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PoGO: Prediction of Gene Ontology terms for fungal proteins

BACKGROUND: Automated protein function prediction methods are the only practical approach for assigning functions to genes obtained from model organisms. Many of the previously reported function annotation methods are of limited utility for fungal protein annotation. They are often trained only to o...

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Autores principales: Jung, Jaehee, Yi, Gangman, Sukno, Serenella A, Thon, Michael R
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882390/
https://www.ncbi.nlm.nih.gov/pubmed/20429880
http://dx.doi.org/10.1186/1471-2105-11-215
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author Jung, Jaehee
Yi, Gangman
Sukno, Serenella A
Thon, Michael R
author_facet Jung, Jaehee
Yi, Gangman
Sukno, Serenella A
Thon, Michael R
author_sort Jung, Jaehee
collection PubMed
description BACKGROUND: Automated protein function prediction methods are the only practical approach for assigning functions to genes obtained from model organisms. Many of the previously reported function annotation methods are of limited utility for fungal protein annotation. They are often trained only to one species, are not available for high-volume data processing, or require the use of data derived by experiments such as microarray analysis. To meet the increasing need for high throughput, automated annotation of fungal genomes, we have developed a tool for annotating fungal protein sequences with terms from the Gene Ontology. RESULTS: We describe a classifier called PoGO (Prediction of Gene Ontology terms) that uses statistical pattern recognition methods to assign Gene Ontology (GO) terms to proteins from filamentous fungi. PoGO is organized as a meta-classifier in which each evidence source (sequence similarity, protein domains, protein structure and biochemical properties) is used to train independent base-level classifiers. The outputs of the base classifiers are used to train a meta-classifier, which provides the final assignment of GO terms. An independent classifier is trained for each GO term, making the system amenable to updating, without having to re-train the whole system. The resulting system is robust. It provides better accuracy and can assign GO terms to a higher percentage of unannotated protein sequences than other methods that we tested. CONCLUSIONS: Our annotation system overcomes many of the shortcomings that we found in other methods. We also provide a web server where users can submit protein sequences to be annotated.
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spelling pubmed-28823902010-06-09 PoGO: Prediction of Gene Ontology terms for fungal proteins Jung, Jaehee Yi, Gangman Sukno, Serenella A Thon, Michael R BMC Bioinformatics Software BACKGROUND: Automated protein function prediction methods are the only practical approach for assigning functions to genes obtained from model organisms. Many of the previously reported function annotation methods are of limited utility for fungal protein annotation. They are often trained only to one species, are not available for high-volume data processing, or require the use of data derived by experiments such as microarray analysis. To meet the increasing need for high throughput, automated annotation of fungal genomes, we have developed a tool for annotating fungal protein sequences with terms from the Gene Ontology. RESULTS: We describe a classifier called PoGO (Prediction of Gene Ontology terms) that uses statistical pattern recognition methods to assign Gene Ontology (GO) terms to proteins from filamentous fungi. PoGO is organized as a meta-classifier in which each evidence source (sequence similarity, protein domains, protein structure and biochemical properties) is used to train independent base-level classifiers. The outputs of the base classifiers are used to train a meta-classifier, which provides the final assignment of GO terms. An independent classifier is trained for each GO term, making the system amenable to updating, without having to re-train the whole system. The resulting system is robust. It provides better accuracy and can assign GO terms to a higher percentage of unannotated protein sequences than other methods that we tested. CONCLUSIONS: Our annotation system overcomes many of the shortcomings that we found in other methods. We also provide a web server where users can submit protein sequences to be annotated. BioMed Central 2010-04-29 /pmc/articles/PMC2882390/ /pubmed/20429880 http://dx.doi.org/10.1186/1471-2105-11-215 Text en Copyright ©2010 Jung 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
Jung, Jaehee
Yi, Gangman
Sukno, Serenella A
Thon, Michael R
PoGO: Prediction of Gene Ontology terms for fungal proteins
title PoGO: Prediction of Gene Ontology terms for fungal proteins
title_full PoGO: Prediction of Gene Ontology terms for fungal proteins
title_fullStr PoGO: Prediction of Gene Ontology terms for fungal proteins
title_full_unstemmed PoGO: Prediction of Gene Ontology terms for fungal proteins
title_short PoGO: Prediction of Gene Ontology terms for fungal proteins
title_sort pogo: prediction of gene ontology terms for fungal proteins
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882390/
https://www.ncbi.nlm.nih.gov/pubmed/20429880
http://dx.doi.org/10.1186/1471-2105-11-215
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