Biology subjects

Quenu, M.

Publications and source records attributed to Quenu, M..

2 recordsLinked to original sources

bioRxiv2025

De novo assembly of complete Plasmodium falciparum isolate genomes using PacBio HiFi sequencing technology

Plasmodium falciparum possesses a highly structured genome with extensive sequence diversity concentrated in Variant Surface Antigen (VSA) families. These genes--var, rif, and stevor--play key roles in immune evasion and pathogenesis and are difficult to assemble using short-read sequencing technologies. Here, we applied PacBio HiFi long-read sequencing to generate high-quality de novo genome assemblies from 43 P. falciparum parasite cultures originating from community cases in The Gambia. Parasites were culture-adapted, cloned by limiting dilution where possible, and sequenced using high molecular weight DNA extracts. Assemblies from single-genotype lineages were constructed using hifiasm, producing complete chromosomal-length scaffolds with high base accuracy without requiring short-read polishing. We recovered full repertoires of var, rif, and stevor genes and classified them into known subgroups. Together, our results demonstrate that PacBio HiFi sequencing enables accurate assembly of complex P. falciparum genomes from natural infections. This work provides a valuable genomic resource for future studies of parasite evolution, transmission dynamics, and antigenic diversity, and suggests that VSA repertoires can serve as reliable proxies of genetic relatedness across infections.

genomics↗

bioRxiv2025

upsAI: A high-accuracy machine learning classifier for predicting Plasmodium falciparum var gene upstream groups

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the hypervariable var gene family, is central to malaria pathogenesis, influencing both disease severity and immune evasion. Classifying var genes into upstream groups (upsA, upsB, upsC, upsE) is important for understanding parasite biology and clinical outcomes, but remains challenging, especially with partial sequences, such as the DBL tag or RNA-Seq assemblies. We developed upsAI, a machine learning-based classifier trained on 2,530 curated var genes, to accurately assign upstream groups using sequence features from different partial gene regions. We compared seven different methods, including support vector machines, random forest, XGB boost and HMMer models. The best model of upsAI for DBL-tags sequences achieves an overall accuracy of 83%, 92% and for full-length var genes, therefore significantly outperforming existing tools. Further, we propose a new model to distinguish between internal and subtelomeric var genes with high accuracy and scalability. upsAI is available at https://github.com/sii-scRNA-Seq/upsAI, providing a robust and efficient resource for large-scale var gene analysis. It can classify var genes from 20 genomes in under one second.

bioinformatics↗