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
This course aims at providing participants with the essential tools to understand the output of bioinformatics analysis pipelines, interpret the results for making better-informed clinical recommendations, recognise the main challenges and limitations when using NGS bioinformatics analysis pipelines in clinical settings, and acquire a common language to foster multidisciplinary discussions of results with clinical bioinformaticians in their team. It will cover the essential bioinformatics concepts on NGS data management and processing for typing and phylogenetics, resistance and virulence prediction, and metagenomics-based taxonomic classification.
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
This course is exclusively restricted to UNIL/CHUV members currently using the Vital-IT HPC cluster.
CAS-PMO 2018-2019