Krista L. Ternus

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

This paper uses machine learning on microscopy images to predict microbial community taxonomic diversity and dominant bacterial classes, achieving high accuracy and low error.

SeqScreen-Nano rapidly characterizes novel pathogens using ONT sequencing in resource-limited settings by identifying ORFs and classifying them functionally and taxonomically.

SeqScreen uses ensemble learning to accurately label pathogenic sequences with functional annotations, achieving high recall and precision for microbial pathogenesis.

This study analyzes over 7,000 SARS-CoV-2 datasets to reveal genomic diversity, showing iSNVs and small indels drive variation, and highlights implications for qRT-PCR and transmission tracking.

This study evaluates NGS-based methods for detecting healthcare-acquired pathogens in simulated skin transmission events using metagenomic and metatranscriptomic sequencing.

This paper develops a rapid workflow for detecting and characterizing bacteremia pathogens using metagenomic sequencing and targeted PCR, achieving sensitivities of 1x10^6 and 1x10^5 cells/mL for untargeted and targeted detection, respectively.

SIANN detects target organisms in mixed samples with high specificity and sensitivity, even at low abundances, using alignment to near neighbor genomes.