clusters, to perform a wide range of genomic and epidemiologic analyses, which currently can only be carried out on reduced sample sizes or under restricted conditions. We demonstrate the usefulness of our new tool by addressing the challenge of predicting phenotypes from genotype data in human populations using mixed-linear model analysis. We analyse simulated traits from 470,000 individuals genotyped for 590,004 SNPs in ∼4 h using the combined computational power of 8,400 processor cores. We find that prediction accuracies in excess of 80% of the theoretical maximum could be achieved with large sample sizes. PMID:26657010

Genomic dark matter: the reliability of short read mapping illustrated by the genome mappability score.

PubMed

Lee, Hayan; Schatz, Michael C

2012-08-15

Genome resequencing and short read mapping are two of the primary tools of genomics and are used for many important applications. The current state-of-the-art in mapping uses the quality values and mapping quality scores to evaluate the reliability of the mapping. These attributes, however, are assigned to individual reads and do not directly measure the problematic repeats across the genome. Here, we present the Genome Mappability Score (GMS) as a novel measure of the complexity of resequencing a genome. The GMS is a weighted probability that any read could be unambiguously mapped to a given position and thus measures the overall composition of the genome itself. We have developed the Genome Mappability Analyzer to compute the GMS of every position in a genome. It leverages the parallelism of cloud computing to analyze large genomes, and enabled us to identify the 5-14% of the human, mouse, fly and yeast genomes that are difficult to analyze with short reads. We examined the accuracy of the widely used BWA/SAMtools polymorphism discovery pipeline in the context of the GMS, and found discovery errors are dominated by false negatives, especially in regions with poor GMS. These errors are fundamental to the mapping process and cannot be overcome by increasing coverage. As such, the GMS should be considered in every resequencing project to pinpoint the 'dark matter' of the genome, including of known clinically relevant variations in these regions. The source code and profiles of several model organisms are available at http://gma-bio.sourceforge.net

The cost of large numbers of hypothesis tests on power, effect size and sample size.

PubMed

Lazzeroni, L C; Ray, A

2012-01-01

Advances in high-throughput biology and computer science are driving an exponential increase in the number of hypothesis tests in genomics and other scientific disciplines. Studies using current genotyping platforms frequently include a million or more tests. In addition to the monetary cost, this increase imposes a statistical cost owing to the multiple testing corrections needed to avoid large numbers of false-positive results. To safeguard against the resulting loss of power, some have suggested sample sizes on the order of tens of thousands that can be impractical for many diseases or may lower the quality of phenotypic measurements. This study examines the relationship between the number of tests on the one hand and power, detectable effect size or required sample size on the other. We show that once the number of tests is large, power can be maintained at a constant level, with comparatively small increases in the effect size or sample size. For example at the 0.05 significance level, a 13% increase in sample size is needed to maintain 80% power for ten million tests compared with one million tests, whereas a 70% increase in sample size is needed for 10 tests compared with a single test. Relative costs are less when measured by increases in the detectable effect size. We provide an interactive Excel calculator to compute power, effect size or sample size when comparing study designs or genome platforms involving different numbers of hypothesis tests. The results are reassuring in an era of extreme multiple testing.

Millstone: software for multiplex microbial genome analysis and engineering

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

Goodman, Daniel B.; Kuznetsov, Gleb; Lajoie, Marc J.

Inexpensive DNA sequencing and advances in genome editing have made computational analysis a major rate-limiting step in adaptive laboratory evolution and microbial genome engineering. Here, we describe Millstone, a web-based platform that automates genotype comparison and visualization for projects with up to hundreds of genomic samples. To enable iterative genome engineering, Millstone allows users to design oligonucleotide libraries and create successive versions of reference genomes. Millstone is open source and easily deployable to a cloud platform, local cluster, or desktop, making it a scalable solution for any lab.

Millstone: software for multiplex microbial genome analysis and engineering.

PubMed

Goodman, Daniel B; Kuznetsov, Gleb; Lajoie, Marc J; Ahern, Brian W; Napolitano, Michael G; Chen, Kevin Y; Chen, Changping; Church, George M

2017-05-25

Inexpensive DNA sequencing and advances in genome editing have made computational analysis a major rate-limiting step in adaptive laboratory evolution and microbial genome engineering. We describe Millstone, a web-based platform that automates genotype comparison and visualization for projects with up to hundreds of genomic samples. To enable iterative genome engineering, Millstone allows users to design oligonucleotide libraries and create successive versions of reference genomes. Millstone is open source and easily deployable to a cloud platform, local cluster, or desktop, making it a scalable solution for any lab.

