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


METHODS
Software and datasets are available at http://aug.csres.utexas.edu/msnet
Ramakrishnan
Smriti R
SR
Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.
Vogel
Christine
C
Kwon
Taejoon
T
Penalva
Luiz O
LO
Marcotte
Edward M
EM
Miranker
Daniel P
DP
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D053263:Gene Regulatory Networks; D011506:Proteins / Q000737:chemistry*; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D053719:Tandem Mass Spectrometry / Q000379:methods*
D011506:Proteins; D020543:Proteome