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Semi-supervised learning for peptide identification from shotgun proteomics datasets


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

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Authors and Affiliations



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.

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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).

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