Millstone: software for multiplex microbial genome analysis and engineering

DOE PAGES

Goodman, Daniel B.; Kuznetsov, Gleb; Lajoie, Marc J.; ...

2017-05-25

Inexpensive DNA sequencing and advances in genome editing have made computational analysis a major rate-limiting step in adaptive laboratory evolution and microbial genome engineering. Here, we describe Millstone, a web-based platform that automates genotype comparison and visualization for projects with up to hundreds of genomic samples. To enable iterative genome engineering, Millstone allows users to design oligonucleotide libraries and create successive versions of reference genomes. Millstone is open source and easily deployable to a cloud platform, local cluster, or desktop, making it a scalable solution for any lab.

Comparative genomics of Bacillus anthracis from the wool industry highlights polymorphisms of lineage A.Br.Vollum.

PubMed

Derzelle, Sylviane; Aguilar-Bultet, Lisandra; Frey, Joachim

2016-12-01

With the advent of affordable next-generation sequencing (NGS) technologies, major progress has been made in the understanding of the population structure and evolution of the B. anthracis species. Here we report the use of whole genome sequencing and computer-based comparative analyses to characterize six strains belonging to the A.Br.Vollum lineage. These strains were isolated in Switzerland, in 1981, during iterative cases of anthrax involving workers in a textile plant processing cashmere wool from the Indian subcontinent. We took advantage of the hundreds of currently available B. anthracis genomes in public databases, to investigate the genetic diversity existing within the A.Br.Vollum lineage and to position the six Swiss isolates into the worldwide B. anthracis phylogeny. Thirty additional genomes related to the A.Br.Vollum group were identified by whole-genome single nucleotide polymorphism (SNP) analysis, including two strains forming a new evolutionary branch at the basis of the A.Br.Vollum lineage. This new phylogenetic lineage (termed A.Br.H9401) splits off the branch leading to the A.Br.Vollum group soon after its divergence to the other lineages of the major A clade (i.e. 6 SNPs). The available dataset of A.Br.Vollum genomes were resolved into 2 distinct groups. Isolates from the Swiss wool processing facility clustered together with two strains from Pakistan and one strain of unknown origin isolated from yarn. They were clearly differentiated (69 SNPs) from the twenty-five other A.Br.Vollum strains located on the branch leading to the terminal reference strain A0488 of the lineage. Novel analytic assays specific to these new subgroups were developed for the purpose of rapid molecular epidemiology. Whole genome SNP surveys greatly expand upon our knowledge on the sub-structure of the A.Br.Vollum lineage. Possible origin and route of spread of this lineage worldwide are discussed. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers

PubMed Central

2014-01-01

Background Although the X chromosome is the second largest bovine chromosome, markers on the X chromosome are not used for genomic prediction in some countries and populations. In this study, we presented a method for computing genomic relationships using X chromosome markers, investigated the accuracy of imputation from a low density (7K) to the 54K SNP (single nucleotide polymorphism) panel, and compared the accuracy of genomic prediction with and without using X chromosome markers. Methods The impact of considering X chromosome markers on prediction accuracy was assessed using data from Nordic Holstein bulls and different sets of SNPs: (a) the 54K SNPs for reference and test animals, (b) SNPs imputed from the 7K to the 54K SNP panel for test animals, (c) SNPs imputed from the 7K to the 54K panel for half of the reference animals, and (d) the 7K SNP panel for all animals. Beagle and Findhap were used for imputation. GBLUP (genomic best linear unbiased prediction) models with or without X chromosome markers and with or without a residual polygenic effect were used to predict genomic breeding values for 15 traits. Results Averaged over the two imputation datasets, correlation coefficients between imputed and true genotypes for autosomal markers, pseudo-autosomal markers, and X-specific markers were 0.971, 0.831 and 0.935 when using Findhap, and 0.983, 0.856 and 0.937 when using Beagle. Estimated reliabilities of genomic predictions based on the imputed datasets using Findhap or Beagle were very close to those using the real 54K data. Genomic prediction using all markers gave slightly higher reliabilities than predictions without X chromosome markers. Based on our data which included only bulls, using a G matrix that accounted for sex-linked relationships did not improve prediction, compared with a G matrix that did not account for sex-linked relationships. A model that included a polygenic effect did not recover the loss of prediction accuracy from exclusion of X chromosome markers. Conclusions The results from this study suggest that markers on the X chromosome contribute to accuracy of genomic predictions and should be used for routine genomic evaluation. PMID:25080199

