Juraj Bergman

The paper curates a genome-scale nucleotide diversity panel of non-human primates using ploidy-aware variant calling and multiple-genome alignment.

Across 38 catarrhine subspecies, the population-scaled distribution of fitness effects (DFE) tracks effective population size while the unscaled deleterious DFE appears conserved, with no detectable epistasis.

The paper demonstrates parallelism between phylogeny and ontogeny using transcriptomics and phylostratigraphy, showing that later phylogenetically emerging genes are expressed later in ontogeny.

This study uses phylogenetic path analysis to examine how life history traits affect population dynamics metrics like effective population size and density across mammalian species.

The paper introduces oHMMed, a Hidden Markov Model for genomic segmentation, and applies it to analyze base composition, gene density, and chromatin features across multiple species.

The paper uses primate genetic data to predict human variant pathogenicity with a deep learning classifier trained on 4.3 million common missense variants.

This study presents whole-genome data from 233 primate species, revealing patterns of genetic diversity, mutation rates, and recurrent missense mutations across primate clades.