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Genome Majority Vote Improves Gene Predictions

Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome...

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Detalles Bibliográficos
Autores principales: Wall, Michael E., Raghavan, Sindhu, Cohn, Judith D., Dunbar, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3219611/
https://www.ncbi.nlm.nih.gov/pubmed/22131910
http://dx.doi.org/10.1371/journal.pcbi.1002284
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author Wall, Michael E.
Raghavan, Sindhu
Cohn, Judith D.
Dunbar, John
author_facet Wall, Michael E.
Raghavan, Sindhu
Cohn, Judith D.
Dunbar, John
author_sort Wall, Michael E.
collection PubMed
description Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome majority vote (GMV) scheme to increase the consistency of gene starts among orthologs. We used a set of validated Escherichia coli genes as a standard to quantify accuracy. Results showed that the GMV algorithm can correct hundreds of gene prediction errors in sets of five or ten genomes while introducing few errors. Using a conservative calculation, we project that GMV would resolve many inconsistencies and errors in publicly available microbial gene maps. Our simple and logical solution provides a notable advance toward accurate gene maps.
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spelling pubmed-32196112011-11-30 Genome Majority Vote Improves Gene Predictions Wall, Michael E. Raghavan, Sindhu Cohn, Judith D. Dunbar, John PLoS Comput Biol Research Article Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome majority vote (GMV) scheme to increase the consistency of gene starts among orthologs. We used a set of validated Escherichia coli genes as a standard to quantify accuracy. Results showed that the GMV algorithm can correct hundreds of gene prediction errors in sets of five or ten genomes while introducing few errors. Using a conservative calculation, we project that GMV would resolve many inconsistencies and errors in publicly available microbial gene maps. Our simple and logical solution provides a notable advance toward accurate gene maps. Public Library of Science 2011-11-17 /pmc/articles/PMC3219611/ /pubmed/22131910 http://dx.doi.org/10.1371/journal.pcbi.1002284 Text en This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. 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
Wall, Michael E.
Raghavan, Sindhu
Cohn, Judith D.
Dunbar, John
Genome Majority Vote Improves Gene Predictions
title Genome Majority Vote Improves Gene Predictions
title_full Genome Majority Vote Improves Gene Predictions
title_fullStr Genome Majority Vote Improves Gene Predictions
title_full_unstemmed Genome Majority Vote Improves Gene Predictions
title_short Genome Majority Vote Improves Gene Predictions
title_sort genome majority vote improves gene predictions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3219611/
https://www.ncbi.nlm.nih.gov/pubmed/22131910
http://dx.doi.org/10.1371/journal.pcbi.1002284
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