Python is an object-oriented programming language that is ideal for biological data analysis. The course will start from zero knowledge, and will introduce the participants to all the basic concepts of Python such as calculating, organizing data, reading and writing files, program logic and writing larger programs. All the examples and practical sessions will focus on solving biological problems.

High-throughput technologies allow us to detect transcripts present in a cell or tissue. This course covers practical aspects of the analysis of differential gene expression by RNAseq. Participants will be presented with real world examples and work with them in the training room, covering all the steps of RNAseq analysis, from planning the gathering of sequence data to the generation of tables of differentially expressed gene lists and visualization of results. We we will also cover some of the initial steps of secondary analysis, such as functional enrichment of the obtained gene lists.

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.

Mass spectrometry based proteomic experiments generate ever larger datasets and, as a consequence, complex data interpretation challenges. In this course, the concepts and methods required to tackle these challenges will be introduced, covering peptide and protein identification, quantification, and differential analysis. Moreover, more advanced experimental designs and blocking will also be introduced. The core focus will be on shotgun proteomics data, and quantification using label-free precursor peptide (MS1) ion intensities. The course will rely exclusively on free and user-friendly software, all of which can be directly applied in your lab upon returning from the course. You will also learn how to submit data to PRIDE/ProteomeXchange, which is a common requirement for publication in the field, and how to browse and reprocess publicly available data from online repositories. The course will thus provide a solid basis for beginners, but will also bring new perspectives to those already familiar with standard data interpretation procedures in proteomics.