Sébastien Raguideau

REMAG improves eukaryotic genome recovery from metagenomic data by using non-contrastive self-supervised learning, outperforming existing tools in producing near-complete genomes.

SCGBinner improves long-read metagenomic binning by using single-copy gene guided contrastive learning, outperforming existing methods on real-world datasets and recovering novel microbial species.

nanoMDBG improves metagenome assembly from ONT reads, producing more high-quality MAGs with less computational resources than existing assemblers.

This study shows rapid establishment of a distinct soil surface microbiome in temperate agricultural soils, enriched in photosynthetic taxa and functional pathways related to carbon and nutrient cycling.

This study compares the genome of a Mucoromycotina AM fungus to Glomeromycotina fungi, revealing distinct symbiotic traits and potential ecological roles.

The paper shows that changes in ARG abundance in environmental studies may be due to community shifts rather than antibiotic selection, using correlations with ribosomal RNA genes.

The paper introduces a metagenomics assembler that improves efficiency and assembly quality for long reads, achieving faster performance and better recovery of MAGs and viral/plasmid sequences.

STRONG identifies strains de novo from metagenome samples using coassembly, binning, and a Bayesian algorithm to resolve haplotypes and abundances.