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 biosynthesis and disease resistance. The study demonstrated the advantages of having an integrated approach to gene prediction and developed a computational framework for combining multiple genome annotations. These results, available in the oil palm annotation database ( http://palmxplore.mpob.gov.my ), will provide important resources for studies on the genomes of oil palm and related crops. This article was reviewed by Alexander Kel, Igor Rogozin, and Vladimir A. Kuznetsov.

Validation and Application of a Real-time PCR Protocol for the Specific Detection and Quantification of Clavibacter michiganensis subsp. sepedonicus in Potato.

PubMed

Cho, Min Seok; Park, Duck Hwan; Namgung, Min; Ahn, Tae-Young; Park, Dong Suk

2015-06-01

Clavibacter michiganensis subsp. sepedonicus (Cms) multiplies very rapidly, passing through the vascular strands and into the stems and petioles of a diseased potato. Therefore, the rapid and specific detection of this pathogen is highly important for the effective control of the pathogen. Although several PCR assays have been developed for detection, they cannot afford specific detection of Cms. Therefore, in this study, a computational genome analysis was performed to compare the sequenced genomes of the C. michiganensis subspecies and to identify an appropriate gene for the development of a subspecies-specific PCR primer set (Cms89F/R). The specificity of the primer set based on the putative phage-related protein was evaluated using genomic DNA from seven isolates of Cms and 27 other reference strains. The Cms89F/R primer set was more specific and sensitive than the existing assays in detecting Cms in in vitro using Cms cells and its genomic DNA. This assay was also able to detect at least 1.47×10(2) copies/μl of cloned-amplified target DNA, 5 fg of DNA using genomic DNA or 10(-6) dilution point of 0.12 at OD600 units of cells per reaction using a calibrated cell suspension.

Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency

PubMed Central

Zaboikin, Michail; Freter, Carl

2018-01-01

We describe a method for measuring genome editing efficiency from in silico analysis of high-resolution melt curve data. The melt curve data derived from amplicons of genome-edited or unmodified target sites were processed to remove the background fluorescent signal emanating from free fluorophore and then corrected for temperature-dependent quenching of fluorescence of double-stranded DNA-bound fluorophore. Corrected data were normalized and numerically differentiated to obtain the first derivatives of the melt curves. These were then mathematically modeled as a sum or superposition of minimal number of Gaussian components. Using Gaussian parameters determined by modeling of melt curve derivatives of unedited samples, we were able to model melt curve derivatives from genetically altered target sites where the mutant population could be accommodated using an additional Gaussian component. From this, the proportion contributed by the mutant component in the target region amplicon could be accurately determined. Mutant component computations compared well with the mutant frequency determination from next generation sequencing data. The results were also consistent with our earlier studies that used difference curve areas from high-resolution melt curves for determining the efficiency of genome-editing reagents. The advantage of the described method is that it does not require calibration curves to estimate proportion of mutants in amplicons of genome-edited target sites. PMID:29300734

Simplifier: a web tool to eliminate redundant NGS contigs.

PubMed

Ramos, Rommel Thiago Jucá; Carneiro, Adriana Ribeiro; Azevedo, Vasco; Schneider, Maria Paula; Barh, Debmalya; Silva, Artur

2012-01-01

Modern genomic sequencing technologies produce a large amount of data with reduced cost per base; however, this data consists of short reads. This reduction in the size of the reads, compared to those obtained with previous methodologies, presents new challenges, including a need for efficient algorithms for the assembly of genomes from short reads and for resolving repetitions. Additionally after abinitio assembly, curation of the hundreds or thousands of contigs generated by assemblers demands considerable time and computational resources. We developed Simplifier, a stand-alone software that selectively eliminates redundant sequences from the collection of contigs generated by ab initio assembly of genomes. Application of Simplifier to data generated by assembly of the genome of Corynebacterium pseudotuberculosis strain 258 reduced the number of contigs generated by ab initio methods from 8,004 to 5,272, a reduction of 34.14%; in addition, N50 increased from 1 kb to 1.5 kb. Processing the contigs of Escherichia coli DH10B with Simplifier reduced the mate-paired library 17.47% and the fragment library 23.91%. Simplifier removed redundant sequences from datasets produced by assemblers, thereby reducing the effort required for finalization of genome assembly in tests with data from Prokaryotic organisms. Simplifier is available at http://www.genoma.ufpa.br/rramos/softwares/simplifier.xhtmlIt requires Sun jdk 6 or higher.

