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Automatic policing of biochemical annotations using genomic correlations

Nature Chemical Biology volume 6, pages 34–40 (2010)Cite this article

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.

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Figure 1: Illustration of the developed approach.
Figure 2: Performance of the developed method.
Figure 3: Function of genes forming the yng cluster in B. subtilis.

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Acknowledgements

We thank I. Feldman, A. Rzhetsky, M. de Hoon, S. Gilman and C. Weinreb for comments on the manuscript and valuable discussions. This work was supported in part by US National Institutes of Health grant GM079759 to D.V. and National Centers for Biomedical Computing (MAGNet) grant U54CA121852 to Columbia University.

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Author notes

  1. Tzu-Lin Hsiao, Olga Revelles and Lifeng Chen: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Computational Biology and Bioinformatics and Department of Biomedical Informatics, Columbia University, Irving Cancer Research Center, New York, New York, USA

    Tzu-Lin Hsiao, Lifeng Chen & Dennis Vitkup

  2. Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zurich, Zurich, Switzerland

    Olga Revelles & Uwe Sauer

Authors
  1. Tzu-Lin Hsiao
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  2. Olga Revelles
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Contributions

T.-L.H., L.C. and D.V. performed computational research and data analysis. D.V. conceived and directed computational research. O.R. performed experimental research and analysis. U.S. conceived and directed experimental research. L.C., T.-L.H. and D.V. cowrote the paper. All authors read and edited the manuscript.

Corresponding author

Correspondence to Dennis Vitkup.

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Supplementary Figures 1–4, Supplementary Tables 1 and 2 and Supplementary Methods (PDF 431 kb)

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Hsiao, TL., Revelles, O., Chen, L. et al. Automatic policing of biochemical annotations using genomic correlations. Nat Chem Biol 6, 34–40 (2010). https://doi.org/10.1038/nchembio.266

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