Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

OmegaSwitch: Bayesian Markov-Modulated Codon Models for Estimating dN/dS

Created on 20 Aug 2026

Authors

DeMontigny, W. C., Delwiche, C. F.

Abstract

Selective pressures can vary across both sites and evolutionary lineages; however, most codon models accommodate heterogeneity along only one of these dimensions and require the number of selective regimes to be specified in advance. Here, we introduce OmegaSwitch, a Bayesian phylogenetic software framework for inferring changes in the nonsynonymous-to-synonymous substitution-rate ratio (dN/dS) across sites and through evolutionary time. We implement a Markov-modulated codon model in which lineages transition among discrete dN/dS regimes and use reversible-jump Markov chain Monte Carlo to infer the number of regimes simultaneously. We further develop a Dirichlet-process mixture extension that allows the parameters governing these time-heterogeneous processes to vary among sites. Ancestral sampling produces joint posterior distributions of dN/dS across sites and nodes of the phylogeny, enabling lineage- and site-specific summaries with quantified uncertainty. Simulation analyses showed that both the posterior intervals for dN/dS and the number of evolutionary regimes were well calibrated under both models. We demonstrate OmegaSwitch using vertebrate alpha- and beta-globins. OmegaSwitch therefore provides a flexible Bayesian framework for investigating how selective pressures vary across protein-coding sequences and phylogenetic history.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 20 Aug 2026.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 14
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement