Xavier Didelot

Jul 11, 2024 | science.org | Michelle Kendall |Luca Ferretti |Chris Wymant |James Petrie |Francesco Di Lauro |Lucie Abeler-Dörner | +7 more

Understanding the drivers of respiratory pathogen spread is challenging, particularly in a timely manner during an ongoing epidemic. In this work, we present insights that we obtained using daily data from the National Health Service COVID-19 app for ...

May 10, 2024 | nature.com | Yu Wan |Ho Kwong Li |Xavier Didelot |Nicholas J Croucher |Elita Jauneikaite |Colin Brown | +1 more

AbstractThe UK observed a marked increase in scarlet fever and invasive group A streptococcal infection in 2022 with severe outcomes in children and similar trends worldwide. Here we report lineage M1UK to be the dominant source of invasive infections in this upsurge. Compared with ancestral M1global strains, invasive M1UK strains exhibit reduced genomic diversity and fewer mutations in two-component regulator genes covRS. The emergence of M1UK is dated to 2008.

Mar 9, 2024 | biorxiv.org | Xavier Didelot |Paolo Ribeca

AbstractThe recent explosion in the amount of available sequencing data challenges existing analysis techniques. Here we introduce KPop, a novel versatile method based on full k-mer spectra and dataset-specific transformations, through which thousands of assembled or unassembled microbial genomes can be quickly compared. Unlike minimizer-based methods that produce distances and have lower resolution, KPop is able to accurately map sequences onto a low-dimensional space.

Mar 9, 2024 | biorxiv.org | Xavier Didelot |Paolo Ribeca

AbstractThe recent explosion in the amount of available sequencing data challenges existing analysis techniques. Here we introduce KPop, a novel versatile method based on full k-mer spectra and dataset-specific transformations, through which thousands of assembled or unassembled microbial genomes can be quickly compared. Unlike minimizer-based methods that produce distances and have lower resolution, KPop is able to accurately map sequences onto a low-dimensional space.