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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

Keywords

worm, yeast

Synonyms

Percolator

Additional Resource Types

Software Resource, Database

Supercategory

Resource

Parent Organization

Original Submitter

Anonymous

Version Status

Curated

Submitted On

12:00am April 9, 2011

Originated From

SciCrunch

Changes from Previous Version

  • Description was changed
  • Additional Resource Types was changed

Version 2

Created 3 weeks ago by Christie Wang

Version 1

Created 4 years ago by Anonymous

Semi-supervised learning for peptide identification from shotgun proteomics datasets.

  • Käll L
  • Nat. Methods
  • 2007 31

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.