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- Title
Integrating shotgun proteomics and mRNA expression data to improve protein identification.
- Authors
Smriti R. Ramakrishnan; Christine Vogel; John T. Prince; Zhihua Li; Luiz O. Penalva; Margaret Myers; Edward M. Marcotte; Daniel P. Miranker; Rong Wang
- Abstract
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 proteins presence is likely to correlate with its mRNA concentration. Results: We develop a Bayesian score that estimates the posterior probability of a proteins 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/.
- Subjects
PROTEIN analysis; PROTEOMICS; MESSENGER RNA; GENE expression; TANDEM mass spectrometry; BAYESIAN analysis; COMPUTER software; BIOINFORMATICS
- Publication
Bioinformatics, 2009, Vol 25, Issue 11, p1397
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btp168