Offered simultaneously in Edmonton, Alberta and Montréal, Québec.
Using high-throughput technologies, life science researchers can identify and characterize all the small molecules or metabolites in a given cell, tissue, or organism. The CBW course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases. Hands-on practical tutorials using various data sets and tools will assist participants in learning metabolomics analysis techniques.
Participants will gain practical experience and skills to be able to:
- Design appropriate metabolome-focused experiments
- Understand the advantages and limitations of metabolomic data analysis
- Devise an appropriate bioinformatics workflow for processing and analyzing metabolomic data
- Apply appropriate statistics to undertake rigorous data analysis
- Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome
This course is intended for graduate students, post-doctoral fellows, clinical fellows and investigators who are interested in learning about both bioinformatic and cheminformatic tools to analyze and interpret metabolomics data.
You will require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 2.4GHz CPU, 8GB RAM, 100GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements).
This workshop requires participants to complete pre-workshop tasks and readings.
Module 1: Introduction to Metabolomics (David Wishart)
- Short history of metabolomics and metabolomes
- Relationship between metabolomics and other “omics”
- Principles of NMR, chromatography, and mass spectrometry
- Targeted vs. non-targeted metabolomics
Module 2: Metabolite Identification and Annotation (David Wishart)
- Spectral deconvolution and its application to NMR, GC-MS, and LC-MS data
- Introduction to software tools: Bayesil, AMDIS, and GC-AutoFit
- Introduction to MS databases and database searches: PubChem, ChEBI, CFM-ID, and NIST
Lab Practical: Compound ID and Quantification:
- Perform metabolite ID and/or quantification using:
- NMR data and Bayesil
- GC-MS data and GC-AutoFit
- Explore results with Human Metabolome Database
Module 3: Databases for Chemical, Spectral, and Biological Data (David Wishart)
- Explore different database models and different kinds of metabolomic databases
- Introduction to public spectral databases, pathway databases, and comprehensive metabolomic databases
- Optional exercises:
- Identify and annotate metabolites using databases
- Explore software tools and databases
Module 4: Backgrounder in omics data science (Jeff Xia)
- Overview of omics data analysis patterns and strategies
- Introduction to univariate (t-tests and ANOVA), multivariate (PCA and PLS-DA) statistics and biomarker analysis
- Introduction to enrichment analysis and pathway analysis
Module 5: Using MetaboAnalyst 5.0 for targeted and untargeted metabolomics (Jeff Xia)
- Introduction to MetaboAnalyst and its modules
- LC-MS spectra processing and annotation
- Metabolomic data processing (missing value, batch effect, normalization)
- Data reduction and statistical analysis
- Functional analysis
Lab Practical: Metabolomic Data Analysis using MetaboAnalyst 5.0
- LC-MS-based metabolomic data – from raw spectra to pathway activities
- Compound concentration table – from patterns to biomarkers
Module 6: Integrating Metabolomics with other Omics (Jeff Xia)
- Current challenges and practices for multi-omics integration
- Introduce and explore Xia Lab tools (https://www.xialab.ca/tools.xhtml) for integrating metabolomics with SNPs, transcriptomics, and microbiome data
Duration: 2 days
Start: Jul 06, 2023
End: Jul 07, 2023
Status: Registration Closed
Workshop Ended
Download the poster announcing this workshop
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