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A combined algorithm for genome-wide prediction of protein function

Author

Listed:
  • Edward M. Marcotte

    (Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, University of California, PO Box 951570)

  • Matteo Pellegrini

    (Protein Pathways)

  • Michael J. Thompson

    (Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, University of California, PO Box 951570
    Protein Pathways)

  • Todd O. Yeates

    (Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, University of California, PO Box 951570)

  • David Eisenberg

    (Molecular Biology Institute, UCLA-DOE Laboratory of Structural Biology and Molecular Medicine, University of California, PO Box 951570)

Abstract

The availability of over 20 fully sequenced genomes has driven the development of new methods to find protein function and interactions. Here we group proteins by correlated evolution1, correlated messenger RNA expression patterns2 and patterns of domain fusion3 to determine functional relationships among the 6,217 proteins of the yeast Saccharomyces cerevisiae. Using these methods, we discover over 93,000 pairwise links between functionally related yeast proteins. Links between characterized and uncharacterized proteins allow a general function to be assigned to more than half of the 2,557 previously uncharacterized yeast proteins. Examples of functional links are given for a protein family of previously unknown function, a protein whose human homologues are implicated in colon cancer and the yeast prion Sup35.

Suggested Citation

  • Edward M. Marcotte & Matteo Pellegrini & Michael J. Thompson & Todd O. Yeates & David Eisenberg, 1999. "A combined algorithm for genome-wide prediction of protein function," Nature, Nature, vol. 402(6757), pages 83-86, November.
    • Handle: RePEc:nat:nature:v:402:y:1999:i:6757:d:10.1038_47048
    DOI: 10.1038/47048
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    Cited by:

    1. Christopher Y Park & Aaron K Wong & Casey S Greene & Jessica Rowland & Yuanfang Guan & Lars A Bongo & Rebecca D Burdine & Olga G Troyanskaya, 2013. "Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-14, March.
    2. Han Yan & Kavitha Venkatesan & John E Beaver & Niels Klitgord & Muhammed A Yildirim & Tong Hao & David E Hill & Michael E Cusick & Norbert Perrimon & Frederick P Roth & Marc Vidal, 2010. "A Genome-Wide Gene Function Prediction Resource for Drosophila melanogaster," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-11, August.
    3. Heiko Müller & Francesco Mancuso, 2008. "Identification and Analysis of Co-Occurrence Networks with NetCutter," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-16, September.
    4. Sara Mostafavi & Anna Goldenberg & Quaid Morris, 2012. "Labeling Nodes Using Three Degrees of Propagation," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-10, December.
    5. Antigoni Elefsinioti & Marit Ackermann & Andreas Beyer, 2009. "Accounting for Redundancy when Integrating Gene Interaction Databases," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-9, October.

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