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A Practical Guide to Phylogenetics for Nonexperts

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Random-effects substitution models for phylogenetics via scalable gradient approximations.

Andrew F Magee, Andrew J Holbrook, Jonathan E Pekar

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces advanced random-effects substitution models for evolutionary inference, improving accuracy and computational efficiency. These models better capture complex evolutionary dynamics, as shown in SARS-CoV-2 and influenza virus analyses.

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Area of Science:

Evolutionary biology
Computational phylogenetics
Statistical modeling

Background:

Phylogenetic inference relies on accurate models of character evolution.
Existing models may not capture the full spectrum of substitution dynamics.
Complex evolutionary processes require more flexible modeling approaches.

Conclusions:

Random-effects substitution models offer a more robust framework for evolutionary inference.
The proposed gradient-based inference method enables scalable analysis of large datasets.
These models provide deeper insights into evolutionary processes and biological systems.