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README.md

DU-Bii module 6: Integrative Bioinformatics

Access to training material

Teaching material

Topics Trainers Teaching material
Analyse multi-omique par factorisation multi-niveaux de matrices Laura Cantini, Sébastien Déjean and Jérôme Mariette Session 1 & 2
Network Analysis & Cytoscape Anaïs Baudot Session 3
Web semantique, représentation des connaissances Alban Gaignard Session 4
Network Inference & WGCNA Costas Bouyioukos Session 5

Description

This course takes place in the 1-month training "Diplôme Universitaire en Bioinformatique Intégrative" (DU-Bii) organised by Université Paris-Diderot and the Institut Français de Bioinformatique (IFB).

Pre-requisites

All participants are encouraged to follow the two introductory videos and read the review in the Paris Diderot course "Moodle" page. https://moodlesupd.script.univ-paris-diderot.fr/mod/page/view.php?id=167920

Table of contents

Session 1 & 2: Integrating multi-omics data with multi-level matrix factorisation

Contenu HTML pdf Rmd R
Presentation Laura Cantini Slides
Practical MOFA html Rmd
Presentation Sébastien Dejean et Jérôme Mariette Slides
MixOmics Slides R
Practical mixKernel html Rmd

Teachers: Laura Cantini, Sébastien Déjean, Jérôme Mariette

Concepts:

  • Integrative bioinformatics approaches and their application to cancer - Motivation
    - Which approach to answer which question (subsetting, modules, pathways) ?
    - Main methodologies: networks, matrix factorisation
  • Principles of multi-level matrix factorisation (Sébastien Déjean)
  • Kernel-based approaches (Jérôme Mariette)

Practicals:

  • MOFA
  • mixOmics
  • JM tools (please specify)

Datasets:

  • Chronic Lymphoblastic Leukemia (CLL)
  • metagenomics data (Jérôme Mariette)

Session 3: Network Analysis & Cytoscape

Teacher: Anaïs Baudot

  • Introduction to network sciences in biology

  • Practical with Cytoscape

    • Basics on human interactome
    • Keywords: interactome, regulome, network visualisation and topological analyses
    • Practicals: Tuto

Session 4: Web sémantique et représentation des connaissances

Teacher: Alban Gaignard

Session 6: WGCNA, network inference

Teacher: Costas Bouyioukos

  • Introduction to gene co-expression networks.
  • Introduction to WGCNA and the concept of eigengenes.
  • Introduction: inferring networks from *omics data, clustering for Gene Regulatory Networks.

A document to familiarise with the terminology of correlation networks and WGCNA can be found here

  • Practical with R
    • Inferrence of co-expression networks with the WGCNA package

The document containing the R code for the TP, together with explanations and output graphs can is here: Network_Inference_with_WGCNA.html

Conclusions and mentions of Inferelator and cMonkey, two network inference tools which combine RNA-seq and Chip-Seq data.

Credits

Course coordinators

  1. Anaïs Baudot
  2. Olivier Sand
  3. Jacques van Helden
  4. Costas Bouyioukos

Other teachers

  1. Laura Cantini
  2. Sébastien Déjean
  3. Jérôme Mariette
  4. Alban Gaignard

Installation

Contributors (members of the teaching team)

git clone git@github.com:DU-Bii/module-6-Integrative-Bioinformatics.git

Other people

git clone https://github.com/DU-Bii/module-6-Integrative-Bioinformatics.git

License

This content is released under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0) license. See the bundled LICENSE file for details.

Ce contenu est mis à disposition selon les termes de la licence Creative Commons Attribution - Partage dans les Mêmes Conditions 4.0 International (CC BY-SA 4.0). Consultez le fichier LICENSE pour plus de détails.