→ 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
Generation and analysis of data
Nicolas WIART CNRGH, CEA
→ "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.