Informatics and Statistics for Metabolomics
Date: May 3-4, 2012
Location: Toronto, ON
Lead Faculty (2012): David Wishart
Registration Fee for Applications received before April 2, 2012: $500 + HST
Registration Fee for Applications received after April 2, 2012: $700 + HST
Awards available for 2012.
Apply now!
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.
Prerequisite: Your own laptop computer. Minimum requirements: 1024x768 screen resolution, 1.5GHz CPU, 1GB RAM, 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 access to a laptop, you may loan one from the CBW. Please contact course_info@bioinformatics.ca for more information.
Course Objectives
The workshop will cover 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. Participants will be given various data sets and short assignments to assist with the learning process.
Register now as space is limited to 30 participants.
Course Outline
Day 1
Module 1: Introduction to metabolomics and its technologies (Faculty: 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
• Examples of interesting/successful metabolomics studies
Module 2: Software for identifying and quantifying metabolites
• Introduction to spectral deconvolution
• Identifying and quantifying compounds from NMR spectra (Chenomx/AMIX)
• Identifying and quantifying compounds from GC-MS spectra (AMDIS)
• Identifying and quantifying compounds from LC-MS spectra
Lab Practical: Hands on compound identification via spectral deconvolution
• Identifying and quantifying compounds from example NMR spectra with Chenomx
Module 3: Databases for chemical substances and spectral data
• Review of PubChem, ChEBI, ChEMBL, DrugBank, HMDB, FooDB, PhenolExplorer (Contents and features)
• Review of Metlin, BMRB, MMCD, MassBank, HMDB (Contents and features)
• Spectral data exchange formats
Lab Practical: Hands on exploration of databases
• Structured lab using web-based chemical and spectral database resources to solve practical problems in metabolomics
Day 2
Module 4: Data processing and metabolomics pipelines (MetaboAnalyst) (Faculty: David Wishart)
• Introduction to univariate and multivariate statistics
• Principal Component Analysis, PLS-DA and clustering
• Metabolite Set Enrichment Analysis
• Over-representation analysis and pathway analysis
• Introduction to MetaboAnalyst and its modules
Lab Practical: Processing a metabolomics data set using MetaboAnalyst
• Structured lab using MetaboAnalyst to process metabolomic data of the student’s choice
Module 5: Databases for metabolism pathways
• Review of KEGG, Reactome, the Cyc databases and SMPDB (Contents and features)
• Review of Ingenuity Pathway tools and other commercial pathway resources (BioCarta, etc.)
• MetPA and pathway topology analysis
• Biological interpretation of metabolomic data
Lab Practical: Hands on exploration of pathway databases
• Structured lab using pathway databases to explain, visualize and interpret metabolomic data
Module 6: Integrating metabolomics with genomics & proteomics
• Introduction to systems biology and metaboloic modeling (SimCell)
• Critique of recent studies involving metabolomics and genomics/proteomics
• Current and future challenges for metabolomics
• Current and future challenges for integrated “omics” studies
