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

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. Research Abstract Details 

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

Carson Andorf,Drena Dobbs,Vasant Honavar,

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. Editors Note: Authors from the original publication (Okazaki et al.: Nature 2002, 420:563-73) have provided their response to Andorf et al, directly following the correspondence.

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. Publishing Authors By Initials

C Andorf,D Dobbs,V Honavar,

For similar investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research abstracts see: investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research

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

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: BMC bioinformatics

VOLUME: 8

Page Numbers: 284

Journal Abbreviation: BMC Bioinformatics

ISSN: 1471-2105

DAY: 3

MONTH: 08

YEAR: 2007

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

Number of References:

LANGUAGE: eng

NlmUniqueID: 100965194

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. Keywords Mesh Terms:

KEYWORDS: Sequence Analysis, Protein

MESH TERMS: methods

Chemical & Substance for Abstract: Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. Information

Substance Name: Proteins

Registry Number: 0

Grant and Affiliation Information for Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach.

AFFILIATION:

Country: England

AGENCY: United States NIGMS

GRANT: GM066387

ACRONYM: GM

MEDLINETA: BMC Bioinformatics

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