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


METHODS
Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from (http://www.marcottelab.org/MSpresso/).
Ramakrishnan
Smriti R
SR
Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
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