Ryan A. Leo Elworth

Olivar automates variant-aware primer design for tiled amplicon sequencing of pathogens, improving mapping rates and coverage compared to existing methods.

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

Squeegee identifies microbial contaminants in low biomass microbiomes using de novo analysis, showing high precision in simulated and real datasets.

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.

PlasmidHawk predicts the lab-of-origin of synthetic plasmids with higher accuracy than machine learning methods, identifying signature sub-sequences responsible for predictions.

KOMB identifies repetitive and homologous genomic regions in metagenomes using K-core graph decomposition, validated on simulated and real datasets.

This paper introduces RACE, a fast and memory-efficient algorithm for diversified sampling from data streams, applied to metagenomic sequence analysis to improve diversity metrics.