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.
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.
Authors: Jacob D Jaffe; Hasmik Keshishian; Betty Chang; Theresa A Addona; Michael A Gillette; Steven A Carr Journal: Mol Cell Proteomics Date: 2008-06-04 Impact factor: 5.911
Authors: Terri A Addona; Susan E Abbatiello; Birgit Schilling; Steven J Skates; D R Mani; David M Bunk; Clifford H Spiegelman; Lisa J Zimmerman; Amy-Joan L Ham; Hasmik Keshishian; Steven C Hall; Simon Allen; Ronald K Blackman; Christoph H Borchers; Charles Buck; Helene L Cardasis; Michael P Cusack; Nathan G Dodder; Bradford W Gibson; Jason M Held; Tara Hiltke; Angela Jackson; Eric B Johansen; Christopher R Kinsinger; Jing Li; Mehdi Mesri; Thomas A Neubert; Richard K Niles; Trenton C Pulsipher; David Ransohoff; Henry Rodriguez; Paul A Rudnick; Derek Smith; David L Tabb; Tony J Tegeler; Asokan M Variyath; Lorenzo J Vega-Montoto; Asa Wahlander; Sofia Waldemarson; Mu Wang; Jeffrey R Whiteaker; Lei Zhao; N Leigh Anderson; Susan J Fisher; Daniel C Liebler; Amanda G Paulovich; Fred E Regnier; Paul Tempst; Steven A Carr Journal: Nat Biotechnol Date: 2009-06-28 Impact factor: 54.908
Authors: Hasmik Keshishian; Terri Addona; Michael Burgess; D R Mani; Xu Shi; Eric Kuhn; Marc S Sabatine; Robert E Gerszten; Steven A Carr Journal: Mol Cell Proteomics Date: 2009-07-13 Impact factor: 5.911
Authors: Christopher R Kinsinger; James Apffel; Mark Baker; Xiaopeng Bian; Christoph H Borchers; Ralph Bradshaw; Mi-Youn Brusniak; Daniel W Chan; Eric W Deutsch; Bruno Domon; Jeff Gorman; Rudolf Grimm; William Hancock; Henning Hermjakob; David Horn; Christie Hunter; Patrik Kolar; Hans-Joachim Kraus; Hanno Langen; Rune Linding; Robert L Moritz; Gilbert S Omenn; Ron Orlando; Akhilesh Pandey; Peipei Ping; Amir Rahbar; Robert Rivers; Sean L Seymour; Richard J Simpson; Douglas Slotta; Richard D Smith; Stephen E Stein; David L Tabb; Danilo Tagle; John R Yates; Henry Rodriguez Journal: Mol Cell Proteomics Date: 2011-11-03 Impact factor: 5.911
Authors: Johannes A Hewel; Jian Liu; Kento Onishi; Vincent Fong; Shamanta Chandran; Jonathan B Olsen; Oxana Pogoutse; Mike Schutkowski; Holger Wenschuh; Dirk F H Winkler; Larry Eckler; Peter W Zandstra; Andrew Emili Journal: Mol Cell Proteomics Date: 2010-05-13 Impact factor: 5.911