2011-04-16T20:48:23-07:00
Resource:Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets
curated
http://noble.gs.washington.edu/proj/percolator/
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.
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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.
nlx_98814
Resource:Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets
2010-03-16T00:00:00
17952086
Percolator
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Yeast
Worm
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University of Washington; Washington; USA
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Downloadable database
Software resource
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