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 will present all the bioinformatics tools required to analyze RNA-seq gene expression data, from the raw data to the biological interpretation. This two-day course will discuss the following topics:

• Quality control and reads cleanup
• RNAseq reads mapping to genome & transcriptome
• Gene reads counting, gene & exons differential expression
• GO enrichment and pathway analysis

In this course, we will see more advanced statistical models and techniques to provide you the necessary set of tools that will enable you to analyze different types of (biological) data, beyond classical linear modeling.

The course will be centered on "statistical modeling" applied to biological problems. Topics addressed during this course include advanced linear models, mixed models, generalized linear models, survival analysis. The emphasis will be put on concrete applications in biology, enabling the participants to analyze data consisting for example of counts or presence/absence of a feature.

At the end of this course, participants are expected to be able to:

• understand statistical modeling
• understand the specificities of the different models
• identify the appropriate model to analyze a dataset
• fit the desired model using R