학술논문
Semi-supervised learning for peptide identification from shotgun proteomics datasets.
Document Type
Article
Author
Source
Subject
*PROTEINS
*PROTEOMICS
*MOLECULAR biology
*PEPTIDE antibiotics
*BIOMOLECULES
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Language
ISSN
1548-7091
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. [ABSTRACT FROM AUTHOR]