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dc.contributor.authorMargolin, Adam A.
dc.contributor.authorOng, Shao-En
dc.contributor.authorSchenone, Monica
dc.contributor.authorGould, Robert
dc.contributor.authorSchreiber, Stuart L.
dc.contributor.authorCarr, Steven A.
dc.contributor.authorGolub, Todd R.
dc.date.accessioned2010-10-01T18:43:41Z
dc.date.issued2009
dc.identifier.citationMargolin, Adam A., Shao-En Ong, Monica Schenone, Robert Gould, Stuart L. Schreiber, Steven A. Carr, and Todd R. Golub. 2009. Empirical Bayes Analysis of Quantitative Proteomics Experiments. PLoS ONE 4(10): e7454.en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4455263
dc.description.abstractBackground: Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein. Methodology/Principal Findings: We analyzed 2 types of mass spectrometric experiments. First, we showed that the method identified the protein targets of small-molecules in affinity purification experiments with high precision. Second, we re-analyzed a mass spectrometric data set designed to identify proteins regulated by microRNAs. Our results were supported by sequence analysis of the 3′ UTR regions of predicted target genes, and we found that the previously reported conclusion that a large fraction of the proteome is regulated by microRNAs was not supported by our statistical analysis of the data. Conclusions/Significance: Our results highlight the importance of rigorous statistical analysis of proteomic data, and the method described here provides a statistical framework to robustly and reliably interpret such data.en_US
dc.description.sponsorshipChemistry and Chemical Biologyen_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdx.doi.org/10.1371/journal.pone.0007454en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759080/pdf/en_US
dash.licenseOAP
dc.subjectcomputational biologyen_US
dc.subjectbiochemistryen_US
dc.subjectdrug discoveryen_US
dc.subjectchemical biologyen_US
dc.subjectprotein chemistry and proteomicsen_US
dc.subjectgenetics and genomicsen_US
dc.subjectbioinformaticsen_US
dc.subjectmathematicsen_US
dc.subjectalgorithmsen_US
dc.subjectstatisticsen_US
dc.subjectmolecular biologyen_US
dc.subjecttranslational regulationen_US
dc.subjectpharmacologyen_US
dc.subjectdrug developmenten_US
dc.titleEmpirical Bayes Analysis of Quantitative Proteomics Experimentsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS ONEen_US
dash.depositing.authorSchreiber, Stuart L.
dc.date.available2010-10-01T18:43:41Z
dc.identifier.doi10.1371/journal.pone.0007454*
dash.contributor.affiliatedGolub, Todd
dash.contributor.affiliatedSchreiber, Stuart


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