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Semi-supervised learning for peptide identification from shotgun proteomics datasets
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Semi-supervised learning for peptide identification from shotgun proteomics datasets
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Journal Article
Semi-supervised learning for peptide identification from shotgun proteomics datasets
2007
Overview
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.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ Biomedical and Life Sciences
/ Biomedical Engineering/Biotechnology
/ Methods
/ Pancreatic Elastase - analysis
/ Pancreatic Elastase - chemistry
/ Peptide Fragments - analysis
/ Peptides
/ Saccharomyces cerevisiae Proteins - analysis
/ Saccharomyces cerevisiae Proteins - chemistry
/ Software
