Evolution and Genetic Dynamics

The paper investigates how evolutionary tradeoffs between multiple tasks shape the genetic polymorphism structure in populations, finding that prevalent polymorphisms align with the Pareto front of phenotypic tradeoffs.

This study measures the proliferation rates of yeast gene deletion mutants under periodic salinity stress, identifying genes that influence growth in fluctuating conditions.

The paper shows that multiple beneficial mutations in HIV lead to wavelet-like oscillations in multilocus linkage disequilibrium, which can indicate mutation interference.

The paper introduces NCD2, a statistic to detect long-term balancing selection in human genomes, finding it affects 0.6% of genomic windows and 0.8% of positions, with many novel targets.

The paper investigates how a genetic barrier to gene flow can strengthen or weaken in parapatric speciation through the accumulation of Dobzhansky-Muller incompatibilities.

Bacmeta simulates genomic evolution in bacterial metapopulations with adjustable connectivity and stochastic events, enabling large-scale simulations and likelihood-free inference.

The paper estimates the mutation rate and evolutionary relevance in a colonizing plant lineage using historical and modern genomic data.

This paper computes the third moments of the site frequency spectrum and derives analytical results for the bias of neutrality tests.

The paper introduces SD plots to identify selective sweeps in influenza A virus genomes, showing their use in tracking adaptive changes in pH1N1 and sH3N2 viruses.

The paper addresses discrepancies in heritability estimates of HIV-virulence by introducing new methods and showing that at least 25% of variation is explained by the virus genome.

The paper shows that quiescence in fission yeast leads to mutation accumulation equivalent to one DNA replication round, revealing a new evolutionary force.

The paper investigates how clonal heterogeneity and genetic variation influence antimicrobial resistance in yeast, showing that both pre-existing and de novo mutations contribute to fitness and adaptation.

The paper examines the frequencies of recessive lethal mutations in human populations and finds they are higher than expected, suggesting ascertainment bias rather than mutation-selection balance.

The paper models how mitochondrial genomes avoid mutational erosion through uniparental inheritance and fusion-fission cycles, showing that recombination is not necessary for this process.

The paper assesses how mutations in the TDH3 promoter of Saccharomyces cerevisiae affect cell growth and predicts fitness consequences of these variants.

This study shows that the order of antibiotic exposure affects bacterial resistance development, with past drug regimens influencing future resistance profiles.

The paper shows that false viral sequence clusters can lead to incorrect phylodynamic parameter estimates, such as underestimating population size and overestimating growth rates.

The paper estimates the mutation rate in Atlantic herring and explains its moderate nucleotide diversity despite large population size.

This study examines mutations in E. coli during prolonged stationary-phase, finding increased genetic diversity and evidence of positive selection on regulatory mutations.

The paper quantifies fitness landscapes and selection on phenotypic states using single-cell genealogical data from bacterial populations under antibiotic stress.

The paper shows that accounting for genetic interactions improves modeling of quantitative trait phenotypes in yeast by considering epistatic networks and allele frequencies.

The paper uses a Potts Hamiltonian model to infer epistatic effects in HIV-1 protease mutations, showing how these interactions influence drug resistance and fitness.

The paper measures homoplasies in mitochondrial proteins across metazoan species and finds that parallel substitutions occur at shorter phylogenetic distances than divergent substitutions.

The paper estimates nonlinear scales in genotype-phenotype maps and finds significant high-order epistasis even after accounting for nonlinearity.

This paper introduces GEMME, a method for predicting mutational effects using sequence data and enhances it with structural and allele frequency data to improve biological insights.