Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

Background: Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors. Results: In a set of 211 previously annotated mouse protein kinases, we found that 201 of the GO annotations returned by AmiGO appear to be inconsistent with the UniProt functions assigned to their human counterparts. In contrast, 97% of the predicted annotations generated using a machine learning approach were consistent with the UniProt annotations of the human counterparts, as well as with available annotations for these mouse protein kinases in the Mouse Kinome databa...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Carson M. Andorf, Drena Dobbs, Vasant Honavar
• Keywords: Annotation, Annotation Errors, BMCBI 2007, Mouse Protein Kinases