mProphet: automated data processing and statistical validation for large-scale SRM experiments

• Published in: Nature methods Vol. 8; no. 5; pp. 430 - 435
• Main Authors: Reiter, Lukas, Rinner, Oliver, Picotti, Paola, Hüttenhain, Ruth, Beck, Martin, Brusniak, Mi-Youn, Hengartner, Michael O, Aebersold, Ruedi
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.05.2011; Nature Publishing Group
• Subjects: 631/1647/527/296; 631/92/475; Algorithms; Automatic Data Processing - methods; Automation; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Data processing; Electronic data processing; Humans; Inspection; Life Sciences; Mass spectrometry; Mass Spectrometry - statistics & numerical data; Methods; Models, Statistical; Peptides; Peptides - chemistry; Proteins; Proteomics; Proteomics - statistics & numerical data; Software; Spectrum analysis; Statistical models; Switzerland
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.1584

mProphet, a computational tool for statistically validating selected reaction monitoring (SRM) mass spectrometry data, is described. Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.