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VIGOR, an annotation program for small viral genomes

BACKGROUND: The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of...

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Autores principales: Wang, Shiliang, Sundaram, Jaideep P, Spiro, David
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2942859/
https://www.ncbi.nlm.nih.gov/pubmed/20822531
http://dx.doi.org/10.1186/1471-2105-11-451
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author Wang, Shiliang
Sundaram, Jaideep P
Spiro, David
author_facet Wang, Shiliang
Sundaram, Jaideep P
Spiro, David
author_sort Wang, Shiliang
collection PubMed
description BACKGROUND: The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing. RESULTS: We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested. CONCLUSIONS: VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.
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spelling pubmed-29428592010-09-21 VIGOR, an annotation program for small viral genomes Wang, Shiliang Sundaram, Jaideep P Spiro, David BMC Bioinformatics Software BACKGROUND: The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing. RESULTS: We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested. CONCLUSIONS: VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline. BioMed Central 2010-09-07 /pmc/articles/PMC2942859/ /pubmed/20822531 http://dx.doi.org/10.1186/1471-2105-11-451 Text en Copyright ©2010 Wang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Wang, Shiliang
Sundaram, Jaideep P
Spiro, David
VIGOR, an annotation program for small viral genomes
title VIGOR, an annotation program for small viral genomes
title_full VIGOR, an annotation program for small viral genomes
title_fullStr VIGOR, an annotation program for small viral genomes
title_full_unstemmed VIGOR, an annotation program for small viral genomes
title_short VIGOR, an annotation program for small viral genomes
title_sort vigor, an annotation program for small viral genomes
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2942859/
https://www.ncbi.nlm.nih.gov/pubmed/20822531
http://dx.doi.org/10.1186/1471-2105-11-451
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