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Open Access Highly Accessed Methodology article

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

William E Haskins1234, Konstantinos Petritis5 and Jianqiu Zhang6*

Author Affiliations

1 Pediatric Biochemistry Laboratory, University of Texas at San Antonio, TX, 78249, USA

2 Depts. Biology & Chemistry, University of Texas at San Antonio, TX, 78249, USA

3 RCMI Proteomics & Protein Biomarkers Cores, University of Texas at San Antonio, San Antonio, TX 78249, USA

4 Dept. of Medicine, Division of Hematology & Medical Oncology, Cancer Therapy & Research Center,University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA

5 Center for Proteomics, Translational Genomics Research Institute, Phoenix, AZ 85004, USA

6 Dept. Electrical and Computer Engineering, University of Texas at San Antonio, TX 78249, USA

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BMC Bioinformatics 2011, 12:74  doi:10.1186/1471-2105-12-74

Published: 15 March 2011

Abstract

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-of-flight (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 significant improvement in the number of accurately quantified peptides.

Conclusions

MRCQuant effectively addresses major issues in the relative quantification of LC-MS-based proteomics data, and it provides improved performance in the quantification of low abundance peptides.