P

pipeline

Official implementation of "An open-source Python workflow to characterize food samples and food processes by hyperspectral image analysis coupled with machine learning."

Annotation pipeline (transform SGBDR/CSV data to rdf)

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.

An hybrid tool to accurately scaffold genomes using Hi-C and Deep-Learning.

Juicer workflow, but compressed :) (Using BAM instead of SAM)

A reimplementation of Bisulfite Sequencing Processing Software (BiSePS https://forgemia.inra.fr/irhs-bioinfo/biseps)