Phillip Byron Pope

The paper integrates holo-omic data from porcine hosts and gut microbiomes to predict host-microbiome interactions, identifying both known and novel relationships.

The paper investigates how two ruminant species digest brown algae by analyzing microbial pathways and omics data from in vivo and in vitro experiments.

This paper identifies two carbohydrate esterases from Bacteroides cellulosilyticus that work together to deacetylate complex β-mannans, with one showing specificity for 2-O-acetyl groups and the other acting broadly on diverse substrates.

The paper identifies carrageenan catabolism pathways in ruminant gut microbiomes, revealing their widespread distribution and evolutionary history.

This study swaps rumen contents between low- and high-methane-yielding cows and finds that methane production remains tied to the host, not the microbiome, with asymmetric microbiome reconstitution observed.

The paper introduces a collaborative platform for curating microbial metabolite knowledge to enhance metabolomics data interpretation.

The paper develops an in vitro model to study porcine caecal microbiota response to β-mannan and finds similar microbial responses to in vivo trials with some taxa showing greater changes in vitro.

The paper identifies functional differences in the rumen microbiome of lactating cows with varying methane intensity, linking specific metabolic pathways to methane production efficiency.

OmniCorr is an R package that clusters and visualizes multi-omics data to identify putative host-microbiota interactions across different biological systems.

This study identifies Ruminosignatures in cattle rumen microbiota linked to methane emissions and feed efficiency across breeds and geographies.

This study analyzes marsupial gut microbiomes to identify genetic factors contributing to their low methane emissions compared to ruminants.

This study shows that 3-NOP reduces methane emissions in ruminants by altering microbial communities and fermentation pathways, but limits productivity gains due to shifts in hydrogen-cycling microbes.

CompareM2 is a pipeline that compares microbial genomes to generate reports on quality, annotation, taxonomy, function, and phylogeny.

The paper identifies two rumen community types linked to protozoal populations and shows their impact on microbial interactions and metabolite profiles.

This study evaluates microbiota-directed fibers (MDFs) in Atlantic salmon diets, finding negligible effects on gut microbiome and host physiology, but identifying potential endogenous lactic acid bacteria for future MDF strategies.

The paper develops GutEuk, a deep-learning classifier, and curated protein databases to improve identification and functional analysis of eukaryotic microbes in ruminant gut metagenomes.

This paper trains an artificial neural network to predict microbial genome-scale metabolic networks directly from 16S rRNA gene sequences, enabling metabolic analysis of microbes without full genomes.