Evolution and Genetic Dynamics

This study examines how different fitness statistics in high-throughput microbial experiments affect interpretations of mutant fitness and evolutionary dynamics.

The paper introduces kGain, a metric quantifying k-mer frequency shifts due to mutations, and shows it predicts adaptive potential across species and experimental systems.

The paper uses Neanderthal ancestry patterns to identify genomic regions enriched with recessive deleterious mutations using a machine learning classifier.

This study investigates how intergenomic epistasis affects endpoint repeatability in coevolving species, finding high repeatability within landscapes but varying fitness outcomes across landscapes.

The paper tracks parallel evolution of full-length genomes in bacteriophage populations over 412 generations, showing non-neutral evolutionary dynamics and biases in phylodynamic analysis.

The paper infers a multi-host fitness landscape of endive necrotic mosaic virus using cross-inoculation experiments and a Bayesian model linked to Fisher's geometrical model.

The paper introduces a numerical method to accurately model random genetic drift with or without natural selection and mutation, showing improved accuracy over standard methods.

The paper quantifies the fitness gain of DGR diversification compared to standard mutagenesis and identifies when DGR activation is evolutionarily favored.

The paper models introgression of a small genomic block with weakly selected loci into a population, showing survival depends on selection strength relative to recombination.