With a constant evolution of technologies, laboratory biologists are faced with an increasing need of bioinformatics skills to deal with high-throughput data storage, retrieval and analysis.

Although several resources developped for such tasks have a web interface (most of the time, the first choice of biologists), many operations can be more efficiently handled with command lines (CLI).

This course introduces best practices in research data management (how to collect, describe, store, secure and archive research data) and the need for a Data Management Plan (DMP), an evolving document reporting how the research data will be managed during and after a research project.

This "First Steps with R" course is addressed to beginners wanting to become familiar with the R environment and master the most common commands to be able to start exploring their own datasets.

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.

Shiny is an open-source R package that allows building interactive web applications from R. This course introduces the R Shiny framework and equips R programmers with the basic tools to build and deploy their own web apps.

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.