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Automatic policing of biochemical annotations using genomic correlations.

ABSTRACT: With the increasing role of computational tools in the analysis of sequenced genomes, there is an urgent need to maintain high accuracy of functional annotations. Misannotations can be easily generated and propagated through databases by functional transfer based on sequence homology. We developed and optimized an automatic policing method to detect biochemical misannotations using context genomic correlations. The method works by finding genes with unusually weak genomic correlations in their assigned network positions. We demonstrate the accuracy of the method using a cross-validated approach. In addition, we show that the method identifies a significant number of potential misannotations in Bacillus subtilis, including metabolic assignments already shown to be incorrect experimentally. The experimental analysis of the mispredicted genes forming the leucine degradation pathway in B. subtilis demonstrates that computational policing tools can generate important biological hypotheses.

SUBMITTER: Hsiao TL

PROVIDER: S-EPMC2935526 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Automatic policing of biochemical annotations using genomic correlations.

Hsiao Tzu-Lin TL Revelles Olga O Chen Lifeng L Sauer Uwe U Vitkup Dennis D

Nature chemical biology 20091122 1

With the increasing role of computational tools in the analysis of sequenced genomes, there is an urgent need to maintain high accuracy of functional annotations. Misannotations can be easily generated and propagated through databases by functional transfer based on sequence homology. We developed and optimized an automatic policing method to detect biochemical misannotations using context genomic correlations. The method works by finding genes with unusually weak genomic correlations in their a ...[more]

PMID: 19935659

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Automatic policing of biochemical annotations using genomic correlations.

Publications

Automatic policing of biochemical annotations using genomic correlations.

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