Andrew J. Page

The paper identifies non-coli Escherichia species, including clade I ST485, that carry EAEC virulence genes and are often misidentified as E. coli, highlighting their potential role in diarrheal disease.

This study analyzes SARS-CoV-2 genomic data from Norfolk, UK, revealing high diversity and dynamic emergence of VOCs over 2.5 years.

hAMRonization standardizes AMR gene detection outputs from multiple tools into a unified format for easier analysis and integration.

uvaia improves phylogenetic analysis of SARS-CoV-2 genomes by efficiently finding similar sequences using partial and exact similarity measures.

This study characterizes neonatal Staphylococcus capitis NRCS-A isolates and compares them with non-NRCS-A isolates from neonates and adults, identifying genetic and phenotypic traits associated with their success in NICUs.

This study conducts genomic surveillance of SARS-CoV-2 in vaccinated healthcare workers in Lebanon, identifying Omicron as the dominant variant during the study period.

LoRTIS-SS enables transposon insertion site mapping using long-read sequencing, improving accuracy in repeated genomic regions compared to short-read methods.

tatajuba identifies homopolymer tracts in bacterial genomes and highlights polymorphic variants across samples, confirming and expanding on previous findings.

The paper analyzes whole genome sequences of invasive Salmonella isolates in The Gambia to study changes in serovar distribution and virulence factors over time.

This study uses genomic data from 8746 SARS-CoV-2 genomes across 33 African countries to show that most epidemics were initiated by European importations and later driven by local transmission and mobility, leading to the spread of variants of concern.

The paper introduces Pathogenwatch, a tool for genomic surveillance of Salmonella Typhi, enabling AMR prediction and population-level risk assessment through global data sharing.

The paper generates high-quality reference genomes for seven major ETEC lineages using long-read sequencing, revealing conserved virulence plasmids crucial for pathogenesis.

AlbaTraDIS analyzes transposon mutagenesis data to identify genes involved in stress survival, demonstrating its utility in identifying Triclosan resistance genes in E. coli.

CoronaHiT is a method for high-throughput SARS-CoV-2 genome sequencing that improves coverage and throughput compared to existing protocols.

Galru rapidly predicts Mycobacterium tuberculosis spoligotypes from uncorrected long reads with performance comparable to short read methods.

The paper examines how long-read sequencing captures full-length ribosomal RNA operons in bacteria, noting that intra-species variation may limit taxonomic resolution to the genus level for uncultured organisms.

Socru identifies genome order and orientation between ribosomal operons in bacteria using long-read sequencing, supporting 434 species and detecting misassemblies.

The paper reports the discovery of a new bacterial species, Candidatus Ornithobacterium hominis, identified from nasopharyngeal swabs and describes its genomic features and colonization patterns.

Krocus predicts bacterial sequence types directly from uncorrected long reads, providing results in minutes with high accuracy.

SeroBA predicts Streptococcus pneumoniae serotypes from whole genome sequencing data with high concordance and scalability.

PlasmidTron identifies mobile genetic elements linked to phenotypes by analyzing short read WGS data and phenotypic metadata from bacterial populations.

ARIBA rapidly identifies antimicrobial resistance genes and SNPs from sequencing reads, outperforming existing tools on bacterial datasets.

This study compares six MLST software tools using real and simulated data, evaluating their accuracy, performance on outbreak datasets, contamination impact, and computational requirements.

This paper introduces a high-throughput pipeline for bacterial genome assembly and improvement, demonstrating robust performance across diverse datasets and achieving high gene completeness.

SNP-sites efficiently extracts SNPs from large multi-FASTA alignments using minimal resources, enabling scalable analysis on modest hardware.