Austin Daigle

The paper introduces Bvalcalc, a tool to calculate B-values, which quantify the effect of background selection on genetic diversity, and validates it using simulations and public data.

The paper estimates the distribution of fitness effects of new mutations in regulatory regions of D. melanogaster genomes and finds they contribute more deleterious and beneficial mutations than coding regions.

The paper identifies key components for building a baseline evolutionary model of Plasmodium falciparum to better understand genomic variation and drug resistance.

This study introduces TEforest, a machine learning method that improves short-read TE detection and genotyping by leveraging long-read assemblies, achieving better accuracy than traditional methods.

This study investigates the relationship between meiotic double-strand breaks (DSBs) and mutation patterns in Drosophila melanogaster by comparing mutation accumulation in strains with and without DSBs.

This study examines transposable element insertions in Aedes aegypti genomes to assess their contribution to genetic diversity and potential role in insecticide resistance.

GHIST 2024 evaluates population genetic inference methods through four demographic history challenges, revealing the dominance of site frequency spectrum-based methods and the potential of flexible models for complex histories.

This study shows that Hill-Robertson interference in highly selfing species biases the inference of mutation fitness effects, leading to misestimation of selection coefficients and adaptive substitution rates.