Topics And Speakers


Mihai POP, Professor, Department of Computer Science
Director, Institute for Advanced Computer Studies (UMIACS)
Title To Be confirmed (topic on metagenomics in human health and disease)

Marco Antonio MENDOZA-PARA, team leader, Genoscope - Centre National de Séquençage · Laboratory of Synthetic and Systems Biology LISSB
Title : Quality Control Assessment of ChIP-Seq and Related Deep Sequencing-Generated Datasets

Practical sessions

Generation and analysis of data

→ "Workflow optimisation and data analysis on HPC facilities"

Valentin LOUX, INRA,
→ “Quality control, alignment and variant calling”

Variation in the human genome (SNPs, CNV...)
Vincent MEYER, Lilia MESROB and Florian SANDRON, CNRGH, CEA
→ Content on Whole Exome/Genome Sequencing as a strategy to identify the genetic bases of common Mendelian disorders and rare diseases.

Statistical methods (1)
Christophe AMBROISE, Université d’Evry-Val d’Essonne
Guillem RIGAILL, Inra Université d’Evry Val d’Essonne
→ Transcriptome / RNA-Seq analysis (Whole Transcriptome Sequencing) : quantification and differential expression
→ How to choose the appropriate statistical methodologies : detecting bias and troubleshoots and interpreting statistical results
→ Statistical inference /hypothesis testing
→ Quantitative genetics

Strategies for metagenomics

Katarzyna HOOKS, Université de Bordeaux , CNRS UMR 5800,
→ bioinformatic tools for the study of microbial communities in the scope of human health
Outcome of this session : After this workshop you will be able to : find a suitable tool to analyse the metagenomic sequencing, extract publicly available data from repositories (e.g. EBI Metagenomic), analyse and visualise it using web-based resources.

Andrei ZINOVYEV, Inserm U900-Institut Curie
→ Network-based visualization of genomics data
After this session, students will master using several popular platforms for network-based visualization of genomics data : Cytoscape, NaviCell, MINERVA. The students will be able to visualize a transcriptomic, proteomic, mutational, copy-number profile on an existing biological network (for example, from Atlas of Cancer Signaling Network), or to construct an ad hoc biological network from a pathway database, and use it for data visualization.