Samuel Blanquart

This study characterizes the bacterial, archaeal, and fungal communities of Ascophyllum nodosum across multiple environmental factors, revealing dynamic bacterial interactions and stable fungal associations.

The paper develops a computational pipeline to classify gut microbiome states using functional annotations, showing comparable performance to microbial profiles and identifying reliable discriminant variables.

This study analyzes 60 brown algal genomes to identify genomic innovations and evolutionary patterns that enabled their adaptation to coastal ecosystems.

AuCoMe reduces technical biases in metabolic network comparisons across diverse genomes and identifies shared and divergent metabolic traits.

EsMeCaTa estimates consensus proteomes and metabolic functions from taxonomic data, validated on environmental datasets and applied to a methanogenic reactor study.

MATAM improves taxonomic assignment in metagenomics by accurately assembling short reads into marker genes, outperforming existing methods in error rates and genome recovery.