Journal: Proceedings of the National Academy of Sciences of the United States of America

Article Title: Distinct classes of gut bacterial molybdenum-dependent enzymes produce urolithins

doi: 10.1073/pnas.2501312122

Figure Lengend Snippet: Metagenomic abundance of the eam gene cluster correlates with urolithin A levels and gut inflammation. ( A ) Scatter plots showing multivariate linear regression analyses between urolithin A metabolomic abundance and the eam gene cluster (average of eah , eadh1 , and eadh2 ) or ucdh metagenomic abundances. Lines represent trend, and colored areas represent SE. Adjusted P values were calculated using Benjamini–Hochberg correction with a target FDR of 0.05. ( B ) Boxplots of urolithin A concentration, Coriobacteriia gene cluster abundance, and ucdh abundance across host phenotypes. One-way ANOVA, Tukey’s multiple comparison. ns, nonsignificant; *0.01 < P value < 0.05; ***0.0001 < P value < 0.001; **** P value < 0.0001. ( C ) Schematic of a potential mechanistic link connecting IBD, the Coriobacteriia gene cluster, urolithin A, and its health effects. IBD, inflammatory bowel disease; CD, Crohn’s disease; UC, ulcerative colitis.

Article Snippet: The metagenomic dataset from the PRISM study was downloaded from the National Center for Biotechnology Information (PRJNA400072) ( ).

Techniques: Concentration Assay, Comparison

Journal: bioRxiv

Article Title: Rapid terabase-scale simulation of realistic metagenomes for experimental design and pathogen detection with RandomReadsMG

doi: 10.1101/2025.07.18.665570

Figure Lengend Snippet: (A) Schematic diagram of the RandomReadsMG workflow, detailing the path from input FASTA files to output FASTQ reads. Key configurable modules include coverage modeling, platform selection, and the simulation of errors and artifacts. (B) A representative coverage plot from a simulated Illumina metagenome. The figure shows the variable, wave-like read depth generated along a contig. Visualization was done with the Integrative Genomics Viewer (IGV) . (C) Simulated GC, insert size (Illumina), and length (Pacbio/ONT) distributions to demonstrate the simultaneous modeling of both compositional and structural read properties modeled by RandomReadsMG .

Article Snippet: As presented in , RandomReadsMG provides a realistic, unpredictable coverage distribution across a genome in a simulated Illumina metagenome dataset.

Techniques: Selection, Generated

Journal: bioRxiv

Article Title: Rapid terabase-scale simulation of realistic metagenomes for experimental design and pathogen detection with RandomReadsMG

doi: 10.1101/2025.07.18.665570

Figure Lengend Snippet: (A) Workflow for the construction of a baseline drinking water (DW) microbiome, composed of 103 genomes with realistic abundance profiles derived from real metagenomic data. (B) Taxonomic and genomic characteristics of the 103 members of the baseline DW microbiome. (C) Validation of the simulation parameters. Six different pathogens were spiked into the baseline community at 11 different read depths. The resulting read depth, genome coverage, and relative abundance are shown to correlate directly with the simulated input depth. (D) Assessment of pathogen genome recovery. Each simulated metagenome was processed through an assembly and binning pipeline (top schematic). The completeness and contamination of the resulting pathogen MAG are plotted against the initial simulated pathogen depth (bottom plots). (E) Computational resources (elapsed time and RAM) required for the simulation of each metagenome.

Article Snippet: As presented in , RandomReadsMG provides a realistic, unpredictable coverage distribution across a genome in a simulated Illumina metagenome dataset.

Techniques: Derivative Assay, Biomarker Discovery