This course 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.

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

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 data resource and research teams.  This allows people to explore the bioinformatics tools and resources available in their area of interest and to apply these to a set problem, providing them with hands-on experience of relevance 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 by the trainers who set the initial challenge, but active participation from all group members is expected.  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.

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

This year’s projects are as follows:

Networks and pathways

The project will make use of gene expression data (RNA-seq) to build protein-protein interaction networks which can be used to explore functional relationships between the (potentially) expressed protein products. You will use Cytoscape to visualise protein networks, identify key regulators of biological pathways and explore biological function through network analysis, integration and co-visualisation of additional data, and ontology/functional enrichment analysis - helping to build a better view of the wider biological context.

Metabolic network engineering using a systems model based approach

You will work with a model example of metabolic pathways set, coming from the BioModels database, and you will learn how to carry out computational analyses to find common patterns (i.e. set of reactions) in the network. These might include computing feasible pathways through the network, and minimal set of reactions to knock out specific metabolic functions. Visualisation of results will be achieved with an interactive graphical tool available as a web service.

Modelling cell signalling pathways

Curating models of biological processes is an effective training in computational systems biology, where the curators gain an integrative knowledge on biological systems, modelling and bioinformatics. You will learn to encode models of signalling pathways from a recent publication using COPASI and reproduce the simulation results. Furthermore you will learn to annotate models and learn to re-use pre-existing models from open repositories such as BioModels.

Proteomics (data analysis and functional annotation)

In this project, you will obtain real-life proteomics data from clinical tumour samples. Your task will be to process the raw data, analyse the results, and eventually interpret them in a wider context using the Open Targets Platform.

An introduction to deep learning through functional annotation of proteins

Automatically annotating protein sequences with functional information is vital in a world where sequences are produced so fast that humans can't keep up. In this project you will explore how deep learning can be used to enrich sequences automatically.

Single cell characterization of cell types and cell development

This project will make use of single cell RNA Sequencing data (scRNA-Seq) to show how to: 1) quality control the sequencing data; 2) understand the variances of the data; 3) cluster the cell types; 4) understand the cell development; 5) find differential expression genes that determine the cell types or cell development. You will use data from the Human Cell Atlas and Tabula Muris to understand human and mouse cell types respectively.

Finding and extracting meaningful structural data from PDBe

This project will introduce you to the wealth of data available at PDBe and how this can be extracted to analyse macromolecular structures. You will firstly explore the search and entry pages at PDBe to identify the type of data available for analysis. Using this knowledge, you will then use and adapt template scripts in order to access this data programmatically and analyse a subset of your results. This project should give you the foundation of knowledge about how to access data through the PDBe API, and how you can analyse subsets of PDB data related to your field of expertise.

Exploring variation data across human populations

Natural variation is required to generate the broad range of traits and phenotypes that exist between single individuals and between different populations. In this project you will explore the results of SNP-calling using web-based resources such as Ensembl Variant Effect Predictor. You will predict the functional consequences of variants between separate human populations and identify the variant(s) within your samples that have been associated with several interesting phenotypes.