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Printable Handouts
Navigable Slide Index
- Introduction
- Two routes to metabolomics
- Metabolomics workflow
- Resources for going from spectra to lists
- Levels of metabolite identification
- Metabolite ID by spectral deconvolution (NMR)
- Chemical shift matching via NMR spectral DBs
- NMR spectral matching (level 1)
- NMR spectral matching (Bayesil – level 1)
- Metabolite ID by GC-MS
- GC-MS compound identification (resources)
- NIST library & MS peak matching - AMDIS
- Other GC-MS resources
- GC-AutoFit (automated GC-MS)
- Metabolite ID by LC-MS/MS
- Processing untargeted LC-MS data (resources)
- Compound identification via LC-MS
- Resources for mass or formula matching
- Advice for mass or formula matching
- Resources for MS/MS spectral matching
- Resources for MS/MS matching
- What if your compound doesn’t have a match?
- Predicted spectral matching
- Compound ID by CFM-ID
- Going from lists to significant metabolites
- Finding significant metabolites
- Statistics with SIMCA
- XCMS online
- Galaxy for metabolomics
- MetaboAnalyst
- MetaboAnalyst modules
- Going from metabolites to biological meaning
- Extracting biological meaning
- Compound databases (species/source info)
- Comprehensive MetDBs
- UofA database resources
- New human metabolome database (HMDB)
- The food database (FooDB)
- The drug database (DrugBank v. 5.0)
- The toxic exposome database (T3DB)
- The yeast metabolome database (YMDB)
- The E. coli metabolome database (ECMDB)
- Pathway resources
- Pathway databases
- KEGG: Kyoto encyclopedia of genes & genomes
- Limitations with today’s pathway databases
- The small molecule pathway database (SMPDB)
- Exploring pathways with SMPDB
- Biological interpretation
- Resources for depositing metabolomics data
- MetaboLights
- Metabolomics workbench
- Conclusions
- Acknowledgements
Topics Covered
- Metabolomics workflow and processes
- Overview of metabolomics resources freely available on the web
- Various resources for analyzing NMR, GC-MS and LC-MS metabolomics data
- Tools for finding significant metabolites via multivariate statistics
- Widely used online metabolomics databases
- Online pathway resources used to facilitate biological interpretation
- Deposition of metabolomics data
Talk Citation
Wishart, D. (2023, July 18). Metabolomics resources [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 9, 2024, from https://doi.org/10.69645/CBQH6781.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. David Wishart has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Other Talks in the Series: Bioinformatics for Metabolomics
Transcript
Please wait while the transcript is being prepared...
0:00
My name is David Wishart,
I'm with the Departments of Biological Sciences and
Computing Science at the University of Alberta in Canada.
Today, my talk is on "Metabolomics Resources".
0:11
So, there are two routes to metabolomics.
One is quantitative or targeted approach,
the second is called untargeted metabolomics or profiling.
What we're showing here is an NMR spectrum of urine,
and with quantitative methods,
typically one is identifying and quantifying all of
the peaks before following on with multivariate statistics.
In untargeted profiling, typically what you do is multivariate statistics first,
which essentially simplifies the spectra and allows you to
identify important features which is shown with these PCA plots,
and then after those important features are identified,
then you can proceed to doing quantification and identification.
0:51
So, in essence with metabolomics,
there's a general workflow that almost every researcher follows.
The first part is to collect spectra,
make the NMR, or GC-MS,
or LC-MS/MS spectra, and from there you might
generate long lists of metabolites or peaks.
In this case, we're trying to focus on
targeted metabolomic so typically you'll get a long list of metabolites.
Second, from that long list of metabolites,
you try and use multivariate statistics to
identify smaller numbers of significant metabolites.
Those things have changed,
they've gone up or down depending on the experimental conditions you are looking at.
From that smaller list of significant metabolites,
then you try and move towards pathways,
biomarkers, and biological interpretation.
This is of course the essence of metabolomics and
what mostly we'll try and focus on today.
Once you've done all of that,
you try and deposit your metabolomic data and some of
your interpretation in various public databases.
Today, I'll talk about all four steps in metabolomics workflow.