Since Darwin, natural selection has been recognized as one of major biological forcesshaping genetic patterns in molecular data. Detecting selection on proteins hasbecome an indispensible part of genome studies. Remarkably selection can act notonly on proteins, but also on synonymous codons translating into the same aminoacid. This manifests itself as codon bias, with no influence on the protein sequence,but with potentially strong impact on the protein product and associated cellularprocesses. In addition, mechanisms such as biased gene conversion may result in anexcess of synonymous changes with mild deleterious effect. The role of selection onsynonymous changes is often studied by measuring codon usage on the entire gene.This approach however lacks power: it ignores evolutionary information and the impactof site-specific synonymous rate variation, found in >1/3 of proteins. For instance, theuse of rare codons at certain sites may slow down translation producing a ribosomalpause for ubiquitin modification or for co-translational protein folding. Codon choice atsuch sites may affect protein synthesis or product’s properties. Synonymous changesat sites of miRNA or siRNA binding may have impact on protein abundance in aprocess known as RNA interference (RNAi). Recently single synonymous mutationshave been shown to contribute to human diseases such as cancers and diabetes. Suchsites often use rare codons or exhibit high synonymous variability.Here we focus on site-specific synonymous codon bias due to selection orbiased gene conversion. We develop statistical methods to identify candidatesites in genome-wide scans of species orthologs. A deeper insight into evolutionarydynamics at synonymous sites will come from contrasting fixed differences betweenspecies and polymorphisms within populations. To test predictions of the neutraltheory about macro- and microevolutionary forces acting on genomes, we develop astatistical framework for analyzing mixed population/species data, thereby bridgingthe existing methodological gap between molecular evolution and population geneticsmodels.