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 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 ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Jaehee Jung, Gangman Yi, Serenella A. Sukno, Michael R. Thon
• Keywords: Annotation, BMCBI 2010, Gene Ontology, Protein