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Semi-supervised learning for peptide identification from shotgun proteomics datasets

Nature Methods volume 4, pages 923–925 (2007)Cite this article

Abstract

Shotgun proteomics uses liquid chromatography–tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.

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Figure 1: Comparison of SEQUEST post-processing methods.
Figure 2: A peptide that was re-ranked by Percolator.

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Acknowledgements

This work was funded by US National Institutes of Health grants P41 RR011823 and R01 EB007057.

Author information

Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, 1705 NE Pacific St., William H. Foege Building, Seattle, 98195, Washington, USA

    Lukas Käll, Jesse D Canterbury, William Stafford Noble & Michael J MacCoss

  2. NEC Laboratories America, Inc.,

    Jason Weston

  3. 4 Independence Way, Suite 200, Princeton, 08540, New Jersey, USA

    Jason Weston

  4. Department of Computer Science and Engineering, University of Washington, AC101 Paul G. Allen Center, 185 Stevens Way, Seattle, 98195, Washington, USA

    William Stafford Noble

Authors
  1. Lukas Käll
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  2. Jesse D Canterbury
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  3. Jason Weston
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  4. William Stafford Noble
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  5. Michael J MacCoss
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Contributions

M.J.M. came up with the initial idea to use decoy PSMs as negative examples. L.K. and W.S.N. came up with the idea to use a support vector machine using semi-supervised learning. L.K. implemented Percolator and performed computational experiments. J.W. provided machine learning expertise. J.D.C. performed initial proof-of-concept experiment and provided mass spectrometry expertise. W.S.N., L.K. and M.J.M. wrote the article.

Corresponding author

Correspondence to Michael J MacCoss.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4, Supplementary Tables 1 and 2, Supplementary Methods, Supplementary Data (PDF 1393 kb)

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Käll, L., Canterbury, J., Weston, J. et al. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4, 923–925 (2007). https://doi.org/10.1038/nmeth1113

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