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Kwon, Taejoon
TaejoonLab
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  • Systems biology, computational biology, functional genomics

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Mining gene functional networks to improve mass-spectrometry-based protein identification

Cited 21 times inthomson ciCited 22 times inthomson ci
Title
Mining gene functional networks to improve mass-spectrometry-based protein identification
Other Titles
Mining gene functional networks to improve mass-spectrometry-based protein identification
Author
Ramakrishnan, Smriti R.Vogel, ChristineKwon, TaejoonPenalva, Luiz O.Marcotte, Edward M.Miranker, Daniel P.
Keywords
FALSE DISCOVERY RATES; SACCHAROMYCES-CEREVISIAE; SHOTGUN PROTEOMICS; YEAST PROTEOME; PEPTIDE IDENTIFICATION; STATISTICAL-MODEL; TANDEM; EXPRESSION; DATABASE; PREDICTION
Issue Date
2009-11
Publisher
OXFORD UNIV PRESS
Citation
BIOINFORMATICS, v.25, no.22, pp.2955 - 2961
Abstract
Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly. Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8-29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets
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DOI
10.1093/bioinformatics/btp461
ISSN
1367-4803
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BME_Journal Papers
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