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.

Availability and implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnet

• PMID: 19633097
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Journal Article | Research Support, N.I.H., Extramural | Research Support, Non-U.S. Gov't | Research Support, U.S. Gov't, Non-P.H.S.

Authors

Ramakrishnan SR, Vogel C, Kwon T, Penalva LO, Marcotte EM, Miranker DP

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