Metabolomics

H3ABioNet_high_res

Prepared by: Faisal M. Fadlelmola
Module Name: Metabolomics
Possible Lecturer(s): Faisal M. Fadlelmola
Contact hours (to be used as a guide)Total (40 hours), Theory (60%), Practical (40%)

SPECIFIC OUTCOMES ADDRESSED

On completion of this module, students should be able to

1. Explain the mode of operation of the most common analytical techniques used in the acquisition of metabolomic data.
2. Explain and carry out the mathematical and statistical methods to derive biological relevant information from metabolomics data sets.
3. Demonstrate critical awareness of current practices and recognise the relative strengths and weaknesses of the techniques covered and how these relate to the quality of the biological findings.
4. Explain and perform data collection and analysis, using pathway databases to performing pathway analysis.
5. Demonstrate an understanding of metabolomics databases and explore chemical databases.
6. Demonstrate an understanding of metabolomics applications (microbial metabolomic, nutritional metabolomics and cancer biomarker discovery).

BACKGROUND KNOWLEDGE REQUIRED

H3ABioNet bioinformatics modules as pre-requisites:  Genomics and Comparative Genomics; Proteomics (recommended)
Additional: Introductory courses on genomics, proteomics and bioinformatics.

BOOKS & OTHER SOURCES USED

1. The Handbook of Metabolomics. Fan, Teresa Whei-Mei, Lane, Andrew N, Higashi, Richard M. (Eds.). Humana Press, 2012.
2. Metabolomics: A Powerful Tool in Systems Biology. Jens Nielsen, Michael C. Jewett. Springer, 2007.
3. Metabolomics: Methods and Protocols. Wolfram Weckwerth. Humana Press, 2007.
4. Informatics and Statistics for Metabolomics (2013): Canadian Bioinformatics Workshops http://bioinformatics.ca/workshops/2013/informatics-and-statistics-metabolomics-2013

COURSE CONTENT

A) Theory lectures

1. Introduction to Metabolomics. (3 hour(s))

2. Introduction to NMR, LC-MS and GC-MS. (3 hour(s))

3. Introduction to R. (3 hour(s))

4. Multivariate classification (PLS-DA, SVMs, ANNs). (4 hour(s))

5. Multiway analysis (PARAFAC). (4 hour(s))

6. Compound identification (e.g. spectral library searching). (3 hour(s))

7. Sampling and Sample Preparation in Microbial Metabolomics. (4 hour(s))

8. Metabolomics applications: cancer miomarkers discovery; microbial metabolomic; nutritional metabolomics. (4 hour(s))

B) Practical component

1. Metabolomic data analysis using MetaboAnalyst.  (follows lecture 1 )

2. LC-MS spectra processing using XCMS.  (follows lecture 2 )

3. Metabolite identification and annotation.  (follows lecture 4 )

4. Statistical methods for metabolomics.  (follows lecture 5 )

5. Software for metabolite identification and quantification.  (follows lecture 6 )

6. Databases for chemical, spectral and biological data.  (follows lecture 6 )

 ASSESSMENT ACTIVITIES AND THEIR WEIGHTS

Coursework (40% weight) comprising:
– Assignments/Quizzes (20% weight)
– Project/Case Study (20% weight)
Midterm Exam: 20%
Final Exam: 40%

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