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Workflows for automated downstream data analysis and visualization in large‐scale computational mass spectrometry
Author(s) -
Aiche Stephan,
Sachsenberg Timo,
Kenar Erhan,
Walzer Mathias,
Wiswedel Bernd,
Kristl Theresa,
Boyles Matthew,
Duschl Albert,
Huber Christian G.,
Berthold Michael R.,
Reinert Knut,
Kohlbacher Oliver
Publication year - 2015
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.201400391
Subject(s) - workflow , computer science , visualization , flexibility (engineering) , data mining , isobaric labeling , proteomics , data science , software engineering , database , quantitative proteomics , chemistry , biochemistry , statistics , mathematics , gene
MS‐based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom‐tailored and flexible data‐processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open‐source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high‐quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label‐free quantitation and identification of metabolites, and quality control for proteomics experiments.