Stéphane Aris‐Brosou

This paper develops an unsupervised pipeline to predict viral fitness using sequence data and finds that protein alignment, not model size, is key for accurate prediction.

The paper develops a label-free framework to detect antimicrobial resistance determinants using evolutionary signatures and protein-language models, finding known determinants are mostly accessory in E. coli but enriched in K. pneumoniae.

The paper examines how glacial meltwater affects the diversification of metal resistance genes in Arctic soil microbiomes across different runoff regimes.

The paper compares divergence of terrestrial and marine DNA viromes from reference databases, finding terrestrial viromes more divergent, especially in polar regions.

The paper analyzes SARS-CoV-2 spike evolution in Canada, finding that variant emergence, not vaccine deployment, drove episodic positive selection at the provincial level.

dna-parser encodes DNA and RNA sequences into numerical features efficiently using Rust and Python, offering fast and parallel processing.

This study shows that positive selection on influenza A surface antigens has increased over time, driven by immune escape and surveillance, not climate factors.

The paper analyzes viral diversity in Lake Hazen, the largest High Arctic lake, using metagenomic and metatranscriptomic data to show unique evolutionary processes and high genetic diversity in High Arctic viruses.

The paper identifies that both epistasis and physical linkage can drive correlated evolution detected computationally under antibiotic selection in Pseudomonas aeruginosa.

This paper evaluates machine learning algorithms for identifying genetic determinants in small sample size, large proteome datasets, showing improved performance with a modified random forest approach.