4.7 Article

Automated Detection of Inaccurate and Imprecise Transitions in Peptide Quantification by Multiple Reaction Monitoring Mass Spectrometry

期刊

CLINICAL CHEMISTRY
卷 56, 期 2, 页码 291-305

出版社

OXFORD UNIV PRESS INC
DOI: 10.1373/clinchem.2009.138420

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资金

  1. National Cancer Institute (NCI) [U24 CA126476, R01 CA1 26219]
  2. National Heart Lung and Blood Institute [R01 HL096738-01]
  3. Women's Cancer Research Fund of the Entertainment Industry Foundation

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BACKGROUND: Multiple reaction monitoring mass spectrometry (MRM-MS) of peptides with stable isotope-labeled internal standards (SISs) is increasingly being used to develop quantitative assays for proteins in complex biological matrices. These assays can be highly precise and quantitative, but the frequent occurrence of interferences requires that MRM-MS data be manually reviewed, a time-intensive process subject to human error. We developed an algorithm that identifies inaccurate transition data based on the presence of interfering signal or inconsistent recovery among replicate samples. METHODS: The algorithm objectively evaluates MRM-MS data with 2 orthogonal approaches. First, it compares the relative product ion intensities of the analyte peptide to those of the SIS peptide and uses a t-test to determine if they are significantly different. A CV is then calculated from the ratio of the analyte peak area to the SIS peak area from the sample replicates. RESULTS: The algorithm identified problematic transitions and achieved accuracies of 94%-100%, with a sensitivity and specificity of 83%-100% for correct identification of errant transitions. The algorithm was robust when challenged with multiple types of interferences and problematic transitions. CONCLUSIONS: This algorithm for automated detection of inaccurate and imprecise transitions (AuDIT) in MRM-MS data reduces the time required for manual and subjective inspection of data, improves the overall accuracy of data analysis, and is easily implemented into the standard data-analysis work flow. AUDIT currently works with results exported from MRM-MS data-processing software packages and may be implemented as an analysis tool within such software. (C) 2009 American Association for Clinical Chemistry

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