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Updated: Jun 23, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.

Daniele Silvestro^1,2, Thibault Latrille³, Nicolas Salamin³

¹Department of Biology, University of Fribourg and Swiss Institute of Bioinformatics, 1700 Fribourg, Switzerland.

Systematic Biology

|June 25, 2024

Summary

This summary is machine-generated.

Deep learning models accurately infer molecular evolution parameters and evolutionary rates directly from sequence alignments. This approach surpasses traditional methods for complex evolutionary scenarios, improving phylogenetic tree accuracy.

Keywords:

Molecular evolution phylogenetic inference recurrent neural networks simulations substitution rates

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Published on: December 7, 2021

Area of Science:

Computational Biology
Evolutionary Biology
Machine Learning

Background:

Phylogenetic tree reconstruction relies on molecular evolution models.
Traditional models struggle with complex evolutionary scenarios and large datasets, necessitating simplifying assumptions.
Maximum likelihood and Bayesian inference are common parameter estimation methods.

Purpose of the Study:

To develop a deep learning model for inferring molecular evolution parameters directly from sequence data.
To estimate per-site evolutionary rates and divergence without a predefined phylogenetic tree.
To improve the accuracy and scalability of phylogenetic inference, especially under complex evolutionary models.

Main Methods:

Coupling stochastic simulations of genome evolution with a supervised deep learning model.
Direct analysis of multiple sequence alignments to estimate per-site evolutionary rates.
Integration of deep learning-derived rates into a Bayesian phylogenetic framework.

Main Results:

Deep learning model predictions matched likelihood-based inference for simple rate heterogeneity (gamma distribution).
Performance significantly exceeded traditional methods for complex rate variation (e.g., codon models).
Scalable application to large genomic datasets demonstrated on 26 million nucleotides.
Integration of deep learning rates improved phylogenetic inference accuracy, particularly branch lengths.

Conclusions:

Deep learning offers a powerful, scalable approach to infer molecular evolution parameters.
This method overcomes limitations of traditional models in complex evolutionary scenarios.
A semi-supervised learning approach combining deep learning and probabilistic inference promises future advancements in phylogenetics.