Title
Computational quality control tools for mass spectrometry proteomics Computational quality control tools for mass spectrometry proteomics
Author
Faculty/Department
Faculty of Sciences. Biology
Faculty of Sciences. Chemistry
Faculty of Sciences. Mathematics and Computer Science
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Weinheim ,
Subject
Biology
Human medicine
Computer. Automation
Source (journal)
Proteomics. - Weinheim
Volume/pages
(2016) , p. 1-36
ISSN
1615-9853
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
As mass-spectrometry-based proteomics has matured during the past decade a growing emphasis has been placed on quality control. For this purpose multiple computational quality control tools have been introduced. These tools generate a set of metrics that can be used to assess the quality of a mass spectrometry experiment. Here we review which different types of quality control metrics can be generated, and how they can be used to monitor both intra- and inter-experiment performance. We discuss the principal computational tools for quality control and list their main characteristics and applicability. As most of these tools have specific use cases it is not straightforward to compare their performance. For this survey we used different sets of quality control metrics derived from information at various stages in a mass spectrometry process and evaluated their effectiveness at capturing qualitative information about an experiment using a supervised learning approach. Furthermore, we discuss currently available algorithmic solutions that enable the usage of these quality control metrics for decision-making.
Handle