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Course Details

Informatics for Metabolomics

Cranfield University
Cranfield Health
Contact Provider
Five days
Short Course
Everyone who makes use of metabolomics data derived from mass spectrometry analyses.
Cranfield University, Cranfield, MK43 0AL, Bedfordshire, United Kingdom


This short course introduces delegates to the essential informatics concepts behind the interpretation of metabolomic data acquired using mass spectrometry. Concepts include metabolite identification, metabolite quantitation, and pattern recognition for metabolomic profiling. Key software is introduced and concepts demonstrated through hands-on computer-based practicals.

It will provide delegates with a solid understanding of the informatics concepts underpinning modern metabolomics, and an awareness of the currently available software.


  • Sources of metabolomic data: Overview of HPLC, GC, MS, MS/MS, and NMR, focussing on the nature of the data produced by these techniques.
  • Data pre-processing: Chromatrographic alignment (warping), deconvolution, peak detection.
  • Molecular identification algorithms: Identification of compounds by library searching and in silico fragmentation. Introduction to popular publicly available tools including XCMS2, AMDIS and MetFrag.
  • Quantitation: Algorithms for metabolite quantitation. Introduction to popular publicly available tools such as MetaQuant and MZmine.
  • Computer-based practicals: Identification and quantitation of metabolites from real metabolomic spectral data using software tools listed above.
  • Data standards: Update on the status of the metabolomics standards initiative (MSI) and overview of the core information for metabolomics reporting (CIMR) standard.
  • Data repositories: Finding and retrieving biologically relevant data from metabolomics databases such as the Human Metabolome Database (HMDB), and related databases covering small molecules (e.g. PubChem, ChEBI) and spectra (e.g. Metlin).
  • Statistical methods for discovering biological knowledge: Introduction to downstream statistical analysis of metabolomics data, using the R statistical computing environment. Including hands-on practicals covering exploratory data analysis (principal components analysis, hierarchical cluster analysis), pattern recognition and biomarker discovery.

Course Format

Five days including lectures and practical sessions.

The course is also Module 4 of Applied Bioinformatics MSc.


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