Journal Article

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

Smriti R. Ramakrishnan, Christine Vogel, Taejoon Kwon, Luiz O. Penalva, Edward M. Marcotte and Daniel P. Miranker

in Bioinformatics

Volume 25, issue 22, pages 2955-2961
Published in print November 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp461

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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

Contact: miranker@cs.utexas.edu, marcotte@icmb.utexas.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5960 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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