Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors

Area of Science:

• Evolutionary biology
• Computational biology
• Genomics

Background:

• Branch-specific substitution models are key for identifying evolutionary shifts.
• Current methods require predefined change-point locations or struggle with large datasets.

Purpose of the Study:

• To develop a method for automatic change-point detection without prior knowledge.
• To enhance the scalability and efficiency of evolutionary model inference.

Main Methods:

• Integration of branch-specific substitution models with shrinkage priors.
• Development of an analytical gradient algorithm for high-dimensional parameter estimation.
• Application to primate BRCA1 gene evolution and mpox viral sequences.

Main Results:

• Automatic identification of evolutionary change-points.
• Significant computational speedups: up to 90x in maximum-likelihood optimization and 360x in Bayesian inference.
• Efficient estimation of distinct substitution parameters per branch.

Conclusions:

• The novel algorithm overcomes limitations of existing methods for detecting evolutionary change-points.
• Enhanced inference efficiency and computational performance for complex evolutionary analyses.
• Provides a powerful tool for studying selection pressure and mutational dynamics in diverse organisms.