VariantSpark: population scale clustering of genotype information.

PubMed

O'Brien, Aidan R; Saunders, Neil F W; Guo, Yi; Buske, Fabian A; Scott, Rodney J; Bauer, Denis C

2015-12-10

Genomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. The widely used Hadoop MapReduce architecture and associated machine learning library, Mahout, provide the means for tackling computationally challenging tasks. However, many genomic analyses do not fit the Map-Reduce paradigm. We therefore utilise the recently developed SPARK engine, along with its associated machine learning library, MLlib, which offers more flexibility in the parallelisation of population-scale bioinformatics tasks. The resulting tool, VARIANTSPARK provides an interface from MLlib to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results. To demonstrate the capabilities of VARIANTSPARK, we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VARIANTSPARK is 80 % faster than the SPARK-based genome clustering approach, ADAM, the comparable implementation using Hadoop/Mahout, as well as ADMIXTURE, a commonly used tool for determining individual ancestries. It is over 90 % faster than traditional implementations using R and Python. The benefits of speed, resource consumption and scalability enables VARIANTSPARK to open up the usage of advanced, efficient machine learning algorithms to genomic data.

SAGE: String-overlap Assembly of GEnomes.

PubMed

Ilie, Lucian; Haider, Bahlul; Molnar, Michael; Solis-Oba, Roberto

2014-09-15

De novo genome assembly of next-generation sequencing data is one of the most important current problems in bioinformatics, essential in many biological applications. In spite of significant amount of work in this area, better solutions are still very much needed. We present a new program, SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers. SAGE benefits from innovations in almost every aspect of the assembly process: error correction of input reads, string-overlap graph construction, read copy counts estimation, overlap graph analysis and reduction, contig extraction, and scaffolding. We hope that these new ideas will help advance the current state-of-the-art in an essential area of research in genomics.

Validation and Application of a Real-time PCR Protocol for the Specific Detection and Quantification of Clavibacter michiganensis subsp. sepedonicus in Potato

PubMed Central

Cho, Min Seok; Park, Duck Hwan; Namgung, Min; Ahn, Tae-Young; Park, Dong Suk

2015-01-01

Clavibacter michiganensis subsp. sepedonicus (Cms) multiplies very rapidly, passing through the vascular strands and into the stems and petioles of a diseased potato. Therefore, the rapid and specific detection of this pathogen is highly important for the effective control of the pathogen. Although several PCR assays have been developed for detection, they cannot afford specific detection of Cms. Therefore, in this study, a computational genome analysis was performed to compare the sequenced genomes of the C. michiganensis subspecies and to identify an appropriate gene for the development of a subspecies-specific PCR primer set (Cms89F/R). The specificity of the primer set based on the putative phage-related protein was evaluated using genomic DNA from seven isolates of Cms and 27 other reference strains. The Cms89F/R primer set was more specific and sensitive than the existing assays in detecting Cms in in vitro using Cms cells and its genomic DNA. This assay was also able to detect at least 1.47×102 copies/μl of cloned-amplified target DNA, 5 fg of DNA using genomic DNA or 10−6 dilution point of 0.12 at OD600 units of cells per reaction using a calibrated cell suspension. PMID:26060431

The solution space of sorting by DCJ.

PubMed

Braga, Marília D V; Stoye, Jens

2010-09-01

In genome rearrangements, the double cut and join (DCJ) operation, introduced by Yancopoulos et al. in 2005, allows one to represent most rearrangement events that could happen in multichromosomal genomes, such as inversions, translocations, fusions, and fissions. No restriction on the genome structure considering linear and circular chromosomes is imposed. An advantage of this general model is that it leads to considerable algorithmic simplifications compared to other genome rearrangement models. Recently, several works concerning the DCJ operation have been published, and in particular, an algorithm was proposed to find an optimal DCJ sequence for sorting one genome into another one. Here we study the solution space of this problem and give an easy-to-compute formula that corresponds to the exact number of optimal DCJ sorting sequences for a particular subset of instances of the problem. We also give an algorithm to count the number of optimal sorting sequences for any instance of the problem. Another interesting result is the demonstration of the possibility of obtaining one optimal sorting sequence by properly replacing any pair of consecutive operations in another optimal sequence. As a consequence, any optimal sorting sequence can be obtained from one other by applying such replacements successively, but the problem of finding the shortest number of replacements between two sorting sequences is still open.

