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.
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Exploring science is typically characterized by a lot of puzzles, frustrations or even failures. This weblog is mainly intended to record my working, thinking and knowledge acquisitions. I expect that some reflection would refresh my mind from time to time, and motivate me to move further, and hopefully give me a better view about even changing the landscape of bioinformatics. You are welcome to leave some comments, good or bad, but hopefully something constructive. Enjoy your surfing!
Monday, March 28, 2011
mProphet: automated data processing and statistical validation for large-scale SRMSRMSRM experiments
Labels:
algorithm,
machine learning,
mass spectrometry,
MRM
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