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Brief Communication
Nature Methods - 4, 923 - 925 (2007)
Published online: 21 October 2007; | doi:10.1038/nmeth1113

Semi-supervised learning for peptide identification from shotgun proteomics datasets

Lukas Käll1, Jesse D Canterbury1, Jason Weston2, William Stafford Noble1, 3 & Michael J MacCoss1

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

2  NEC Laboratories America, Inc., 4 Independence Way, Suite 200, Princeton, New Jersey 08540, USA.

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

Correspondence should be addressed to Michael J MacCoss maccoss@u.washington.edu

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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Nature Methods
ISSN: 1548-7091
EISSN: 1548-7105
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