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Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines
The annotation of the well-studied organism, Saccharomyces cerevisiae, has been improving over the past decade while there are unresolved debates over the amount of biologically significant open reading frames (ORFs) in yeast genome. We revisited the total count of protein-coding genes in S. cerevis...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707884/ https://www.ncbi.nlm.nih.gov/pubmed/23874379 http://dx.doi.org/10.1371/journal.pone.0064477 |
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author | Lin, Dan Yin, Xin Wang, Xianlong Zhou, Peng Guo, Feng-Biao |
author_facet | Lin, Dan Yin, Xin Wang, Xianlong Zhou, Peng Guo, Feng-Biao |
author_sort | Lin, Dan |
collection | PubMed |
description | The annotation of the well-studied organism, Saccharomyces cerevisiae, has been improving over the past decade while there are unresolved debates over the amount of biologically significant open reading frames (ORFs) in yeast genome. We revisited the total count of protein-coding genes in S. cerevisiae S288c genome using a theoretical approach by combining the Support Vector Machine (SVM) method with six widely used measurements of sequence statistical features. The accuracy of our method is over 99.5% in 10-fold cross-validation. Based on the annotation data in Saccharomyces Genome Database (SGD), we studied the coding capacity of all 1744 ORFs which lack experimental results and suggested that the overall number of chromosomal ORFs encoding proteins in yeast should be 6091 by removing 488 spurious ORFs. The importance of the present work lies in at least two aspects. First, cross-validation and retrospective examination showed the fidelity of our method in recognizing ORFs that likely encode proteins. Second, we have provided a web service that can be accessed at http://cobi.uestc.edu.cn/services/yeast/, which enables the prediction of protein-coding ORFs of the genus Saccharomyces with a high accuracy. |
format | Online Article Text |
id | pubmed-3707884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37078842013-07-19 Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines Lin, Dan Yin, Xin Wang, Xianlong Zhou, Peng Guo, Feng-Biao PLoS One Research Article The annotation of the well-studied organism, Saccharomyces cerevisiae, has been improving over the past decade while there are unresolved debates over the amount of biologically significant open reading frames (ORFs) in yeast genome. We revisited the total count of protein-coding genes in S. cerevisiae S288c genome using a theoretical approach by combining the Support Vector Machine (SVM) method with six widely used measurements of sequence statistical features. The accuracy of our method is over 99.5% in 10-fold cross-validation. Based on the annotation data in Saccharomyces Genome Database (SGD), we studied the coding capacity of all 1744 ORFs which lack experimental results and suggested that the overall number of chromosomal ORFs encoding proteins in yeast should be 6091 by removing 488 spurious ORFs. The importance of the present work lies in at least two aspects. First, cross-validation and retrospective examination showed the fidelity of our method in recognizing ORFs that likely encode proteins. Second, we have provided a web service that can be accessed at http://cobi.uestc.edu.cn/services/yeast/, which enables the prediction of protein-coding ORFs of the genus Saccharomyces with a high accuracy. Public Library of Science 2013-07-10 /pmc/articles/PMC3707884/ /pubmed/23874379 http://dx.doi.org/10.1371/journal.pone.0064477 Text en © 2013 Lin et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lin, Dan Yin, Xin Wang, Xianlong Zhou, Peng Guo, Feng-Biao Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines |
title | Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines |
title_full | Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines |
title_fullStr | Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines |
title_full_unstemmed | Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines |
title_short | Re-Annotation of Protein-Coding Genes in the Genome of Saccharomyces cerevisiae Based on Support Vector Machines |
title_sort | re-annotation of protein-coding genes in the genome of saccharomyces cerevisiae based on support vector machines |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707884/ https://www.ncbi.nlm.nih.gov/pubmed/23874379 http://dx.doi.org/10.1371/journal.pone.0064477 |
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