MRCQuant- an accurate LC/MS relative isotopic quantification algorithm on TOF instruments

Background: Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-offlight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository. Results: MRCQuant showed sign...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: William H. Haskins, Konstantinos Petritis, Jianqiu Zhang
• Keywords: Artificial Intelligence, BMCBI 2011, Liquid Chromatography-mass Spectrometry, Peptides, Quantification