Virtual course

Summer school in bioinformatics

Welcome to the new EMBL-EBI Training site. Please tell us what you think!

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


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.

Who is this course for?

This course is aimed at individuals working across life sciences who have little or no experience in bioinformatics. Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their knowledge and skills further. No previous knowledge of programming is required for this course; group projects may give you the opportunity to learn basic programming, but participants will be supported in this by their mentors. Depending on your chosen project, an introductory programming tutorial may be given as homework prior to attending the course.

What will I learn?

Learning outcomes

After this course you should be able to:

  • Discuss applications of bioinformatics in biological research

  • Browse, search, and retrieve biological data from public repositories

  • Use appropriate bioinformatics tools to explore biological data

  • Comprehend ways that biological data can be stored, organised and integrated

Course content

During this course you will learn about:

  • Bioinformatics as a science

  • Designing bioinformatics studies

  • Data management and reproducibility

  • Basic tools and resources for bioinformatics

The exact range of resources and tools covered will vary depending on the group project undertaken; there will be no opportunity for you to analyse your own data during this course.


Alexandra Holinski
Anna Swan
Alex Bateman
Sarah Morgan
Lee Larcombe
Peter McQuilton
Patricia Carvajal Lopez
Nikiforos Karamanis
Boris Adryan
Merck Group
Selene L. Fernandez-Valverde
Langebio Cinvestav
David Armstrong
Hema Bye-A-Jee
Baron Koylass
Monica Abrudan
Wellcome Sanger Institute
Michal Szpak
Rahuman Sheriff
Kausthubh Ramachandran
Krishna Tiwari
John Berrisford
Maria Zimmermann
Sander Wuyts
Priit Adler
University of Tartu, Estonia
Hedi Peterson
University of Tartu, Estonia



Time (BST) Subject Trainer
Day 1 - 28th June
10:00 – 10:45 Welcome and introduction Alexandra Holinski & Anna Swan
10:45 – 11:15 Icebreaker Alexandra Holinski & Anna Swan
11:15 - 11:30 Break  
11:30 – 12:30 The science of bioinformatics Alex Bateman
12:30 – 13:30 Lunch  
13:30 – 14:30 Introduction to the command line Patricia Carvajal Lopez
14:30 – 15:00 Break  
15:00 – 16:00 Good data management: Making your data FAIR Peter McQuilton
16:00 – 17:00 Social: tell us about your work!  
17:00 - 17:30 Chat with biocurators Hema Bye-A-Jee, David Armstrong
Day 2 - 29th June
09:30 - 10:00 Introduction & mini-challenge Alexandra Holinski & Anna Swan
10:00 – 11:15 An introduction to EMBL-EBI data resources Sarah Morgan & Training Team
11:15 - 11:30 Break  
11:30 – 12:30 Data visualisation 101: A practical introduction to designing scientific figures Niki Karamanis
12:30 - 13:30 Lunch  
13:30 – 14:00 Q&A for pre-recorded keynote lecture Boris Adryan
14:00 – 14:15 Break  
14:15 - 16:00 Introduction to group projects and meet your mentors and group Sarah Morgan and all mentors
Day 3 - 30th June
09:45 - 10:00 Day introduction  
10:00 - 12:30 Group projects with mentors  
12:30 - 13:30 Lunch  
13:30 - 14:00 Mini tutorial Q&A: Interpreting Integrated Data (optional) Lee Larcombe
14:00 - 16:00 Group project work  
Day 4 - 1st July
09:45 - 10:00 Day introduction Alexandra Holinski & Anna Swan
10:00 - 12:00 Group projects  
12:00 - 12:30 General bioinformatics chat Hedi Peterson, Lee Larcombe, Patricia Carvajal Lopez
12:30 - 13:30 Lunch  
13:30 - 15:30 Group projects  
15:30 - 16:00 Q&A for pre-recorded keynote lecture Selene L. Fernandez-Valverde
Day 5 - 2nd July
09:45 – 10:00 Day introduction Alexandra Holinski & Anna Swan
10:00 – 12:30 Group project work & presentation preparation  
12:30 – 13:30 Lunch  
13:30 – 14:45 Group presentation with mentors  
14:45 – 15:15 Course feedback and wrap up Alexandra Holinski & Anna Swan

This course is organised in association with Wellcome Genome Campus, Scientific Conferences and Advanced Courses. Applications are handled through their website.

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28 June - 02 July 2021
Scientific conferences

  • Alexandra Holinski
  • Anna Swan
  • Lee Larcombe
  • Hedi Peterson
  • Sarah Morgan
  • Lucy Criddle
    Wellcome Connecting Science

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