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Printable Handouts
Navigable Slide Index
- Introduction
- Pre-processing data analysis
- Metabolomics bottleneck
- The cocktail party problem
- The “cocktail party problem” in metabolomics
- Proton nuclear magnetic resonance (1H NMR)
- GC/MS & LC/MS
- From raw data to metabolites IDs
- Challenges in GC/MS
- Challenges in LC/MS
- Metabolic annotation examples
- LC/MS workflow
- Reproducibility of the extraction method
- LC/MS workflow: analytical variability
- LC/MS workflow: calculating analytical variability
- LC/MS workflow: features intensity
- Feature intensity distribution
- LC/MS workflow: hypothesis testing
- Search databases for accurate mass
- Search databases (common adducts)
- MS/MS of different adducts
- Metabolite identification
- Metabolomics bottleneck revisited
- Characterization of the sequence of monomers
- Requirements for a metabolite identification
- Identification of metabolites
- In silico tools for the identification of metabolites
- Organic chemistry
- Identification of a new endogenous metabolite
- Feature up-regulated at cold temperature
- Chemical synthesis of hypothesized structure
- Synthesized metabolite (comparable MS/MS data)
- Hypothesis & experimental validation
- The “Static” picture
- The “Static” picture: example
- The “Static” picture: a snapshot of metabolism
- The “Dynamic” picture: isotopic labels
- The “Dynamic” picture: example
- UDP-GlcNAc: example
- The "Integrated" picture
- Pathways versus networks
- Summary
- Thank you
Topics Covered
- Pre-processing data analysis
- Metabolomics bottleneck & the “cocktail party problem”
- From raw data to metabolites IDs
- Challenges in GC/MS & LC/MS
- Metabolic annotation examples
- LC/MS workflow
- Searching databases
- Hypothesis & experimental validation
- “Static”, “Dynamic” & "Integrated" pictures of metabolomics
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Talk Citation
Yanes, O. (2018, May 31). Metabolomics: importance of experimental design to data acquisition 2 [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 6, 2024, from https://doi.org/10.69645/PMJE8072.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Oscar Yanes has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Metabolomics: importance of experimental design to data acquisition 2
Published on May 31, 2018
30 min
Transcript
Please wait while the transcript is being prepared...
0:00
Hello. My name is Oscar Yanes.
I'm the scientific coordinator of Ciberdem
which is the Spanish National Network for the Study
of Diabetes, and I'm Assistant Professor at
the Universitat Rovira i Virgili in Tarragona, Spain.
The second part of the talk covers mainly the bioinformatic aspects of
the metabolomics workflow, involving
the pre-processing of the data and metabolite identification.
0:23
So once the samples have been analyzed by mass spectrometry or nuclear magnetic resonance (NMR),
we will have tonnes of a spectra that we need to process for data analysis.
0:35
Converting raw spectrometric data into
biological knowledge, has become the major bottleneck for metabolomics.
As you can see here in this slide going from raw spectral data -
either mass spectrometry data or NMR data, to biological information, to the name
of the metabolites, and the association of these names and their structures to
particular pathological diseases, is the main problem in the field right now.
The reason is twofold.
In one aspect, it is because of
the large physico-chemical diversity of compounds that I introduced at the beginning.
But if we go to the next slide,
1:16
I'm sure that anyone listening to my presentation, has been in
a discotheque or pub, with many people talking at the same time,
and if you try to distinguish the different dialogues in this room;
it becomes very difficult to separate the different dialogues, coming from each person.
This is why it's called the cocktail party problem.
In metabolomics we have a similar problem.
Instead of dialogues, we have peaks, and then we have
a huge overlap of peaks in our spectra.
In order to identify the structure and the name of these metabolites,
we need to separate or extract the peaks, belonging to each metabolite from the spectra.
Here, you can see what I'm referring to as the cocktail party problem in metabolomics.
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