Premium
Comparative LC‐MS: A landscape of peaks and valleys
Author(s) -
America Antoine H. P.,
Cordewener Jan H. G.
Publication year - 2008
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200700694
Subject(s) - computer science , quantitative proteomics , matching (statistics) , software , set (abstract data type) , data mining , resolution (logic) , label free quantification , proteomics , chromatography , chemistry , artificial intelligence , mathematics , statistics , biochemistry , gene , programming language
Quantitative proteomics approaches using stable isotopes are well‐known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC‐MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC‐MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label‐free LC‐MS. In order to compare at comprehensive scale peak intensity data in multiple LC‐MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundancies provides an impressive level of detail. This approach also requires an experimental set‐up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.