Bérénice Batut

The paper introduces the Microbiology Galaxy Lab, a community-driven platform for reproducible and FAIR microbial data analysis, offering tools for various omics data types and use cases.

FAIRyMAGs provides a modular, FAIR-compliant Galaxy pipeline for generating and analyzing metagenome-assembled genomes, improving accessibility and reproducibility in genome-resolved metagenomics.

This study develops a machine learning framework to predict memory requirements for metagenomic assembly, improving efficiency in high-performance computing environments.

PathoGFAIR provides FAIR and adaptable metagenomics workflows for detecting and tracking pathogens from Nanopore sequencing data, achieving species-level detection in most tested samples.

Planemo is a command-line toolkit for developing, deploying, and executing Galaxy tools and workflows, promoting best practices in scientific data analysis.

The paper introduces Galaxy Training Network (GTN) as a framework for creating and sharing FAIR training materials for data analysis, with recent improvements to support educators and expand its use in various learning environments.

The paper introduces a Galaxy-based suite for scRNA-seq analysis, offering workflows, training resources, and tools for preprocessing, normalization, and clustering across 10x and alternative platforms.

Bioconda provides a sustainable, comprehensive distribution of bioinformatics software using Conda for reproducible analysis across platforms.

The paper presents a community-driven framework for interactive data analytics training in life sciences, enabling researchers to learn through web-based computation without software installation.

ASaiM provides a Galaxy-based framework for analyzing microbiota data, offering curated tools and workflows for taxonomic and functional analysis of raw sequencing data.