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Topic: Metabolomics Data Processing and Data Analysis Online Course (Read 529 times) previous topic - next topic

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Metabolomics Data Processing and Data Analysis Online Course
Venue: Online
Date: 8 October 2018 - 2 November 2018 (~4 hours per week)
Level: The course would be ideally suited to MSc / PhD students or scientists who are in the early stages of analysing metabolomics data. No previous knowledge of the data processing and statistical analysis approaches is assumed, but a basic understanding of the metabolome, and the analytical techniques applied in the metabolomics field would be beneficial. A pre-course recommended reading list will be provided.

Overview
This online course explores the tools and approaches that are used to process and analyse metabolomics data. You will investigate the challenges that are typically encountered in the analysis of metabolomics data, and provide solutions to overcome these problems. The course is delivered using a combination of short videos, articles, discussions, and online workshops with step-by-step instructions and test data sets. We provide quizzes, polls and peer review exercises each week, so that you can review your learning throughout the course.

The material is delivered over a four week period, with an estimated learning time of four hours per week. We support your learning via social discussions where you will be able post questions and comments to the team of educators and the other learners on the course. In the final week of the course there is a live question and answer session with the entire team of educators.  If you do not have time to complete the course during the 4-week period you will retain access to the course material to revisit, as you are able.

Topics include:
  • An introduction to metabolomics
  • An overview of the untargeted metabolomics workflow
  • The influence of experimental design and data acquisition on data analysis and data quality
  • Processing of NMR data
  • Processing direct infusion mass spectrometry data
  • Processing liquid chromatography-mass spectrometry data
  • Reporting standards and data repositories
  • Data analysis, detecting outliers and drift, and pre-treatment methods
  • Univariate data analysis
  • Multivariate data analysis (including unsupervised and supervised approaches)
  • The importance of statistical validation of results
  • Computational approaches for metabolite identification and translation of results into biological knowledge
  • What are the future challenges for data processing and analysis in metabolomics

For further information and registration details, please visit https://www.birmingham.ac.uk/facilities/metabolomics-training-centre/courses/Metabolomics-Data-Processing-and-Data-Analysis.aspx or contact bmtc@contacts.bham.ac.uk.
  • University of Birmingham