Semi-supervised learning for peptide identification from shotgun proteomics datasets.
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.
Käll
Lukas
L
Department of Genome Sciences, University of Washington, 1705 NE Pacific St., William H. Foege Building, Seattle, Washington 98195, USA.
Canterbury
Jesse D
JD
Weston
Jason
J
Noble
William Stafford
WS
MacCoss
Michael J
MJ
D000465:Algorithms; D001185:Artificial Intelligence; D002918:Chymotrypsin / Q000032:analysis / Q000737:chemistry; D030562:Databases, Protein; D010196:Pancreatic Elastase / Q000032:analysis / Q000737:chemistry; D010446:Peptide Fragments / Q000032:analysis*; D020543:Proteome / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000737:chemistry; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods*; D014357:Trypsin / Q000032:analysis / Q000737:chemistry
D010446:Peptide Fragments; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins; D002918:Chymotrypsin; D010196:Pancreatic Elastase; D014357:Trypsin