module-6-Integrative-Bioinformatics
Integrative bioinformatics course of the Diplôme Universitaire en Bioinformatique Integrative (DU-Bii)
View the Project on GitHub DU-Bii/module-6-Integrative-Bioinformatics
DU-Bii module 6: Integrative Bioinformatics
Examen 2019
- sujet du devoir personnel 2019
- Date-limite: dimanche 2 juin à 23h55
Access to training material
- Web: https://du-bii.github.io/module-6-Integrative-Bioinformatics/
- Github (sources): https://github.com/DU-Bii/module-6-Integrative-Bioinformatics/
Teaching material
| Topics | Trainers | Access |
|---|---|---|
| Introduction to the course + functional annotation of gene sets | Jacques van Helden | Seance1 |
| Integrating multi-omics data with multi-level matrix factorisation | Laura Cantini, Sébastien Déjean and Jérôme Mariette | Seance2-3 |
| Network Analysis with Cytoscape, session I | Anaïs Baudot & Costas Boulyakis | Seance4 |
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
Skills acquired during this course
At the end of this course, trainees should be able to do the following: $
- …
Concepts covered
- ….
(For Costas to verify PPI and RNA-Seq integration?)
Table of contents
Session 1: Functional interpretation of gene sets
Teachers: Jacques van Helden and Olivier Sand
Concepts:
- functional annotations,
- Gene Ontology,
- pathways (metabolic, signaling),
- biomolecular networks (protein-protein, co-expression, molecular complexes, metabolic reactions)
Resources:
- databases containing these data types.
- g:Profiler (Web site + R library)
- metabolic mapping tools (KEGG pathway coloring).
Enrichment tests:
- goals,
- principles and approaches: enrichment, GSEA, …
- statistics (seen in the course Stat with R),
- good practices (choice of an appropriate background, multiple testing correction),
- interpretation of the results)
Application:
- Annotation of groups of genes produced by the other courses (e.g. differentially expressed genes from RNA-seq, disease-associated genes, …)
Sessions 2 and 3: Integrating multi-omics data with multi-level matrix factorisation
| Contenu | HTML | Rmd | R | |
|---|---|---|---|---|
| Presentation Laura Cantini | Slides | |||
| Presentation Sébastien Dejean et Jérôme Mariette | Slides | |||
| MixOmics | Slides | R | ||
| Practical MOFA | html | Rmd | ||
| Practical mixKernel | html | Rmd |
Teachers: Sébastien Déjean, Jérôme Mariette et Laura Cantini
Concepts:
- Integrative bioinformatics approaches and their application to cancer (Laura Cantini)
- 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)
Practical:
- mixOmics
- JM tools (please specify)
Datasets:
- Chronic Lymphoblastic Leukemia (CLL)
- metagenomics data (Jérôme Mariette)
Session 4: Network Analysis with Cytoscape, session I
Teachers: Anaïs Baudot and Costas Bouyioukos
- Introduction to network sciences in biology
- Practical with Cytoscape
- Basics on human interactome
- Basics on bacterial regulons
- Keywords: interactome, regumone, network visualisation and topological analyses
- Slides: SlidesM6S4.pdf
- Practicals: TutoM6S4.pdf
Session 5: WGCNA, network inference
Teachers: Costas Bouyioukos and Anaïs Baudot
- Introduction to gene co-expression networks.
- Introduction: inferring networks from *omics data, clustering for Gene Regulatory Networks.
- Introduction to WGCNA and the concept of eigengenes.
- Practical with R
-
Inferrence of co-expression networks with the WGCNA package
- Slides: SlidesM6S5.pdf
A document to familiarise with the terminology of correlation networks and WGCNA can be found here
The document containing all 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.
Session 6: Network Analysis with Cytoscape, session II
Teachers: Anaïs Baudot and Costas Bouyioukos
-
Introduction to Network algorithms (measures, guilt-by-association, clustering), slides here
-
Practical with Cytoscape, the tutorial for parts 1 and 2 can be found here
- Integrating networks: multiplex networks - Integrating expression data in networks: jActive module - Integrating knowledge in networks: Enrichmap
Planning DU-Bii 2019
https://tinyurl.com/dubii19-planning
Credits
Course coordinators
- Anaïs Baudot, CNRS, Marseille
- Costas Bouyioukos, Université Paris-Diderot, UMR7216
- Olivier Sand
Other teachers
- Jacques van Helden, Institut Français de Bioinformatique, Aix-Marseille Université
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.