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Bio- and chemoinformatic approaches for metabolomics data analysis.
In: Metabolic Profiling. Berlin [u.a.]: Springer, 2025. 67-89 (Methods Mol. Biol. ; 2891)
Metabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative. These tasks include, among others, examples to work with chemical formulae, handle and process mass spectrometry data, or calculate similarities between fragment spectra.
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Publication type
Article: Edited volume or book chapter
Keywords
Formula Handling ; Mass Spectra Handling ; R ; Rformassspectrometry ; Spectra Similarity Calculation
Language
english
Publication Year
2025
HGF-reported in Year
2025
ISSN (print) / ISBN
1064-3745
e-ISSN
1940-6029
Book Volume Title
Metabolic Profiling
Journal
Methods in Molecular Biology
Quellenangaben
Volume: 2891,
Pages: 67-89
Publisher
Springer
Publishing Place
Berlin [u.a.]
Reviewing status
Peer reviewed
Institute(s)
CF Metabolomics & Proteomics (CF-MPC)
POF-Topic(s)
30505 - New Technologies for Biomedical Discoveries
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
A-630710-001
PubMed ID
39812977
Erfassungsdatum
2025-03-20