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- Käll, LukasDepartment of Genome Sciences, University of Washington (author)
Semi-supervised learning for peptide identification from shotgun proteomics datasets
- Article/chapterEnglish2007
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- 2007-10-21
- Springer Science and Business Media LLC,2007
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- LIBRIS-ID:oai:DiVA.org:kth-48858
- https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-48858URI
- https://doi.org/10.1038/nmeth1113DOI
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- Language:English
- Summary in:English
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- QC 20111124
- 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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- Canterbury, Jesse D. (author)
- Weston, Jason (author)
- Noble, William Stafford (author)
- MacCoss, Michael J. (author)
- Department of Genome Sciences, University of Washington (creator_code:org_t)
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- In:Nature Methods: Springer Science and Business Media LLC4:11, s. 923-9251548-70911548-7105
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