Marc W. Van Goethem

This study uses long-read metagenomics and metatranscriptomics to analyze soil communities, revealing phylum-specific secondary metabolite dynamics during wetting events.

This paper introduces a hybrid clustering algorithm for metagenome assembly that improves accuracy and scalability by combining long and short-read clustering.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

This study evaluates eight metagenome assemblers on nine environmental datasets to guide tool selection based on research goals, resources, and expertise.