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

Course Syllabus:

• 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

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.

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

Click here to view additional Birmingham Metabolomics Training Centre courses.