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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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Bio- and Chemoinformatic Approaches for Metabolomics Data Analysis

  • 实验方案

  • 发表日期：

    2025年

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  • Michael Witting,
  • Johannes Rainer

摘要

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.