Journal
BIOINFORMATICS
Volume 25, Issue 11, Pages 1397-1403Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btp168
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Funding
- National Science Foundation [DBI-0640923, IIS-0325116]
- Welch [F-1515]
- Packard Foundation
- National Institutes of Health [GM06779-01, GM076536-01]
- International Human Frontier Science Program
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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 similar to 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.
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