Overordnede kursusmål
The teaching will be carried out as lectures, exercises, workshops,
and participant presentations. During the workshops, participants
will form small groups and carry out various data analyses with
their own data set or provided study data sets. Participants will
meet daily in small groups(2-4 people) and accomplish tasks which
culminate into a final hand-in presentation. Furthermore, a number
of visiting expert researchers are intensively involved in the
course to communicate the most recent advances in the field of
metabolomics.
Læringsmål
En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:
- Describe the basic principles of high-resolution tandem mass
spectrometry
- Understand typical mass spectrometry data formats
- Carry out data pre-processing using MZmine
- Understand and apply metabolite identification confidence
levels
- Use and understand tools for advanced metabolite identification
(e.g. GNPS, FERMO and Sirius+CSI:FingerID)
- Understand and use basic multi- and univariate statistical
methods, e.g. PCA, and differential analysis
- Perform a full workflow for basic analysis of a non-targeted
metabolomics experiment, including data preprocessing, metabolite
annotation, statistical analysis, and biological
interpretation
- Understand quality control and quality assurance in
metabolomics and exposomics
Kursusindhold
Day 1: Half a day on basic principles of metabolomics and
high-resolution mass spectrometry are introduced. We will focus on
data pre-processing the remainder of the time. After a brief
overview of available pre-processing methods, participants will be
introduced to MZmine and will work on their own or study data. Day
1 is concluded with a social event.
Day 2: Focus on (pre)-processing will be continued working more
with MZmine. Data library annotation, data evaluation and data
integration via FERMO.
Day 3: Focus on metabolite identification and advanced metabolome
mining tools, including molecular networking through GNPS and in
silico molecular structure annotation through Sirius+CSI:FingerID.
Participants will test different metabolite annotation strategies
on their own or study data at a nearly full-day workshop. The day
will be concluded with a social event to allow for further
networking.
Day 4: Basic statistical methods for metabolomics data analysis,
e.g. differential abundance analysis and Principal Component’s
Analysis (PCA). FBmn stats guide will be utilized to facilitate
online-statistical software. The participants will get hands-on
experience from working on their own or study data in a nearly
full-day workshop.
Day 5: Focus on data visualization. We will introduce Cytoscape,
for comprehensive network visualization for metabolomics data,
including integration of metabolite annotation and statistics
results. The participants will conclude the full analysis pipeline
from metabolomics preprocessing to metabolite annotation and
visualization of results.
Litteraturhenvisninger
Schmid R., Heuckeroth S. et al., Integrative analysis of multimodal
mass spectrometry data in MZmine 3, Nature Biotechnology (2023).
Nothias, L. F. et al. Feature-based molecular networking in the
GNPS analysis environment. Nat. Methods 17, 905–908 (2020).
Dührkop, K., Fleischauer, M., Ludwig, M. et al. SIRIUS 4: a rapid
tool for turning tandem mass spectra into metabolite structure
information. Nat Methods 16, 299–302 (2019).
Huber F., et al. matchms - processing and similarity evaluation of
mass spectrometry data, Journal of Open Source Software 52, 2411
(2020).
De Jonge N., et al. MS2Query: reliable and scalable MS2 mass
spectra-based analogue search, Nature Communications 14, 1752
(2023).
Ernst, M.; Kang, K.B.; Caraballo-Rodríguez, A.M.; Nothias, L.-F.;
Wandy, J.; Chen, C.; Wang, M.; Rogers, S.; Medema, M.H.;
Dorrestein, P.C.; van der Hooft, J.J.J. MolNetEnhancer: Enhanced
Molecular Networks by Integrating Metabolome Mining and Annotation
Tools. Metabolites 2019, 9, 144.
Wang, M et al. Sharing and community curation of mass spectrometry
data with Global Natural Products Social Molecular Networking.
Nature Biotechnology 34, 828-837.
Bemærkninger
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Sidst opdateret
01. juli, 2025
