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Bioinformatics. 2009 Nov 15;25(22):2955-61. doi: 10.1093/bioinformatics/btp461. Epub 2009 Jul 24.

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

Author information

1
Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.

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

PMID:
19633097
PMCID:
PMC2773251
DOI:
10.1093/bioinformatics/btp461
[Indexed for MEDLINE]
Free PMC Article

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