This course provides an introduction to the use of bioinformatics in biological research, giving you guidance for using bioinformatics in your work whilst also providing hands-on training in tools and resources appropriate to your research.

You will initially be introduced to bioinformatics theory and practice, including best practices for undertaking bioinformatics analysis, data management, and reproducibility. 

You will be required to review some pre-recorded material for their group project prior to the start of the course.

Group projects

A major element of this course is a group project, where you'll be placed in small groups to work together on a challenge set by trainers from EMBL-EBI and external institutes. This allows you to explore the bioinformatics tools and resources available in your area of interest and apply them to a set problem, providing you with hands-on experience relevant to your own research. The group work will culminate in a presentation session involving everyone 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 your 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.

Group project one: 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, attempting to find clinical association and linking them to phenotypes.

Project mentor: Anu Shivalikanjli | EMBL-EBI 

Group project two: 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 mentor: Marcus Bage | EMBL-EBI and Joseph Ellaway | EMBL-EBI

Group project three: 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 and Krishna Tiwari | EMBL-EBI 

Group project four: Improving AI-based bioimage analysis 

Artificial Intelligence (AI) algorithms outperform classical image analysis methods, however, the performance of these models is highly dependent on the quality of the annotated image datasets used to train them. In this project, you will explore the application of AI for biological imaging and the relationship between model training data and model performance. You will use models stored in the BioImage Model Zoo and data in the BioImage Archive to fine-tune and aggregate AI outputs. The aim of this project will be to test, evaluate, and improve model performance on a diverse set of microscopy images and annotations within the BioImage Archive. You will learn how to apply, train, tune, and employ the most performant state-of-the-art computer vision models. This project serves as a valuable demonstration of how FAIR (Findable, Accessible, Interoperable, Reusable) data plays an essential role in the training and enhancement of AI models. 

Project mentors: Aybuke Kupcu Yoldas | EMBL-EBI and Craig Russel | EMBL-EBI 

Group project five: Single-cell RNA-sequencing analysis with Python

In this project, you will learn how to perform single-cell RNA-sequencing data analysis to investigate cell type heterogeneity and expression differences across conditions. The analysis will be based on the SCANPY framework in Python. You will start by collecting the raw count matrix and relevant metadata from the Single-cell Expression Atlas. After constructing the AnnData objects, you will perform quality control, preprocessing, dimensionality reduction, cell type annotation, and differential expression analysis. We will also explore the batch effect and its correction. 

Project mentors: Yuyao Song | EMBL-EBI and Anna Vathrakokoili-Pournara | EMBL-EBI 

Group project six: 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 may also give a few R packages a try.

Project mentor: Priit Adler | University of Tartu