Repository: Freie Universit├Ąt Berlin, Math Department

Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry

Aiche, S. and Sachsenberg, T. and Kenar, E. and Walzer, M. and Wiswedel, B. and Kristl, T. and Boyles, M. and Duschl, A. and Huber, C. G. and Berthold, M. R. and Reinert, K. and Kohlbacher, O. (2015) Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry. PROTEOMICS, 15 (8). pp. 1443-1447. ISSN 16159853

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Official URL: http://dx.doi.org/10.1002/pmic.201400391

Abstract

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.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:1505
Deposited By: Anja Kasseckert
Deposited On:12 Feb 2015 12:07
Last Modified:15 Nov 2016 14:01

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