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

• Published in: Bioinformatics Vol. 25; no. 22; pp. 2955 - 2961
• Main Authors: Ramakrishnan, Smriti R., Vogel, Christine, Kwon, Taejoon, Penalva, Luiz O., Marcotte, Edward M., Miranker, Daniel P.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.11.2009
• Subjects: Biological and medical sciences; Computational Biology - methods; Databases, Protein; Fundamental and applied biological sciences. Psychology; Gene Regulatory Networks; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Original Papers; Proteins - chemistry; Proteome - analysis; Proteomics - methods; Tandem Mass Spectrometry - methods; Identification; Gene; Mass spectrometry; Protein; Network
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp461

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.