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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Fusang: a framework for phylogenetic tree inference via deep learning.

Zhicheng Wang^1,2, Jinnan Sun³, Yuan Gao¹

¹Chinese Institute for Brain Research, Beijing 102206, China.

Nucleic Acids Research

|October 11, 2023

Summary

A new deep learning (DL) framework, Fusang, offers a faster alternative for phylogenetic tree inference, matching traditional methods. With further optimization, Fusang shows potential to surpass existing tools for evolutionary biology research.

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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

Evolutionary Biology
Bioinformatics
Computational Biology

Background:

Phylogenetic tree inference is crucial for understanding evolutionary relationships, traditionally relying on Maximum Likelihood (ML) and Bayesian Inference (BI) methods.
Bayesian Inference (BI) is computationally intensive and too slow for large datasets, limiting its practical application in evolutionary biology.
Deep Learning (DL) has shown promise in phylogenetic inference, but practical, scalable tools for real-world applications are lacking.

Purpose of the Study:

To introduce Fusang, a novel deep learning-based framework for phylogenetic tree inference.
To evaluate Fusang's performance against established methods like ML using both simulated and real biological datasets.
To highlight the potential of Fusang to overcome the computational limitations of traditional phylogenetic inference tools.

Main Methods:

Development of Fusang, a deep learning framework specifically designed for phylogenetic tree inference.
Comparative analysis of Fusang's performance against Maximum Likelihood (ML) methods.
Validation using both simulated datasets and real-world biological sequence data.

Main Results:

Fusang demonstrates performance comparable to existing Maximum Likelihood (ML)-based phylogenetic tree inference tools.
The framework was tested on both simulated and authentic biological datasets, confirming its applicability.
Fusang shows potential for further improvement and outperforming ML tools with ongoing optimization and customized training.

Conclusions:

Fusang presents a viable deep learning alternative for phylogenetic tree inference, addressing the speed limitations of traditional methods.
The framework achieves competitive accuracy, making it suitable for analyzing large-scale evolutionary datasets.
Continuous development and tailored training datasets position Fusang as a promising tool for advancing evolutionary biology research.