R

R

Formation (supports de cours et TP) sur l'analyse statistique de données RNA-seq Formation proposée par la plateforme Genotoul-Bioinfo

Cette application permet aux utilisateurs de visualiser les caractéristiques physicochimiques de plus de 100 produits, ainsi que de comparer entre eux différents produits avec des boxplots.

https://eom-4-soil-visualisation-tool.sk8.inrae.fr/

Replication repository for the article "A comprehensive review and benchmark of differential analysis tools for Hi-C data"

This repository contains the scripts used to produce the experiments presented in

Jorge E., Hocking T.D., Neuvial P., Vialaneix N., and Foissac S. (2025) Posthoc inference for interpretable Hi-C differential analysis. preprint

These experiments aims at illustrating the use of the R package hicream that performs differential analysis of Hi-C data.

Cette application permet l'exploration, l'interrogation, la visualisation et l’interprétation fonctionnelle d'un ensemble pre-processé de données épigénétiques (ATAC-seq) correspondant à la régulation temporelle des réseaux de transcription pendant la régénération des axones du système nerveux central chez le poisson zèbre.

https://rosetta.sk8.inrae.fr

R-Toulouse website

Single-cell transcriptomic (SCT) analysis is essential for resolving cellular heterogeneity and uncovering the molecular foundations of development, physiology, and environmental responses. Despite its increasing importance, robust, reproducible, and broadly applicable SCT analytical tools remain largely restricted to well-annotated animal systems. This limitation poses significant challenges for biologists working on non-model species and poorly characterized tissues, where gene annotation is sparse and computational expertise is often limited.

We developed a stable, end-to-end SCT analysis pipeline designed to be accessible to biologists with little training in bioinformatics and applicable to species and tissues with limited genomic annotation. Built on the Seurat framework and complemented with additional tools, the pipeline supports any organism by automatically generating R-compatible annotation packages from basic genome files. It integrates standardized workflows for data preprocessing, quality control, clustering, biomarker identification, and gene set enrichment analysis within a fixed Snakemake framework, ensuring high reproducibility. By minimizing coding requirements, the pipeline enables rigorous, biologically informed SCT analyses across diverse experimental systems.