Genome-Wide Detection and Analysis of Multifunctional Genes

PubMed Central

Pritykin, Yuri; Ghersi, Dario; Singh, Mona

2015-01-01

Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms—H. sapiens, D. melanogaster, and S. cerevisiae—and show that, as compared to other annotated genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be more central in protein interaction networks, tend to be more evolutionarily conserved, and are more likely to be essential. We also find that multifunctional genes are significantly more likely to be involved in human disorders. These same features also hold when multifunctionality is defined with respect to molecular functions instead of biological processes. Our analysis uncovers key features about multifunctional genes, and is a step towards a better genome-wide understanding of gene multifunctionality. PMID:26436655

GenoQuery: a new querying module for functional annotation in a genomic warehouse

PubMed Central

Lemoine, Frédéric; Labedan, Bernard; Froidevaux, Christine

2008-01-01

Motivation: We have to cope with both a deluge of new genome sequences and a huge amount of data produced by high-throughput approaches used to exploit these genomic features. Crossing and comparing such heterogeneous and disparate data will help improving functional annotation of genomes. This requires designing elaborate integration systems such as warehouses for storing and querying these data. Results: We have designed a relational genomic warehouse with an original multi-layer architecture made of a databases layer and an entities layer. We describe a new querying module, GenoQuery, which is based on this architecture. We use the entities layer to define mixed queries. These mixed queries allow searching for instances of biological entities and their properties in the different databases, without specifying in which database they should be found. Accordingly, we further introduce the central notion of alternative queries. Such queries have the same meaning as the original mixed queries, while exploiting complementarities yielded by the various integrated databases of the warehouse. We explain how GenoQuery computes all the alternative queries of a given mixed query. We illustrate how useful this querying module is by means of a thorough example. Availability: http://www.lri.fr/~lemoine/GenoQuery/ Contact: chris@lri.fr, lemoine@lri.fr PMID:18586731

04-ERD-052-Final Report

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

Loots, G G; Ovcharenko, I; Collette, N

2007-02-26

Generating the sequence of the human genome represents a colossal achievement for science and mankind. The technical use for the human genome project information holds great promise to cure disease, prevent bioterror threats, as well as to learn about human origins. Yet converting the sequence data into biological meaningful information has not been immediately obvious, and we are still in the preliminary stages of understanding how the genome is organized, what are the functional building blocks and how do these sequences mediate complex biological processes. The overarching goal of this program was to develop novel methods and high throughput strategiesmore » for determining the functions of ''anonymous'' human genes that are evolutionarily deeply conserved in other vertebrates. We coupled analytical tool development and computational predictions regarding gene function with novel high throughput experimental strategies and tested biological predictions in the laboratory. The tools required for comparative genomic data-mining are fundamentally the same whether they are applied to scientific studies of related microbes or the search for functions of novel human genes. For this reason the tools, conceptual framework and the coupled informatics-experimental biology paradigm we developed in this LDRD has many potential scientific applications relevant to LLNL multidisciplinary research in bio-defense, bioengineering, bionanosciences and microbial and environmental genomics.« less

Informational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithms.

PubMed

Ferraro Petrillo, Umberto; Roscigno, Gianluca; Cattaneo, Giuseppe; Giancarlo, Raffaele

2018-06-01

Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e. how many times each k-mer in {A,C,G,T}k occurs in a DNA sequence. Although this problem is computationally very simple and efficiently solvable on a conventional computer, the sheer amount of data available now in applications demands to resort to parallel and distributed computing. Indeed, those type of algorithms have been developed to collect k-mer statistics in the realm of genome assembly. However, they are so specialized to this domain that they do not extend easily to the computation of informational and linguistic indices, concurrently on sets of genomes. Following the well-established approach in many disciplines, and with a growing success also in bioinformatics, to resort to MapReduce and Hadoop to deal with 'Big Data' problems, we present KCH, the first set of MapReduce algorithms able to perform concurrently informational and linguistic analysis of large collections of genomic sequences on a Hadoop cluster. The benchmarking of KCH that we provide indicates that it is quite effective and versatile. It is also competitive with respect to the parallel and distributed algorithms highly specialized to k-mer statistics collection for genome assembly problems. In conclusion, KCH is a much needed addition to the growing number of algorithms and tools that use MapReduce for bioinformatics core applications. The software, including instructions for running it over Amazon AWS, as well as the datasets are available at http://www.di-srv.unisa.it/KCH. umberto.ferraro@uniroma1.it. Supplementary data are available at Bioinformatics online.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

