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Percolator: Semi-supervised learning for peptide identification from shotgun proteomics datasets

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.

URL: http://noble.gs.washington.edu/proj/percolator/

Resource ID: nlx_98814     Resource Type: Resource     Version: Latest Version


worm, yeast



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software resource, database



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Last checked up;

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Original Submitter


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Submitted On

12:00am April 9, 2011

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First Version

Version 1

Created 4 years ago by Anonymous