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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.

 

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 also require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 1.5GHz CPU, 2GB RAM, 10GB 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). If you do not have access to your own computer, please contact support@bioinformatics.ca for other possible options.

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. non-targeted metabolomics

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 Statistical Methods (Jeff Xia)

  • Distributions and significance
  • Introduction to univariate (t-tests and ANOVA) and multivariate (PCA and PLS-DA) statistics
  • Correlation and clustering

Module 5: MetaboAnalyst (David Wishart and Jeff Xia)

  • Standard metabolomics data analysis workflow
  • Introduction to MetaboAnalyst and its modules
  • Metabolomic data processing
  • Data reduction and statistical analysis
  • Metabolite Set Enrichment Analysis (MSEA)
  • Pathway analysis
  • Biomarker analysis

Lab Practical: Metabolomic Data Analysis using MetaboAnalyst 3.0

  • Use MetaboAnalyst to analyze:
    • NMR-based metabolomic data
    • GC-MS-based metabolomic data
    • LC-MS/MS-based metabolomic data
Workshop Details:

Duration: 2 days

Start: May 27, 2019

End: May 28, 2019

Location: Edmonton, Alberta Canada
Course Mode:

Status: Registration Closed

Workshop Ended

Offers:
for applications received between to
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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