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Alignment of LC‐MS images, with applications to biomarker discovery and protein identification
Author(s) -
Vandenbogaert Mathias,
LiThiaoTé Sébastien,
Kaltenbach HansMichael,
Zhang Runxuan,
Aittokallio Tero,
Schwikowski Benno
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.200700791
Subject(s) - biomarker discovery , identification (biology) , mass spectrometry , computer science , biomarker , proteomics , resolution (logic) , automation , sampling (signal processing) , chromatography , computational biology , artificial intelligence , chemistry , computer vision , biology , engineering , mechanical engineering , biochemistry , botany , filter (signal processing) , gene
Abstract LC‐MS‐based approaches have gained considerable interest for the analysis of complex peptide or protein mixtures, due to their potential for full automation and high sampling rates. Advances in resolution and accuracy of modern mass spectrometers allow new analytical LC‐MS‐based applications, such as biomarker discovery and cross‐sample protein identification. Many of these applications compare multiple LC‐MS experiments, each of which can be represented as a 2‐D image. In this article, we survey current approaches to LC‐MS image alignment. LC‐MS image alignment corrects for experimental variations in the chromatography and represents a computational key technology for the comparison of LC‐MS experiments. It is a required processing step for its two major applications: biomarker discovery and protein identification. Along with descriptions of the computational analysis approaches, we discuss their relative merits and potential pitfalls.