The aim of the course is to present a complete computational pipeline for the analysis and interpretation of Next-Generation Sequencing (NGS) data such as exome sequencing or targeted panels that are commonly used in the clinic.

3C-based methods, such as Hi-C, produce a huge amount of raw data as pairs of DNA reads that are in close spatial proximity in the cell nucleus. Overall, those interaction matrices have been used to study how the genome folds within the nucleus, which is one of the most fascinating problems in modern biology. The rigorous analysis of those paired-reads using computational tools has been essential to fully exploit the experimental technique, and to study how the genome is folded in the space. Currently, there is a clear expansion on the wealth of data on genome structure with the availability of many datasets of Hi-C experiments down to 1Kb resolution. In this course, participants will learn to use TADbit, a software designed and developed to manage all dimensionalities of the Hi-C data.

Recent years have seen rapid development in biological mass spectrometry and proteomics. In particular, the quantitative performance has taken a huge leap forward, both as a label-free (LF) approach and as multiplexed quantitative mass spectrometry. This development has enabled new technologies for unbiased characterization of drug molecules with protein targets, which is one of the major challenges in drug development. Here we will explain the basic principle of protein quantification by mass spectrometry.

This course will cover the analysis of B and T cell repertoires (adaptive immune receptor repertoires, or AIRR) from high-throughput sequencing data using bioinformatics workflows. Topics include germline allele assignment, identification of clones, and visualisation/analysis of clonal frequencies. The course will also cover emerging standards (VDJML, Change-O) in the analysis and reporting of Adaptive-immune Receptor Repertoire (AIRR) data. In addition to an introduction to software used, such as IgBLAST, pRESTO, Change-O, etc., the course will also provide an introduction to the use of Jupyter notebooks for conducting exploratory analyses and the Common Workflow Language to automate analyses.

For much of its history, our knowledge of evolution has been based heavily on theoretical models and hypotheses. In the age of novel experimental and technological approaches, we are now increasingly able to evaluate this theory; however, the basics of how and why to develop and analyze a simple model are often forgotten in the process of NGS analysis. This course aims at training evolutionary biologists in classical modeling and teach them ways to approach their own research questions through evolutionary theory.

This is one of our "Foundations" type courses, providing a systematic and detailed review of fundamental concepts and techniques in Biostatistics. Participants can expect to go through a set of exercises that are based on biomedical problems. These exercises are preceded by short lectures that are simple to follow. The lectures are designed to provide the conceptual framework that is needed to release the training power of the exercises, not to flood the participants with formality, which will be kept to a minimum. We will make use of a highly interactive methodology, taking advantage of our well equipped Bioinformatics training room. With this approach, we expect to bring the participants to a high degree of usage independence in using the methods that we cover.

This is an entry level course aimed that those with a reasonable biological background but no significant experience with bioinformatics. The course is broadly based around a series of exercises in which a combination of simple analytical tools and reference to publicly available databases is applied to the investigation of a single human gene. The training manual for the course is comprised of detailed instructions for the tasks undertaken. Included are, questions (with answers) and discussion of and the interpretation of the results achieved.

The course will provide a solid basis for beginners, but also new perspectives to those already familiar with standard data interpretation procedures in proteomics.