Bas E. Dutilh

The paper identifies structural adaptations in Microviricetes capsids that enable salinity tolerance by analyzing amino acid residues linked to ocean and lake environments.

This study identifies bacterial secretion systems enriched in the rhizosphere and their associated taxa, suggesting potential applications for bioinoculant optimization.

This study uses genomic feature extraction and machine learning to identify environmental adaptations in prokaryotes, revealing gene and ncRNA associations with salinity, temperature, oxygen, and pH preferences.

PhageTransformer predicts phage hosts with high accuracy and speed, outperforming existing tools on benchmark datasets.

This paper organizes a community challenge to develop automated virus taxonomy classification tools and evaluates their performance on a combined dataset of expert-curated and metagenomic sequences.

The paper analyzes oral microbiomes from museum specimens to show that diet drives oral microbiome evolution and some microbes have ancient origins.

The paper analyzes cross-study metagenomics data to identify microbial signatures associated with urinary tract infections and their genomic variation.

The paper predicts homooligomeric phage protein structures using a computational framework, confirming predictions for dimers and trimers and showing high accuracy in classifying homooligomeric states.

The paper identifies cosmopolitan gut bacteriophages that expand the functional repertoire of health-related bacteria across diverse populations.

The paper identifies synchronized codon usage evolution between functionally related genes across bacterial genomes, revealing new functional associations.

The paper shows that deleting the moeA gene in Flavobacterium IR1 changes structural color from green to blue and affects polysaccharide metabolism.

The paper introduces vConTACT3, a machine learning tool that improves virus taxonomy classification, scalability, and accuracy, and identifies taxonomic issues across the virosphere.

Barbell improves demultiplexing of Nanopore sequencing data by addressing barcode artifacts, reducing trimming errors and barcode bleeding.

This study investigates how microbial communities adapt after coalescence by mixing freshwater and seawater samples in controlled experiments, finding that environmental filtering has a stronger impact than mixing ratios.

Vclust clusters viral genomes efficiently and accurately using Lempel-Ziv parsing for average nucleotide identity, outperforming existing tools.

The paper isolates Acidobacteriota viruses from Arctic tundra soils and analyzes metagenome-assembled viral genomes to characterize their diversity and potential interactions with host bacteria.

The paper uses chemistry-based vectors to analyze untargeted mass spectrometry data and identifies compound classes that differentiate natural biomes.

SpeSpeNet is an R-shiny tool for creating and visualizing microbial correlation networks, with features for environmental data integration and customization.

This paper introduces DNNGIOR, a deep learning method to improve genome-scale metabolic models of incomplete genomes by imputing missing reactions based on reaction frequency and phylogenetic distance.

Graphite uses a colored De Bruijn graph to paint genomes, reducing matches and revealing new biological insights in microbial genomics.

Jaeger is a deep-learning tool that accurately and quickly identifies bacteriophage sequences in metagenomic data, achieving high sensitivity and precision.

This study quantifies the rhizosphere effect along a soil-root gradient in Arabidopsis thaliana, showing microbial community convergence driven by selection and priority effects.

RAT improves metagenomic read annotation and taxonomic profiling by integrating MAGs and contigs, achieving high precision and sensitivity in diverse samples.

This study benchmarks ten virus identification tools on metagenomic datasets from three biomes, finding significant variation in their performance and suggesting parameter adjustments for better results.

The paper quantifies microbial niche breadth using environmental sequencing data, revealing generalist and specialist strategies across the tree of life.

The paper identifies and validates a comprehensive list of mitochondrial proteins in Plasmodium falciparum using Bayesian statistics and experimental confirmation.

The paper identifies over 100,000 horizontal gene transfer events across 90,000 bacterial genomes, revealing frequent long-distance genetic exchange and its functional impact.

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.