Abstract
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.
Publication types
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Research Support, N.I.H., Extramural
MeSH terms
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Algorithms
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Artificial Intelligence*
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Chymotrypsin / analysis
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Chymotrypsin / chemistry
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Databases, Protein
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Pancreatic Elastase / analysis
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Pancreatic Elastase / chemistry
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Peptide Fragments / analysis*
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Proteome / analysis
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Proteome / chemistry
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Proteomics / methods*
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Saccharomyces cerevisiae Proteins / analysis
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Saccharomyces cerevisiae Proteins / chemistry
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Software
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Tandem Mass Spectrometry / methods*
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Trypsin / analysis
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Trypsin / chemistry
Substances
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Peptide Fragments
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Proteome
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Saccharomyces cerevisiae Proteins
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Chymotrypsin
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Pancreatic Elastase
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Trypsin