PoGO: Prediction of Gene Ontology terms for fungal proteins

BMC Bioinformatics

Software

Michael R Thon¹  Serenella A Sukno¹  Gangman Yi²  Jaehee Jung³ 

[1] Centro Hispano-Luso de Investigaciones Agrarias (CIALE), Department of Microbiology and Genetics, University of Salamanca, 37185, Villamayor, Spain;Department of Computer Science, Texas A&M University, 77843, College Station, TX, USA;Department of Computer Science, Texas A&M University, 77843, College Station, TX, USA;Samsung Electronic Co., LTD, 416 Maetan-3dong, 443-742, Yeongtong-gu, Suwon-City, Gyeonggi-do, Korea;

关键词: Base Classifier;    Gene Ontology Term;    Fungal Protein;    Heterogeneous Data Source;    Unbalanced Dataset;   

DOI  :  10.1186/1471-2105-11-215

 received in 2010-01-20, accepted in 2010-04-29,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAutomated 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.ResultsWe 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Jung et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. 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.