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

BACKGROUND: 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 p...

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Autores principales: Thakur, Shalabh, Guttman, David S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929753/
https://www.ncbi.nlm.nih.gov/pubmed/27363390
http://dx.doi.org/10.1186/s12859-016-1142-2
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author Thakur, Shalabh
Guttman, David S.
author_facet Thakur, Shalabh
Guttman, David S.
author_sort Thakur, Shalabh
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1142-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-49297532016-07-06 A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies Thakur, Shalabh Guttman, David S. BMC Bioinformatics Software BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1142-2) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-30 /pmc/articles/PMC4929753/ /pubmed/27363390 http://dx.doi.org/10.1186/s12859-016-1142-2 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Thakur, Shalabh
Guttman, David S.
A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies
title A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies
title_full A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies
title_fullStr A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies
title_full_unstemmed A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies
title_short A De-Novo Genome Analysis Pipeline (DeNoGAP) for large-scale comparative prokaryotic genomics studies
title_sort de-novo genome analysis pipeline (denogap) for large-scale comparative prokaryotic genomics studies
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929753/
https://www.ncbi.nlm.nih.gov/pubmed/27363390
http://dx.doi.org/10.1186/s12859-016-1142-2
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