This course, organised in association with Wellcome Connecting Science, 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 project set by resource and data experts from EMBL-EBI and external collaborating institutes. These projects will end with a presentation from each group on the final day of the course to bring together learnings from all participants.

Participants will be required to review some pre-recorded material prior to the start of the course and will have an opportunity to meet other trainees in an induction session to be held virtually in the week before the course take place.

Group projects

A major element of this course is a group project, where participants will be placed in small groups to work together on a challenge set by trainers from EMBL-EBI and external institutes. This allows people to explore the bioinformatics tools and resources available in their area of interest and apply them to a set problem, providing participants with hands-on experience relevant to their own research. The group work will culminate in a presentation session involving all participants on the final day of the course, giving an opportunity for wider discussion on the benefits and challenges of working with biological data.

Groups are mentored and supported by the trainers who set the initial challenge, but the groups will be responsible for driving their projects forward, with all members expected to take an active role. Groups are pre-organised before the course, and all group members will be sent some short “homework” in preparation for their project work prior to the start of the course.

Basic outlines of the projects on offer this year are given below. In your application you must indicate your first and second choice of project, based on which you think would benefit your research most. Not all projects may be offered, and final decisions on which projects will be run during the course will be made based on the number of applicants per project.

Most of the projects cover mammalian data sets, however, in many cases, the methods and approaches taught are transferable to data from various species.

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.

Project mentors: Priit Adler (University of Tartu), Hedi Peterson (University of Tartu)

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.

Project mentors: Baron Koylass (EMBL-EBI)

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-silico 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.

Project mentors: Rahuman Sheriff (EMBL-EBI) 

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, and implement this process programmatically.

Project mentors: David Armstrong (EMBL-EBI), Preeti Choudhary (EMBL-EBI)

Analysis of intercellular interactions in healthy and diseased states

Ulcerative colitis is an inflammatory bowel disease. The exact pathomechanism of the disease is unknown. However, the interactions between the intestinal immune cells and the intestinal epithelial cells play a crucial role during the development of the disease. Single-cell RNA-seq measurements can help us understand these complex interactions. The expression data combined with protein-protein interaction databases can shed light on the connections between cells in diseased and healthy states.

During this project, you will use a single-cell RNA-seq dataset to build interactions between the various cells. The dataset contains pre-processed, cell type classified data of biopsies from healthy, inflamed and non-inflamed UC colonic biopsies. The interactions between cells will be downloaded from the OmniPath database. You will use Python Notebooks to build up the intercellular networks, map the single cell RNA-seq expression data and visualise them. The intercellular networks between various cell types can then be compared by Cytoscape.

Project mentors: Dezso Modos (Quadram Institute), Marton Olbei (Earlham Institute)