Elizabeth McDaniel

The paper creates a paired dataset of fermented foods and human cell responses to enable predictive models of their anti-inflammatory effects through multi-omics approaches.

The paper uses machine learning on fermented food datasets to predict bioactive peptides and their health benefits, providing publicly available tools and resources for further research.

The paper uses activity-targeted metaproteomics to study rare syntrophic bacteria in anaerobic communities, revealing their metabolic activity and a previously hypothesized oxidative glycine pathway.

The paper introduces TbasCO, a trait-based comparative omics approach that identifies ecosystem-level and niche-differentiating adaptations in microbial communities using genome-resolved time-series metatranscriptomics.

The paper uses genome-resolved metatranscriptomics to analyze gene expression differences among co-occurring Accumulibacter clades in a wastewater treatment bioreactor, revealing functional distinctions.

The paper identifies diverse microbial guilds in a sulfate-enriched lake that possess the mercury methylation trait, highlighting the ecological distribution of this process.

The paper introduces metabolisHMM, a tool for analyzing microbial metagenomic data to explore phylogenies and metabolic pathways through HMM-based workflows.