This three-day course will provide an overview of the RNA-seq analysis pipeline, as well as the downstream analysis of the resulting data using bioconductor packages in R. The course will cover the following topics:

  • The structure of an RNAseq analysis pipeline:
    • Raw data quality check;
    • RNAseq reads alignment;
    • Gene Expression level quantification and normalization by reads counting;
    • De novo Transcripts reconstruction and differential splicing.
  • Overview of downstream analysis
    • Differential Expression analysis with R/Bioconductor packages;
    • Class discovery: usage of Principal Component Analysis, Clustering, Heatmaps, Gene Set Enrichment Analysis in RNA-seq analysis.

Next Generation Sequencing (NGS) techniques will not be covered in this course; experimental design as well as the statistical methods will not be detailed in this course.

We currently live in an era where most computers possess multiple computing units, and where parallelization is key. In particular, GPGPUs (General Purpose Graphical Processing Units) are built for massive parallelism and they have recently risen to prominence as they are now used for many scientific tasks, such as physics or biological simulations, statistical inference or machine learning.

In this crash course we will focus on CUDA as well as several CUDA-based API, including openMP GPU offloading and python APIs. Through concrete examples we will describe the principles at the core of a successful parallelization attempt.

Have you ever been stuck with a file format that doesn't precisely conform to your needs, found yourself doing annoyingly repetitive data manipulations, or struggled to efficiently manage and explore your data? Python to the rescue!

Python is an open-source and general-purpose scripting language which runs on all major operating systems. It was designed to be easily read and written with comparatively simple syntax, and is thus a good choice for beginners in programming. Python is applied in many disciplines and is one of the most common languages for bioinformatics. The Python community enthusiastically maintains a rich collection of libraries/modules for everything from web development to machine learning. Other programming languages such as R have comparable functionality to Python, however some tasks are more natural (and easier!) in Python.

This 3-days course is addressed to beginners who want to become familiar with writing Python code to accomplish common tasks such as automated data parsing, basic statistical operations and graphical representations.