Christopher Quince

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

Polyethylene glycol in human biofluids can mark drug exposure rather than contamination; it is oxidized to acid metabolites and appears in 2.3% of public metabolomics files.

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.

The paper models microbial community diversity using the hierarchical Pitman-Yor process and shows improved diversity estimates when species are missing.

SNooPy is a statistical framework for long-read metagenomic variant calling that outperforms existing methods and is available on GitHub.

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 evaluates DNA extraction and storage protocols for human faecal samples to optimize high-molecular weight DNA recovery for long-read metagenomics.

This study shows geography explains more skin microbiome variation than skin type, and host genetics contributes new structure, with 107 SNPs linked to microbial taxa.

This study improves metagenome-assembled genomes from human gut microbiota using long-read sequencing and novel assembly methods, yielding more high-quality MAGs and uncovering uncultured diversity.

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.

Strainberry uses long-read data to separate microbial strains in low-complexity metagenomes, achieving high accuracy and additional genomic material in real datasets.

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

The paper identifies host-microbiome interactions using systems genetics in mice, revealing mechanisms of microbial involvement in disease.

The paper investigates how microbial diversity converges during the growth of methanogenic granules, suggesting a biofilm life-cycle with distinct size-related stages.

The paper uses machine learning to predict functional and metabolic traits of microbes from their genomes, finding that lasso logistic regression on gene ortholog frequencies performs best.

SPARSE improves taxonomic classification of metagenomic reads by reducing false positives and enhancing sensitivity for low-abundance species detection.

The paper benchmarks computational metagenomics software using complex datasets, showing strengths and limitations in assembly, binning, and taxonomic profiling.