ORCID Profile
0000-0002-1462-4426
Current Organisation
Helmholtz Zentrum München Deutsches Forschungszentrum für Umwelt und Gesundheit
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Publisher: Frontiers Media SA
Date: 14-11-2018
Publisher: Informa UK Limited
Date: 03-04-2017
Publisher: Springer Science and Business Media LLC
Date: 08-08-2022
Publisher: Springer US
Date: 2020
DOI: 10.1007/978-1-0716-0239-3_18
Abstract: Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.
Publisher: Springer Science and Business Media LLC
Date: 13-12-2022
Publisher: MDPI AG
Date: 23-09-2019
Abstract: Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
Location: Germany
No related grants have been discovered for Michael Witting.