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

BMC Bioinformatics

Methodology Article

Konstantinos Petritis¹  Jianqiu Zhang²  William E Haskins³ 

[1] Center for Proteomics, Translational Genomics Research Institute, 85004, Phoenix, AZ, USA;Dept. Electrical and Computer Engineering, University of Texas at San Antonio, 78249, TX, USA;Pediatric Biochemistry Laboratory, University of Texas at San Antonio, 78249, TX, USA;Depts. Biology & Chemistry, University of Texas at San Antonio, 78249, TX, USA;RCMI Proteomics & Protein Biomarkers Cores, University of Texas at San Antonio, 78249, San Antonio, TX, USA;Dept. of Medicine, Division of Hematology & Medical Oncology, Cancer Therapy & Research Center, University of Texas Health Science Center at San Antonio, 78229, San Antonio, TX, USA;

关键词: Maximum Ratio Combine;    High SNRs;    Quantification Accuracy;    Maximum Ratio Combine;    Reference Template;   

DOI  :  10.1186/1471-2105-12-74

 received in 2010-07-29, accepted in 2011-03-15,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundRelative 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.ResultsMRCQuant showed significant improvement in the number of accurately quantified peptides.ConclusionsMRCQuant 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.

【 授权许可】

Unknown   
© Haskins et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.