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1.  Global signatures of protein and mRNA expression levels† 
Molecular bioSystems  2009;5(12):1512-1526.
Cellular states are determined by differential expression of the cell’s proteins. The relationship between protein and mRNA expression levels informs about the combined outcomes of translation and protein degradation which are, in addition to transcription and mRNA stability, essential contributors to gene expression regulation. This review summarizes the state of knowledge about large-scale measurements of absolute protein and mRNA expression levels, and the degree of correlation between the two parameters. We summarize the information that can be derived from comparison of protein and mRNA expression levels and discuss how corresponding sequence characteristics suggest modes of regulation.
doi:10.1039/b908315d
PMCID: PMC4089977  PMID: 20023718
2.  Mining gene functional networks to improve mass-spectrometry-based protein identification 
Bioinformatics  2009;25(22):2955-2961.
Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly.
Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8–29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets.
Availability and Implementation: Software and datasets are available at http://aug.csres.utexas.edu/msnet
Contact: miranker@cs.utexas.edu, marcotte@icmb.utexas.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp461
PMCID: PMC2773251  PMID: 19633097
3.  Integrating shotgun proteomics and mRNA expression data to improve protein identification 
Bioinformatics  2009;25(11):1397-1403.
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/.
doi:10.1093/bioinformatics/btp168
PMCID: PMC2682515  PMID: 19318424

Results 1-3 (3)