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Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets (RRID:SCR_005040)Copy Citation Copied
URL: http://noble.gs.washington.edu/proj/percolator/
Proper Citation: Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets (RRID:SCR_005040)
Description: Percolator post-processes the results of a shotgun proteomics database search program, re-ranking peptide-spectrum matches so that the top of the list is enriched for correct matches. 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 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 dataset and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach. The yeast-01 data is available in tab delimetered format. The SEQUEST parameter file and target database for the yeast and worm data are also available.
Synonyms: Percolator
Resource Type: data or information resource, database, software resource
Defining Citation: PMID:17952086
Keywords: worm, yeast, bio.tools
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