B4EST short course: Data integration for prediction and breeding – new tools for obtaining, processing and integrating phenomic and genomic data

23 June 2022

This course runs on Thursday 23 June, starting at 8h30, ending at 12h30
In addition, half-day individual web-based “coaching sessions” are available in September 2022 – see information below

Summary: System biology approaches are becoming rapidly the goal of analytical frameworks aiming at developing comprehensive studies of the biological reaction to the changing environment. Under such analytical frameworks, we find a combination of computational and mathematical approaches that are applied typically to multilayered datasets covering different levels of integration between the phenome and the genome. The final aims of such integrative framework are: to reveal the architecture of interactions within biological systems, to comprehend the functioning of the system and to predict accurately the phenome.

This short course is not intended to offer a detailed overview of system biology techniques. Indeed, this is a rapidly expanding interdisciplinary field, where new methodologies are been developed every day, with still no clear mainstream method. This course intends to be a first hands-on introduction to the subject, through some cases studies in trees that propose some of the most simple and robust approaches to data integration. Most of the lectures will be illustrated with R scripts showing the key analytical steps and tools.


  • Obtaining “omics” datasets, latest genotyping and sequencing methodologies explained. Overview of the classical and novel methodologies for genotyping and sequencing that help to populate the omics multilayered datasets
  • Simple ways to combine transcriptomics and genomic polymorphisms for phenotype prediction. Through a case study in Poplar, we illustrate here an omics integration approach involving transcriptomics and SNP polymorphisms, and aimed at predicting phenotypes in a context of genomic selection.
    Multiomics prediction (Abdou Wade, Harold Duruflé and Leopoldo Sanchez)
  • Piling up longitudinal phenotypic data to construct genomic reaction norms. We present here another way of data integration equally useful when it comes to quantify the role of environment in tree reactions. The strategy illustrated here combines into a single modelling approach genomic polymorphisms and climatic and environmental records associated to candidate trees, with the aim of predicting individual genomic norms of reaction in a case study of maritime pine from the breeding population.
    Construction of norms of reaction – Case study : Maritime pine (Pinus pinaster)
    (Victor Papin, Laurent Bouffier, Leopoldo Sanchez-Rodriguez)

Time: Morning of Thursday 23 June, starting at 8h30, ending at 12h30

Targeted group: PhD students, postdoctoral fellows and researchers with background in at least one of the following fields: quantitative genetics, functional genomics, bio-informatics, statistics

Place : Lisbon and one embedded web-based lecture.

To register send an email to Leopoldo.sanchez-rodriguez@inrae.fr

Teachers: Patricia Faivre-Rampant (INRAE), Véronique Jorge (INRAE), Fabien Mounet (University of Toulouse), Raphael Ployet (Oakridge, USA), Abdou Wade (INRAE), Harold Duruflé (INRAE), Leopoldo Sanchez (INRAE), Victor Papin (INRAE), Laurent Bouffier (INRAE)


  • Introduction, 8:30 – 8:40 (LS)
  • Lecture 1, 8:40 – 9:20, obtaining the omics (PFR, VJ)
  • Lecture 2, 9:20 – 10:15, omics integration (FM, RP)
  • Break, 10:15 – 10:35
  • Lecture 3, 10:35 – 11:30, multiomics prediction (AW, HD, LS)
  • Lecture 4, 11:30 – 12:25, integration for norms of reaction (VP, LB, LS)

(Part II): Customized web-based training sessions dedicated to phenomic and genomic data analyses and integration
For those who are going to attend B4EST “Data integration for prediction and breeding: new tools for obtaining, processing and integrating phenomic and genomic data” (Part I), we propose half-day individual web based “coaching sessions” (September 2022). Considering your objectives, one or several lecturers involved in this training session will dedicate their time to provide you competent advices / strategies, and propose the most appropriate analytical pipelines to process and analyze your own datasets.

If you are interested, please send a mail to fabien.mounet@univ-tlse3.fr and leopoldo.sanchez-rodriguez@inrae.fr with the following information:
(Please, be aware that most of the analytical pipelines use code-based scripts and require at least medium skills for using R software.)
Institution (Laboratory/team/supervisor)
Level in bioanalyses/biostatistical tools
Brief description of the objectives (datasets, expectations, etc…)
Any unavailability during the 1st week of September