Rohan B. H. Williams

This study identifies oxygen's role in enabling renewable n-caprylate production from ethanol and acetate via intermediate metabolite synthesis in bioreactors.

The paper investigates the scalp microbiome in healthy individuals and those with dandruff, identifying propionic acid's role in microbiome balance and its implications for scalp health.

The paper analyzes protein sequence properties of the glgB gene to identify structural genetic variation associated with a glycogen accumulating phenotype in microbes.

This paper develops a machine learning approach using long-read sequencing to rapidly detect microbial contaminants in T-cell therapy cultures at low abundance.

The paper presents a draft genome of Pseudopithomyces maydicus, identifying gene models and biosynthetic gene clusters.

The paper evaluates MAG recovery strategies from activated sludge microbial communities, finding that co-assembly outperforms single-sample assembly and only 58 MAGs meet quality criteria.

The paper describes methods to validate long read assembled genomes using short read metagenome assembled genomes and assesses correction procedures' impact on genome quality, contributing new closed genomes from wastewater bioprocesses.

The paper demonstrates obtaining whole bacterial chromosomes from metagenomic samples using long-read sequencing and presents a pipeline with frame-shift correction.

The paper evaluates extraction solvents for untargeted metabolomics analysis of EBPR bioreactors, finding that optimal methods vary by reactor type.

The paper demonstrates that MinION long read sequencing can recover near-complete genomes from low-medium complexity microbial communities.

The paper investigates the apparent oxygen half saturation constant (Ks(app)) for Nitrospira sublineage I OTU in wastewater treatment plants, finding it lower than AOB, suggesting NOB can thrive under low oxygen conditions.

MEGAN-LR provides new algorithms for taxonomic and functional binning of long reads and contigs, with evaluation on simulated and real datasets.

The paper identifies gene expression covariation across human populations, showing it is biologically meaningful rather than technical, and links it to transcription factor activity and functional pathways.