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Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach
BMC Bioinformatics
  • Carson Andorf, Iowa State University
  • Drena Dobbs, Iowa State University
  • Vasant Honavar, Iowa State University
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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 database. Conclusion We conjecture that most of our predicted annotations are, therefore, correct and suggest that the machine learning approach developed here could be routinely used to detect potential errors in GO annotations generated by high-throughput gene annotation projects.

This article is from BMC Bioinformatics 8 (2007): 284, doi: 10.1186/1471-2105-8-284. Posted with permission.

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 work is properly cited.
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Andorf et al
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Carson Andorf, Drena Dobbs and Vasant Honavar. "Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach" BMC Bioinformatics Vol. 8 (2007) p. 284
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