Journal Article

Integrating shotgun proteomics and mRNA expression data to improve protein identification

Smriti R. Ramakrishnan, Christine Vogel, John T. Prince, Rong Wang, Zhihua Li, Luiz O. Penalva, Margaret Myers, Edward M. Marcotte and Daniel P. Miranker

in Bioinformatics

Volume 25, issue 11, pages 1397-1403
Published in print June 2009 | ISSN: 1367-4803
Published online March 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp168

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Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.

Results: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by ∼40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19–63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.

Availability and Implementation: Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from http://www.marcottelab.org/MSpresso/.

Contact: marcotte@icmb.utexas.edu; miranker@cs.utexas.edu

Supplementary Information: Supplementary data website: http://www.marcottelab.org/MSpresso/.

Journal Article.  5779 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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