Sadiq, H. | Search bioRxiv

bioRxiv2025

scoup: Simulate Codon Sequences with Darwinian Selection Incorporated as an Ornstein-Uhlenbeck Process

Genetic analyses of natural selection within and between populations have increasingly developed along separate paths. The two important genres of evolutionary biology (i.e. phylogenetics and population genetics) borne from the split can only benefit from research that seeks to bridge the gap. Simulation algorithms that combine fundamental concepts from both genres are important to achieve such unifying objective. We introduce scoup, a codon sequence simulator that is implemented in R and hosted on the Bioconductor platform. There is hardly any other simulator dedicated to genetic sequence generation for natural selection analyses on the platform. Concepts from the Halpern-Bruno mutation-selection model and the Ornstein-Uhlenbeck (OU) evolutionary algorithm were creatively fused such that the end-product is a novelty with respect to computational genetic simulation. Users are able to seamlessly adjust the model parameters to mimic complex evolutionary procedures that may have been otherwise infeasible. For example, it is possible to explicitly interrogate the concepts of static and changing fitness landscapes with regards to Darwinian natural selection in the context of codon sequences from multiple populations.

evolutionary biology↗

bioRxiv2025

difFUBAR: Scalable Bayesian comparison of adaptive evolution

While many phylogenetic methods exist to characterize evolutionary pressure at individual codon sites, relatively few allow direct comparison between different a priori selected sets of branches. Such comparisons may be useful for pinpointing precisely the codon sites that are under differing selective pressures due to differing environmental contexts, or differing genomic contexts via epistatic interactions. Indeed, this was only recently addressed by an approach, developed in the frequentist framework, that proposes a site-wise likelihood ratio hypothesis test. Previously, we have demonstrated that approximate grid-based Bayesian approaches to characterizing site-wise variation in selection parameters can outperform individual site-wise likelihood ratio tests. Such grid-based approaches can exhibit poor computational scaling when the number of site-wise parameters expands, but here we show that this is still tractable up to four parameters, and that a simple subtree-likelihood caching strategy can provide efficiency improvements in some cases. We propose difFUBAR, which allows the demarcation of two branch sets of interest and, optionally, a background set, and estimates joint site-specific posterior distributions over , {omega}1,{omega} 2, and{omega} BG using a Gibbs sampler. Evidence for hypotheses of interest can then be quantified directly from the posterior distribution, and we standardly report P ({omega}1 > {omega}2), P ({omega}2 > {omega}1), P ({omega}1 > 1), and P ({omega}2 > 1). We characterize the computational and statistical performance of this approach on previous simulations, comparing it to the site-wise likelihood ratio test approach, where it shows moderate statistical benefits, and substantial computational gains, typically being more than two orders of magnitude faster on the same datasets. We also demonstrate that it can scale to datasets of over ten thousand taxa, on a laptop in under ten minutes. difFUBAR is implemented in MolecularEvolution.jl - a Julia framework for phylogenetic model development - and can be run locally, or online via a Colab notebook.

bioinformatics↗