DU-Bii module 6: Integrative Bioinformatics

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Access to training material

• Web: https://du-bii.github.io/module-6-Integrative-Bioinformatics/2020/
• Github (sources): https://github.com/DU-Bii/module-6-Integrative-Bioinformatics/2020/

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

  • Vidéo cours 1: Cours
  • Slides cours 1: Slides
  • Vidéo cours 2: Cours
  • Slides cours 2: Slides

• 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.
  • Slides: SLidesM6S5

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.

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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

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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.