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

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

Research output: Contribution to journalArticle

28 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)2955-2961
Number of pages7
JournalBioinformatics
Volume25
Issue number22
DOIs
StatePublished - Nov 15 2009

Fingerprint

Gene Regulatory Networks
Mass Spectrometry
Mass spectrometry
Mining
Genes
Gene
Proteins
Protein
Proteomics
Experiment
Firearms
Yeast
Experiments
Confidence
Yeasts
Equally likely
Biological Phenomena
Tandem Mass Spectrometry
High Throughput
Error Rate

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

Cite this

Mining gene functional networks to improve mass-spectrometry-based protein identification. / Ramakrishnan, Smriti R.; Vogel, Christine; Kwon, Taejoon; Penalva, Luiz O; Marcotte, Edward M.; Miranker, Daniel P.

In: Bioinformatics, Vol. 25, No. 22, 15.11.2009, p. 2955-2961.

Research output: Contribution to journalArticle

Ramakrishnan, SR, Vogel, C, Kwon, T, Penalva, LO, Marcotte, EM & Miranker, DP 2009, 'Mining gene functional networks to improve mass-spectrometry-based protein identification', Bioinformatics, vol. 25, no. 22, pp. 2955-2961. https://doi.org/10.1093/bioinformatics/btp461
Ramakrishnan, Smriti R. ; Vogel, Christine ; Kwon, Taejoon ; Penalva, Luiz O ; Marcotte, Edward M. ; Miranker, Daniel P. / Mining gene functional networks to improve mass-spectrometry-based protein identification. In: Bioinformatics. 2009 ; Vol. 25, No. 22. pp. 2955-2961.
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