Video presentation of the 2023 edition
Integrative bioinformatics is a recent multidisciplinary scientific field that combines and analyses biological data from different sources in order to gain a holistic understanding of biological systems.
The Institut Français de Bioinformatique (IFB) is organising a thematic school aimed at bioinformaticians/biostatisticians/bioanalysts wishing to acquire theoretical and practical skills in integrative bioinformatics.
The school brings together a teaching team of 10 people and can accommodate a maximum of 30 participants for its second edition.
The entire training program will be based on the use of computing resources and work environments provided by the French Institute of Bioinformatics (https://www.france-bioinformatique.fr/en/compute-and-storage/).
This training is open to all scientists (engineers, researchers in research platforms or teams) involved in one or more integrative bioinformatics projects involving omics datasets of different kinds.
- Basic knowledge of Unix/shell, R, Python
- Autonomy in workstation management (installation of libraries and use of packaging environments like conda)
The course aims to :
- introduce the basic concepts and the different types of approaches used in integrative bioinformatics
- to offer an in-depth study and practical application of these approaches on one or more datasets incorporating different types of omics data
- participants will benefit from the teaching team's expertise in implementing an analysis on datasets proposed by the participants.
At the end of the course, participants will
- have acquired a general knowledge base in integrative bioinformatics
- have identified and applied the most commonly used methods in integrative bioinformatics (dimension reduction methods, network approaches, semantic web) to an example, and have implemented an integrative analysis from data preparation to critical analysis of the results on one or more datasets proposed during the course.
Three main categories of methods will be covered during the thematic school:
- Dimension Reduction methods will be used to reduce data complexity while retaining essential information, with two main categories of methods in particular:
- Non-negative Matrix Factorisation (NMF): You will learn how to apply NMF to extract hidden structures from multi-omics data, enabling more efficient visualisation and interpretation.
- Probabilistic Factor Analysis: You will explore in depth the method (MOFA) for extracting latent factors from multi-omics data, uncovering hidden relationships and correlations between different omics.
- Network approaches will be presented with the aim of identifying association profiles and essential relationships between different biological objects using methods that enable :
- analyse multi-omics biological interaction networks, highlighting crucial relationships.
- explore tools for assessing the topology of biological networks, identifying key nodes and understanding their functional roles, particularly in multilayer networks.
- The principles and tools of the Semantic Web will be presented with the aim of facilitating the search for multi-omics data across distributed resources, their integration and the discovery of knowledge. Two axes will be presented
- the principles of the Semantic Web and standards in biology
- ontologies and their use in life sciences to improve semantic searches and the integration of distributed data.
You will need to bring a laptop.
- NSBD (Marseille) : Anaïs Baudot, Morgane Terezol, Marie galadriel BRIERE
- Institut Pasteur (Paris) : Vincent Guillemot
- CNRGH (Évry-Courcouronnes) : Arnaud Gloaguen
- IRD (Montpellier) : Pierre Larmande
- IRISA (Rennes) : Olivier Dameron
- Bird (Nantes) : Alban Gaignard, Samuel Chaffron
- billile (Lille) : Jimmy Vandel
- IFB : Hélène Chiapello, Olivier Sand, Lucie Khamvongsa-Charbonnier