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Bioinformatics Advance Access originally published online on July 24, 2009
Bioinformatics 2009 25(22):2955-2961; doi:10.1093/bioinformatics/btp461
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Smriti R. Ramakrishnan 1, Christine Vogel 2, Taejoon Kwon 2, Luiz O. Penalva 3, Edward M. Marcotte 2,* and Daniel P. Miranker 1,*

1Department of Computer Sciences, 1 University Station C0500, 2Department of Chemistry and Biochemistry & Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, 2500 Speedway, The University of Texas at Austin, Austin, TX 78712 and 3Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA

*To whom correspondence should be addressed.


  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.

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

Contact: miranker{at}cs.utexas.edu, marcotte{at}icmb.utexas.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on March 10, 2009; revised on June 26, 2009; accepted on July 19, 2009

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