Author(s): Kll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ
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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.
This article was published in Nat Methods
and referenced in Journal of Proteomics & Bioinformatics