Dataset Information

Integrating shotgun proteomics and mRNA expression data to improve protein identification.

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 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 approximately 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/).

SUBMITTER: Ramakrishnan SR

PROVIDER: S-EPMC2682515 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Publications

Integrating shotgun proteomics and mRNA expression data to improve protein identification.

Ramakrishnan Smriti R SR Vogel Christine C Prince John T JT Li Zhihua Z Penalva Luiz O LO Myers Margaret M Marcotte Edward M EM Miranker Daniel P DP Wang Rong R

Bioinformatics (Oxford, England) 20090324 11

<h4>Motivation</h4>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.<h4>Results</h4>We develop a Bayesian score that estimates ...[more]

PMID: 19318424

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Dataset Information

Integrating shotgun proteomics and mRNA expression data to improve protein identification.

Motivation

Results

Availability and implementation

Publications

Integrating shotgun proteomics and mRNA expression data to improve protein identification.

Similar Datasets

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