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Updated: Sep 21, 2025

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Jump-Chain Simulation of Markov Substitution Processes Over Phylogenies.

Simon Laurin-Lemay¹, Kassandra Dickson¹, Nicolas Rodrigue^2,3,4

¹Department of Biology, Carleton University, 209 Nesbitt Biology Building, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada.

Journal of Molecular Evolution

|June 2, 2022

Summary

We introduce jump-chain simulation for creating phylogenetic data. This method handles complex molecular evolution models and reveals how CpG hypermutability can mislead positive selection detection, improving inference model understanding.

Keywords:

Approximate Bayesian Computation CpG hypermutability Likelihood ratio test Model violations Positive selection Site-interdependent models Substitution models

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

Area of Science:

Computational Biology
Molecular Evolution
Phylogenetics

Background:

Phylogenetic analyses rely on accurate models of molecular evolution.
Current simulation methods may struggle with complex evolutionary processes.
Detecting positive selection is crucial for understanding evolutionary adaptation.

Purpose of the Study:

Introduce and validate the jump-chain simulation method for phylogenetic data generation.
Demonstrate the utility of jump-chain simulation in exploring model misspecification.
Highlight the method's potential for improving evolutionary inference.

Main Methods:

Developed the jump-chain simulation approach for generating artificial phylogenetic data.
Simulated data under a context-dependent model incorporating CpG hypermutability.
Evaluated the impact of simulated data on standard codon models for positive selection detection.
Explored the application of jump-chain simulation within the Approximate Bayesian Computation framework.

Main Results:

Jump-chain simulation effectively generates data under complex molecular evolution models with intractable likelihoods.
CpG hypermutability, when simulated, can significantly mislead common codon models used for detecting positive selection.
The method illustrates the susceptibility of current inference models to violations of their assumptions.
Jump-chain simulation shows promise as an inference engine for Approximate Bayesian Computation.

Conclusions:

Jump-chain simulation is a powerful, under-appreciated tool for generating complex phylogenetic data.
Understanding model misspecification through simulation is vital for accurate evolutionary inference.
This method enhances the robustness of phylogenetic analyses and aids in developing more reliable evolutionary models.