Michael Nute

Seqwin identifies microbial signature sequences by analyzing large genome collections, outperforming existing methods in efficiency and quality.

Kente detects horizontal gene transfer in microbiomes using a pangenome graph-based approach, achieving higher precision and reducing false positives in simulated and real human gut metagenomes.

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

Strainify estimates strain-level abundances from low-coverage short-read metagenomic data, outperforming existing methods in simulated and real datasets.

This paper introduces Lemur and Magnet, lightweight tools for accurate taxonomic profiling of long-read metagenomic datasets, showing they run efficiently on laptops.

Parsnp 2.0 improves scalability and control for multiple genome alignment of microbial datasets, reducing memory and runtime while maintaining accuracy.

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

Bakdrive identifies minimum sets of bacterial driver species in microbial communities using metagenomic data and control theory, showing potential to restore gut microbiomes in disease states.

Emu provides species-level microbial community profiling using full-length 16S rRNA reads with improved accuracy over existing methods.

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

This study benchmarks statistical multiple sequence alignment methods, finding BAli-Phy more accurate on simulated data but less accurate on biological benchmarks.