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AGeS: A Software System for Microbial Genome Sequence Annotation
BACKGROUND: The annotation of genomes from next-generation sequencing platforms needs to be rapid, high-throughput, and fully integrated and automated. Although a few Web-based annotation services have recently become available, they may not be the best solution for researchers that need to annotate...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049762/ https://www.ncbi.nlm.nih.gov/pubmed/21408217 http://dx.doi.org/10.1371/journal.pone.0017469 |
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author | Kumar, Kamal Desai, Valmik Cheng, Li Khitrov, Maxim Grover, Deepak Satya, Ravi Vijaya Yu, Chenggang Zavaljevski, Nela Reifman, Jaques |
author_facet | Kumar, Kamal Desai, Valmik Cheng, Li Khitrov, Maxim Grover, Deepak Satya, Ravi Vijaya Yu, Chenggang Zavaljevski, Nela Reifman, Jaques |
author_sort | Kumar, Kamal |
collection | PubMed |
description | BACKGROUND: The annotation of genomes from next-generation sequencing platforms needs to be rapid, high-throughput, and fully integrated and automated. Although a few Web-based annotation services have recently become available, they may not be the best solution for researchers that need to annotate a large number of genomes, possibly including proprietary data, and store them locally for further analysis. To address this need, we developed a standalone software application, the Annotation of microbial Genome Sequences (AGeS) system, which incorporates publicly available and in-house-developed bioinformatics tools and databases, many of which are parallelized for high-throughput performance. METHODOLOGY: The AGeS system supports three main capabilities. The first is the storage of input contig sequences and the resulting annotation data in a central, customized database. The second is the annotation of microbial genomes using an integrated software pipeline, which first analyzes contigs from high-throughput sequencing by locating genomic regions that code for proteins, RNA, and other genomic elements through the Do-It-Yourself Annotation (DIYA) framework. The identified protein-coding regions are then functionally annotated using the in-house-developed Pipeline for Protein Annotation (PIPA). The third capability is the visualization of annotated sequences using GBrowse. To date, we have implemented these capabilities for bacterial genomes. AGeS was evaluated by comparing its genome annotations with those provided by three other methods. Our results indicate that the software tools integrated into AGeS provide annotations that are in general agreement with those provided by the compared methods. This is demonstrated by a >94% overlap in the number of identified genes, a significant number of identical annotated features, and a >90% agreement in enzyme function predictions. |
format | Text |
id | pubmed-3049762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-30497622011-03-15 AGeS: A Software System for Microbial Genome Sequence Annotation Kumar, Kamal Desai, Valmik Cheng, Li Khitrov, Maxim Grover, Deepak Satya, Ravi Vijaya Yu, Chenggang Zavaljevski, Nela Reifman, Jaques PLoS One Research Article BACKGROUND: The annotation of genomes from next-generation sequencing platforms needs to be rapid, high-throughput, and fully integrated and automated. Although a few Web-based annotation services have recently become available, they may not be the best solution for researchers that need to annotate a large number of genomes, possibly including proprietary data, and store them locally for further analysis. To address this need, we developed a standalone software application, the Annotation of microbial Genome Sequences (AGeS) system, which incorporates publicly available and in-house-developed bioinformatics tools and databases, many of which are parallelized for high-throughput performance. METHODOLOGY: The AGeS system supports three main capabilities. The first is the storage of input contig sequences and the resulting annotation data in a central, customized database. The second is the annotation of microbial genomes using an integrated software pipeline, which first analyzes contigs from high-throughput sequencing by locating genomic regions that code for proteins, RNA, and other genomic elements through the Do-It-Yourself Annotation (DIYA) framework. The identified protein-coding regions are then functionally annotated using the in-house-developed Pipeline for Protein Annotation (PIPA). The third capability is the visualization of annotated sequences using GBrowse. To date, we have implemented these capabilities for bacterial genomes. AGeS was evaluated by comparing its genome annotations with those provided by three other methods. Our results indicate that the software tools integrated into AGeS provide annotations that are in general agreement with those provided by the compared methods. This is demonstrated by a >94% overlap in the number of identified genes, a significant number of identical annotated features, and a >90% agreement in enzyme function predictions. Public Library of Science 2011-03-07 /pmc/articles/PMC3049762/ /pubmed/21408217 http://dx.doi.org/10.1371/journal.pone.0017469 Text en This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Kumar, Kamal Desai, Valmik Cheng, Li Khitrov, Maxim Grover, Deepak Satya, Ravi Vijaya Yu, Chenggang Zavaljevski, Nela Reifman, Jaques AGeS: A Software System for Microbial Genome Sequence Annotation |
title | AGeS: A Software System for Microbial Genome Sequence
Annotation |
title_full | AGeS: A Software System for Microbial Genome Sequence
Annotation |
title_fullStr | AGeS: A Software System for Microbial Genome Sequence
Annotation |
title_full_unstemmed | AGeS: A Software System for Microbial Genome Sequence
Annotation |
title_short | AGeS: A Software System for Microbial Genome Sequence
Annotation |
title_sort | ages: a software system for microbial genome sequence
annotation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049762/ https://www.ncbi.nlm.nih.gov/pubmed/21408217 http://dx.doi.org/10.1371/journal.pone.0017469 |
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