This virtual course, organised in association with Wellcome Genome Campus, Scientific Conferences and Advanced Courses, provides an introduction to the use of bioinformatics in biological research, giving participants guidance for using bioinformatics in their work whilst also providing hands-on training in tools and resources appropriate to their research.

Participants will initially be introduced to bioinformatics theory and practice, including best practices for undertaking bioinformatics analysis, data management and reproducibility. To enable specific exploration of resources in their particular field of interest, participants will be divided into focused groups to work on a small project set by EMBL-EBI resource and research staff, ending in a presentation from each group on the final day of the course to bring together learnings from all participants.

The course includes training and mentoring by experts from EMBL-EBI and external institutes.

Virtual course

Participants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and communication via Slack. 

Pre-recorded material will be made available to registered participants prior to the start of the course and in the week before the course there will be a brief induction session. Computational practicals will run on EMBL-EBI's virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.

Participants will need to be available between the hours of 09:30-17:30 BST each day of the course. Trainers will be available to assist, answer questions and further explain the analysis during these times.

Group projects

Genome variation across human populations

Natural variation between individuals or between different human populations is a result of genome mutations throughout evolutionary history. Some mutations may become fixed because of their beneficial effect while most drift among individuals. During this project, you will investigate genomic variation between two separate human populations of European and Asian descent. Using sequence data from a number of individuals from each population, you will use a range of bioinformatics tools to discover variants that exist between them. In the second section of the project, you will attempt to analyse the functional consequences of the variants you have identified, linking them to phenotypes.

Modelling cell signalling pathways

Curating models of biological processes is an effective training in computational systems biology, where the curators gain an integrative knowledge of biological systems, modelling and bioinformatics. You will learn to encode and simulate ordinary differential equation models of signalling pathways from a recent publication using user-friendly software such as COPASI even without extensive mathematical background. You will learn to perform in silco experiments, new predictions and develop hypotheses. Furthermore, you will learn how to annotate models and re-use pre-existing models from open repositories such as BioModels.

Interpreting functional information from large scale protein structure data

This project will introduce you to the wealth of publicly available data in the Protein Data Bank (PDB) and give you the opportunity to investigate how large subsets of structure data can be used to analyse protein features and determine function. In the project you will learn how to: identify relevant protein structures, collate and interpret functional information, implement this process programmatically.

Networks and pathways

This project will cover typical bioinformatics analysis steps needed to put differentially expressed genes into a wider biological context. You will start with gene expression data (RNA-seq) to build an initial interaction network. Next, you will learn to combine public network datasets, identify key regulators of biological pathways and explore biological function through network analysis. You will get first-hand experience in integration and co-visualising with additional data and functional enrichment analysis. All this helps to put the initial results into a previously known context and provide hypotheses for potential follow up experiments. We will use Cytoscape, Expression Atlas, g:Profiler, StringDb, among other tools. We also may give a few R packages a try.

Multiomics analysis of human disease

In this project, you will explore the benefits of multiomics data integration to investigate the onset and progression of human disease. You will analyse plasma proteomics and metabolomics data from patients and healthy controls to identify immunological and physiological changes that are associated with disease severity. You will exploit differential analysis, dimensionality reduction methods and multiomics integration tools to identify features that distinguish different patient groups, perform functional enrichment analysis, and visualise metabolomics and proteomics correlation networks in Cytoscape. Finally, you will build basic machine learning models to predict the course of disease and to propose therapeutic interventions that are likely to be most effective at different disease stages

Posters

All participants are expected to provide a poster for the course. We expect the posters to provide other delegates and trainers with information on your research and to act as a talking point. They should give an idea of the work you are engaged in, what you are planning to do next, any challenges you have experienced in your research and anything of interest that might be useful for other delegates. Further information about posters will be provided following application selections.