title: Data processing methods and quality control strategies for label-free LC-MS protein quantification.
abstract: Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
authors.affiliation: Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden.
authors.forename: Marianne
authors.initials: M
authors.lastname: Sandin
authors.forename: Johan
authors.initials: J
authors.lastname: Teleman
authors.forename: Johan
authors.initials: J
authors.lastname: Malmström
authors.forename: Fredrik
authors.initials: F
authors.lastname: Levander
mesh_terms: D002853:Chromatography, Liquid / Q000379:methods*; D011506:Proteins / Q000032:analysis*; D011786:Quality Control; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D011506:Proteins
keywords: LC–MS; Label free; Protein quantification; Quality control; Quantitative proteomics; Selected reaction monitoring