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Semi-supervised learning for peptide identification from shotgun proteomics datasets
Department of Genome Sciences, University of Washington.ORCID iD: 0000-0001-5689-9797
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2007 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 4, no 11, 923-925 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2007. Vol. 4, no 11, 923-925 p.
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Bioinformatics (Computational Biology)
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URN: urn:nbn:se:kth:diva-48858DOI: 10.1038/nmeth1113ISI: 000250575700016PubMedID: 17952086OAI: oai:DiVA.org:kth-48858DiVA: diva2:458731
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QC 20111124Available from: 2011-11-23 Created: 2011-11-23 Last updated: 2017-12-08Bibliographically approved

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Käll, Lukas

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