Samuel P. Forry

The paper evaluates sequence screening tools using a NIST dataset to assess their consistency in identifying sequences of concern.

This paper introduces a mathematical framework using internal standards to correct compositional biases in microbiome datasets, improving accuracy and precision in taxa abundance measurements.

The paper evaluates metagenomic workflows for pathogen detection using NIST RM 8376 in CSF and stool samples, finding workflow-dependent strain discrimination and sample-agnostic detection responses.

This study quantifies methodological biases in microbiome sequencing and shows how to improve dataset comparability across different protocols.

This study demonstrates that extracellular antibiotic resistance genes (eARGs) can be taken up and expressed by gut microbes, promoting antibiotic resistance propagation through natural transformation.

The paper discusses considerations for performance metrics in metagenomic NGS analyses, emphasizing the need for taxon-specific evaluation and appropriate summary metrics.