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Multiple-reaction monitoring mass spectrometry (MRM-MS, aka SRM-MS) is being increasingly used to quantify peptides, with high sensitivity and selectivity in biological and clinical matrices. While instrument methods are now simple to set up, the full benefit of MRM-MS experiments can only be realized if the assays are correctly configured and characterized. Successful configuration involves testing thousands of peptide precursor-product pairs (transitions) for appropriate sensitivity and reproducibility, in addition to evaluating calibration curves to determine limits of detection and quantification, as well as checking for potential interferences. QuaSAR is a suite of software tools to automate and assist in this laborious process, both during assay configuration and in subsequent analysis of quantitative measurements in samples of interest.

QuaSAR implements a comprehensive and easy to use pipeline for the analysis of MRM-MS data that draws upon both novel and many previously published methods [1]. Essential statistics like coefficient of variation, regression slope and intercept (with confidence intervals) and limits of detection and quantification are tabulated for every peptide along with plots summarizing their distribution and variation. The AuDIT [2] interference detection algorithm has been integrated into the pipeline to not only identify problematic transitions, but to also visually mark these transitions in data plots. Execution of the QuaSAR pipeline enables users to quickly and effectively assess data quality and characterize assay performance.

[1] Mani, D. R., Abbatiello, S. E., & Carr, S. A. (2012). Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics. BMC Bioinformatics, 13(Suppl 16), S9. doi:10.1186/1471-2105-13-S16-S9
[2] Abbatiello, S. E., Mani, D. R., Keshishian, H., & Carr, S. A. (2010). Automated Detection of Inaccurate and Imprecise Transitions in Peptide Quantification by Multiple Reaction Monitoring Mass Spectrometry. Clinical Chemistry, 56(2), 291-305. doi:10.1373/clinchem.2009.138420

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Tool Information
Organization: Carr Lab, Broad Institute
Authors: D. R. Mani, Susan Abbatiello, Rushdy Ahmad and Deepak Mani
Languages: R(3.0.1), C#