DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood

Area of Science:

• Evolutionary biology
• Computational biology
• Phylogenetics

Background:

• Understanding evolutionary processes relies on analyzing discrete characters.
• Existing computational tools for non-DNA discrete characters often lack flexibility and are analysis-specific.
• Discrete character data can present challenges such as variable transition rates and unobservable states.

Purpose of the Study:

• To develop a flexible and sophisticated program for estimating evolutionary rates of diverse discrete characters.
• To provide a unified R package accommodating various transition models and character states.

Main Methods:

• Maximum likelihood estimation of evolutionary rates for discrete characters on a phylogeny.
• Incorporation of options to correct for unobservable data, rate variation, and unknown root probabilities.
• Customization of instantaneous transition rate matrices or use of pre-defined models (BD, BDI, ER, SYM, GTR, ARD).

Main Results:

• DiscML performs maximum likelihood estimation for discrete character evolutionary rates.
• The program offers flexibility in handling unobservable data, rate variations, and prior root probabilities.
• Application examples demonstrate DiscML's utility on gene family and intron presence/absence data.

Conclusions:

• DiscML is a unified R program for estimating discrete character evolutionary rates, adaptable to any number of states.
• It offers flexibility with various transition models, suitable for binary, multi-gene family, and multistate morphological data.
• DiscML provides a robust tool for phylogenetic analyses of discrete characters.