Continuous-time Markov chain as a generic trait-based evolutionary model

TL;DR

This paper introduces a novel continuous-time Markov chain model for phylogenetic inference that leverages trait-based data and species relationships to improve the accuracy of evolutionary analysis.

Contribution

It presents a new trait-based CTMC model that calculates pairwise species distances using binary character paths, enabling more comprehensive phylogenetic inference.

Findings

Model allows site-wise phylogenetic inference.

Incorporates information from all species for pairwise distances.

Supports genome-wide phylogenetic analysis.

Abstract

More than ever, today we are left with the abundance of molecular data outpaced by the advancements of the phylogenomic methods. Especially in the case of presence of many genes over a set of species under the phylogeny question, more sophisticated methods than the crude way of concatenation is needed. In this letter, by placing the continuous-time Markov chain (CTMC) on the species set, I present a novel model for inferring the phylogeny, obtaining the network graph, or drawing the proximity conclusions. The rate of transition between states is calculated based on the binary character paths between each two species. This is the base for the pairwise distances between species. Next to its generic use, the formulation of the model allows the site-wise phylogenetic inference and a mathematically justified method of combining these information to form as big as the whole genome phylogenetic inference. Although based on the characters or traits, this model is inherently a distance method but its advantage to other methods of the same class is its ability to incorporate the information of all the other species in forming the pairwise distance between two them.