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

Xiang Ji1, Benjamin Redelings1, Shuo Su2

  • 1Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA.

Research Square
|July 18, 2025
PubMed
Summary

This study introduces a new method to automatically detect evolutionary change-points using branch-specific models and shrinkage priors. This approach improves efficiency and accuracy in evolutionary analyses without needing prior knowledge of change-point locations.

Keywords:
Bayesian inferencebranch-specific substitution modellinear-time gradient algorithmmaximum likelihoodnatural selection

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genomics

Background:

  • Branch-specific substitution models are key for identifying evolutionary shifts, but often require predefined change-point locations or struggle with large datasets.
  • Existing methods face limitations in scalability and the need for prior knowledge, hindering broad application.

Purpose of the Study:

  • To develop a novel computational framework that integrates branch-specific substitution models with shrinkage priors.
  • To enable automatic detection of evolutionary change-points without prior location knowledge.
  • To enhance the efficiency and scalability of evolutionary inference.

Main Methods:

  • Integration of branch-specific substitution models with shrinkage priors for automatic change-point identification.
  • Development of an analytical gradient algorithm for efficient estimation of branch-specific substitution parameters.
  • Application to infer evolutionary dynamics in the BRCA1 gene and mpox virus sequences.

Main Results:

  • The new method automatically identifies change-points and estimates distinct substitution parameters per branch.
  • The analytical gradient algorithm achieves linear computation time with respect to the number of parameters.
  • Significant computational speedups were observed: up to 90-fold in maximum-likelihood optimization and 360-fold in Bayesian inference.

Conclusions:

  • This novel algorithm overcomes limitations of existing methods, enabling efficient and accurate evolutionary inference.
  • The approach facilitates the study of selection pressure and mutational dynamics in diverse biological systems.
  • The developed computational tools significantly advance the field of evolutionary genomics.