Training

Is it right for me?

This course is aimed at advanced PhD students and post-doctoral researchers who are planning to improve their ability or learn new techniques in metabolomics and applying optimal data analysis methods using various bioinformatics tools in their research. The aim of this course is to familiarize the participants with advanced data analysis and data fusion methodologies and provide hands-on training on the latest analytical approaches and abilities to visualized and map metabolic changes on the relevant pathways.

What will I learn?

Lectures will give insight into how biological knowledge can be generated from metabolomics experiments and illustrate different ways of analyzing such data using variety of open source and freely available tools. Practicals will consist of computer exercises that will enable the participants to apply statistical methods and different analytical and data processing software to the analysis of metabolomics data under the guidance of the lecturers and teaching assistants.  Familiarity with the technology such as data acquisition with NMR and MS is required. Ideally also some experience with R/Bioconductor (basic understanding of the syntax and ability to manipulate R objects) and the Unix/Linux operating system.

What will it cover?

The course covers optimal study design for metabolomics experiments, various data analysis methods, usage of online databases and resource as well theoretical a practical approaches on data fusion. Other topics will include: visualization of metabolomics data on metabolic pathways, methods of identification of unknown compounds, differential expression, data quality and reproducibility assessment using statistical analysis and optimal experimental study design.

You will be able to:

• Understand the fundamental principles of metabolomics experimental design and to learn the best practice for conducting metabolomics experiments  

• Understand the fundamental principles of the data analysis of MS-based metabolomics including: feature detection; grouping; quality control; and annotation

• Analyse an untargeted metabolomic workflow, and learn best practice and how to avoid potential pitfalls

• Learn about current methods in metabolomic network data analysis and how to map the identified metabolites onto a metabolic pathway

• Develop an awareness of current trends in metabolomics and its applications to disease

• Develop your own metabolomics experimental designs

• Learn some methods for the advanced analysis of metabolomics data, with a particular emphasis upon data fusion methods that incorporate prior knowledge

• Develop an awareness of strategies for dealing with sparse matrices in high-dimensional metabolomic biomarker applications

• Learn about genome-scale metabolic networks and the regulation of metabolite pools

• Know current conventions in metabolomics standards and why it is important for us to use them

• Browse, search and retrieve metabolomics data from widely used public repositories, tools and resources, including how to submit metabolomics results to the MetaboLights database

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