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Nat Methods. 2007 Nov;4(11):923-5. Epub 2007 Oct 21.

Semi-supervised learning for peptide identification from shotgun proteomics datasets.

Author information

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

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.

PMID:
17952086
DOI:
10.1038/nmeth1113
[Indexed for MEDLINE]

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