PubMed

Elshazly, Hatem; Souilmi, Yassine; Tonellato, Peter J; Wall, Dennis P; Abouelhoda, Mohamed

2017-01-20

Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

PubMed

Stevens, Hallam

2015-10-01

The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource.

An inherited FGFR2 mutation increased osteogenesis gene expression and result in Crouzon syndrome.

PubMed

Fan, Jiayan; Li, Yinwei; Jia, Renbing; Fan, Xianqun

2018-05-30

FGFR2 encodes a fibroblast growth factor receptor whose mutations are responsible for the Crouzon syndrome, involving craniosynostosis and facial dysostosis with shallow orbits. However, few reports are available quantifying the orbital volume of Crouzon syndrome and there was little direct evidence to show FGFR2 mutation actually influencing orbital morphology. Ten Crouzon syndrome patients underwent a standard ophthalmologic assessment. Morphology study was carried out based on 3-dimensional computed tomography scan to calculate orbital volume. Genomic DNA was extracted from peripheral blood leukocytes of the patients and genomic screening of FGFR2. A three-dimensional computer model was used to analyse the structural positioning of the mutation site that was predicted possible impact on functional of FGFR2 protein. Real-time PCR was performed to analyse the expression of bone maker gene. We describe a FGFR2 mutation (p.G338R, c.1012G > C) in a Chinese family with Crouzon syndrome. Computational analysis showed the mutate protein obviously changes in the local spatial structure compared with wild-type FGFR2. The expression of osteocalcin and alkaline phosphatase two osteoblast specific genes significantly increased in orbital bone directly from patient compared to normal individual, which may lead to facial dysostosis. This is compatible with the shallow and round orbits in our Crouzon syndrome patient. Our study further identified G338R FGFR2 mutation (c1012G > C) lead to inherited Crouzon syndrome. Thus, early intervention, both medically and surgically, as well as disciplined by a multiple interdisciplinary teams are crucial to the management of this disorder.

Private and Efficient Query Processing on Outsourced Genomic Databases.

PubMed

Ghasemi, Reza; Al Aziz, Md Momin; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

2017-09-01

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time consuming and expensive process. Second, it requires large-scale computation and storage systems to process genomic sequences. Third, genomic databases are often owned by different organizations, and thus, not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 Single Nucleotide Polymorphisms (SNPs) in a database of 20 000 records takes around 100 and 150 s, respectively.

Private and Efficient Query Processing on Outsourced Genomic Databases

PubMed Central

Ghasemi, Reza; Al Aziz, Momin; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

2017-01-01

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time-consuming and expensive process. Second, it requires large-scale computation and storage systems to processes genomic sequences. Third, genomic databases are often owned by different organizations and thus not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 SNPs in a database of 20,000 records takes around 100 and 150 seconds, respectively. PMID:27834660

EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles.

PubMed

Dittmar, W James; McIver, Lauren; Michalak, Pawel; Garner, Harold R; Valdez, Gregorio

2014-07-01

The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Comparative genome analysis in the integrated microbial genomes (IMG) system.

PubMed

Markowitz, Victor M; Kyrpides, Nikos C

2007-01-01

Comparative genome analysis is critical for the effective exploration of a rapidly growing number of complete and draft sequences for microbial genomes. The Integrated Microbial Genomes (IMG) system (img.jgi.doe.gov) has been developed as a community resource that provides support for comparative analysis of microbial genomes in an integrated context. IMG allows users to navigate the multidimensional microbial genome data space and focus their analysis on a subset of genes, genomes, and functions of interest. IMG provides graphical viewers, summaries, and occurrence profile tools for comparing genes, pathways, and functions (terms) across specific genomes. Genes can be further examined using gene neighborhoods and compared with sequence alignment tools.