Biology subjects

Vilain, M.

Publications and source records attributed to Vilain, M..

3 recordsLinked to original sources

bioRxiv2026

Variant emergence, not vaccine deployment, drives episodic positive selection on the SARS-CoV-2 spike at provincial scale in Canada

Mass immunization against SARS-CoV-2 created a heterogeneous landscape of antibody-mediated immune pressure, yet whether this pressure measurably altered episodic positive selection on spike remains unresolved. Using Canadian genomic surveillance data spanning the five major variants of concern (Alpha, Beta, Gamma, Delta, and Omicron), we inferred time-resolved phylogenies from spike-coding sequences and applied site- and branch-level episodic selection models to identify when and where adaptive change occurred. To evaluate whether vaccination intensity was associated with selection, we integrated these phylogenetic analyses with provincial vaccination time series using cross-correlation and lagged panel regression models that accounted for province and time effects, lineage prevalence, and sampling heterogeneity. Episodic positive selection was concentrated at a limited number of spike codons, especially within the N-terminal domain, receptor-binding domain, and furin cleavage region. However, these signals were dominated by substitutions associated with variant emergence, particularly during the Alpha-to-Delta transition, rather than by vaccination rollout. Whole-gene tests provided no evidence that vaccine intensity was associated with elevated episodic selection, and residualized vaccination trajectories did not predict selection at biologically plausible lags. Across provinces, the timing and distribution of selection events were inconsistent with a vaccine-driven escape model. Together, these results indicate that, at provincial resolution in Canada, episodic positive selection on SARS-CoV-2 spike was driven primarily by variant turnover rather than vaccine deployment. More broadly, this study provides a quantitative, VOC-resolved assessment of spike evolution in a structured epidemic and suggests that population-level vaccination intensity was not a detectable determinant of spike adaptation in the period examined.

evolutionary biology↗

bioRxiv2026

dna-parser: a Python library written in Rust for fast encoding of DNA and RNA sequences

BackgroundThe ever-growing amount of available biological data leads modern analysis to be performed on large datasets. Unfortunately, bioinformatics tools for preprocessing and analyzing data are not always designed to treat such large amounts of data efficiently. Notably, this is the case when encoding DNA and RNA sequences into numerical representations, also called descriptors, before passing them to machine learning models. Furthermore, current Python tools available for this preprocessing step are not well suited to be integrated into pipelines resulting in slow encoding speeds. ResultsWe introduce dna-parser, a Python library written in Rust to encode DNA and RNA sequences into numerical features. The combination of Rust and Python allows to encode sequences rapidly and in parallel across multiple threads while maintaining compatibility with packages from the Python ecosystem. Moreover, this library implements many of the most widely used types of numerical feature schemes coming from bioinformaticss and natural language processing. Conclusiondna-parser is an easy to install Python library that offers many Python wheels for Linux (muslinux and manylinux), macOS, and Windows via pip (https://pypi.org/project/dna-parser/). The open source code is available on GitHub (https://github.com/Mvila035/dna_parser) along with the documentation (https://mvila035.github.io/dna_parser/documentation/).

bioinformatics↗

bioRxiv2025

Global warming drives the evolutionary rate of H1N1 and H3N2 influenza viruses

The haemagglutinin (HA) and neuraminidase (NA) genes of seasonal influenza A evolve under continual immune-driven positive selection. To test whether the tempo of selection has changed over time, we mapped branch- and site-specific episodic diversifying selection (MEME) onto Bayesian relaxed-clock time trees for HA and NA in H1N1 and H3N2, across multiple countries and four sequence-subsampling schemes. We dated each selection episode and tested whether episodes accumulated through time after accounting for the growing number of sampled lineages. Positive-selection episodes increased over time in every gene-subtype combination, at about 2-6% per lineage-year, and rose faster for NA than HA. Episodes were concentrated at a small number of codon sites, especially recurrent sites in H3N2 HA that fell within canonical antigenic regions of the HA1 head. This increase was robust to subsampling scheme and time-bin width, and was driven disproportionately by recent lineages. A detrended spatial analysis found no association with latitude or temperature anomalies. Overall, positive selection on influenza surface antigens appears to be intensifying through time, most likely because of immune escape and expanded surveillance rather than climate warming.

evolutionary biology↗