A major goal in evolutionary biology is to understand the forces that operate in the genomic
sequences and are responsible for the adaptation of species to different environments. Codon
models are one of the main tools used to infer selection on protein-coding genes. These have been
popularized in comparative genomic studies by their extensive use in genome-wide scans of
diversifying selection. However, models of codon evolution have significant limitations that are
increasingly being recognized. The main one being that current codon models make simplistic
assumptions, such as ignoring species demography and nucleotide usage bias. This project offers a
new polymorphism-aware model, PoMo-cod, to detect signatures of natural selection acting on
protein-coding sequences. PoMo-cod will address codon evolution in a unique way by properly
reconciling the neutral and adaptive population-level processes by which coding sequences evolve.
More importantly, PoMo-cod will allow us to tell apart the sole action of natural selection from
known confounding forces (e.g., fluctuating demography and GC-biased gene conversion),
ultimately producing more accurate genome-wide maps of diversifying evolving genes. Selection
maps have impactful applications in other fields. They permit developing species-specific
conservation strategies to mitigate the anthropogenic action on biodiversity or characterize the
genome functionally, which has direct implications for medical research.