Bas E. Dutilh

The paper investigates STS in bacterial genomes, finding they are common and often linked to prophages, with mechanisms to evade auto-immunity.

RaFAH is a machine-learning method for virus-host prediction that outperforms existing methods with an F1-score of 0.97 at the phylum level, discovering new archaeal virus lineages and auxiliary metabolic genes.

OGRE clusters metagenomic reads into species-specific bins efficiently for large datasets without computational limitations.

This study reconstructs the genome of the non-culturable spinach downy mildew pathogen Peronospora effusa using metagenome filtering and identifies infection-related genes.

This study investigates the association between HLA haplotype and gut microbiome composition using 3,002 public datasets, finding that similar HLA molecules correlate with similar microbiota and HLA homozygosity is linked to reduced microbiome diversity.

The paper introduces tools for taxonomic classification of metagenomic sequences and MAGs, showing improved accuracy in handling unknown organisms.

This paper develops a deep learning approach to predict ALS from genotype by leveraging genome structure, showing better performance than other methods.

The paper analyzes global ocean viruses to uncover their genomic diversity and ecological roles in biogeochemical cycles.

The paper predicts high-resolution metabolomes from metagenomic datasets, showing body-site specific metabolomes and identifying common cosmetics ingredients as major factors shaping the human skin microbiome.