This study tracks the 28-year evolution of a Burkholderia pseudomallei strain in sterile water, identifying genetic changes and metabolic adaptations to nutrient-depletion.

bioRxiv · Feb 6, 2026 · PreprintEvolutionary Biology

A molecular barcode and online tool to identify and map imported infection with Plasmodium vivax

Hidayat Trimarsanto, Roberto Amato, Richard D. Pearson, Edwin Sutanto, Rintis Noviyanti, Leily Trianty, Jutta Marfurt, Zuleima Pava, Diego F. Echeverry, Tatiana M. Lopera-Mesa, Lidia Madeline Montenegro, Alberto Tobón-Castaño, Matthew J. Grigg, Bridget E. Barber, Timothy William, , Sarah Auburn

The paper develops a molecular barcode and online tool to identify and map imported Plasmodium vivax infections using SNP analysis and machine learning.

Communications Biology · Dec 23, 2022Peer-reviewedMolecular Biology

Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

James A Watson, Carolyne Ndila, Sophie M. Uyoga, Alexander W. Macharia, Gideon Nyutu, Shebe Mohammed, Caroline Ngetsa, Neema Mturi, Norbert M. Peshu, Benjamin Tsofa, Kirk A. Rockett, Stije J. Leopold, Hugh W. F. Kingston, Elizabeth C. George, Kathryn Maitland, , Nicholas John White

The paper improves statistical power in severe malaria genetic studies by addressing phenotypic imprecision through a probabilistic diagnostic model and data-tilting approach.

eLife · Jul 5, 2021Peer-reviewedGenetics

The mechanism of artemisinin resistance of Plasmodium falciparum malaria parasites originates in their initial transcriptional response.

Lei Zhu, Rob W. van der Pluijm, Michal Kucharski, Sourav Nayak, Jaishree Tripathi, François Henry Nosten, Abul Faiz, Chanaki Amaratunga, Dysoley Lek, Elizabeth A. Ashley, Frank M. Smithuis, Aung Pyae Phyo, Khin Lin, Mallika Imwong, Mayfong Mayxay, , Zbynek Bozdech

The paper identifies a transcriptional profile in artemisinin-resistant Plasmodium falciparum that originates from initial drug responses and involves proteotoxic stress and REDOX metabolism.

bioRxiv · May 17, 2021 · PreprintSystems Biology

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

James A Watson, Aimee R. Taylor, Elizabeth A. Ashley, Arjen M. Dondorp, Caroline O. Buckee, Nicholas John White, Chris C. Holmes

This paper warns against using unsupervised machine learning algorithms like PCoA and HAC to infer malaria parasite ancestry from genetic distance matrices, highlighting their limitations and providing guidance for more rigorous analysis.

PLoS Genetics · Oct 9, 2020Peer-reviewedGenetics