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Course Description

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.

This is a distributed workshop. It is held simultaneously at two locations, with faculty and TAs present at both venues. Lectures are broadcast back and forth between sites. When applying, please choose the location where you would like to attend.

Course Objectives

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

 

Target Audience

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.

Prerequisites

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.

Course Outline

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. untargeted metabolomics

Module 2: Survey of Metabolomics Software and Databases (David Wishart) 

  • Review of different kinds of metabolomic databases and software 
  • Introduction to public spectral (MS and NMR) databases, pathway databases (KEGG and PathBank), and comprehensive metabolomic databases (HMDB, MiMeDB) 
  • Brief review of popular metabolomics software tools: XCMS, MS-DIAL, MZMine, MetaboAnalyst 
  • Identifying unknowns with spectral prediction and compound generation tools (SIRIUS, CFM-ID, FraGGNet, BioTransformer, DeepMet) 

Module 3: Targeted metabolomics – methods and software (David Wishart) 

  • Targeted metabolomics methodologies 
  • Spectral deconvolution and targeted metabolomics using NMR, GC-MS, and LC-MS data 
  • Introduction to software tools: MagMet, GC-AutoFit and LC-AutoFit 

Lab Practical: Targeted metabolomics – Perform metabolite ID and quantification using: 

  • NMR data and MagMet 
  • GC-MS data and GC-AutoFit 
  • LC-MS data and LC-AutoFit 
  • Explore results with the Human Metabolome Database and PathBank 

Module 4: Backgrounder in statistics (Jeff Xia) 

  • Introduction to basic statistics (normal distributions, statistical significance) 
  • 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: MetaboAnalyst 6.0  

  • 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 

Module 6: MetaboAnalyst for untargeted metabolomics

Module 7: (Lab Practical): Metabolomic Data Analysis using MetaboAnalyst 6.0

  • LC-MS-based metabolomic data – from raw spectra to pathway activities
  • Compound concentration table – from patterns to biomarkers

Module 8: (Lab Practical): Multi-omics data analysis using XiaLab Tools (xialab.ca/tools.xhtml)

  • Using OmicsNet to integrate significant metabolites with SNPs, transcriptomics, and microbiome data via knowledge-driven network integration
  • Using OmicsAnalyst  to integrate metabolomics data table with other omics data via multivariate dimensional analysis (such as MOFA, DIABLO)
Workshop Details:

Duration: 3 days

Start: Jun 03, 2024

End: Jun 05, 2024

Location: Edmonton, Alberta Canada

Course Mode: Distributed

Status: Application Open

Apply
Offers:
CAD $695 for applications received between April 3, 2024 to February 7, 2024
CAD $895 for applications received between April 4, 2024 to May 20, 2024
Limited to: 30 participants
Lead Instructors:
Open Access Content:

Canadian Bioinformatics Workshops promotes open access. Past workshop content is available under a Creative Commons License.

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