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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era.

Nhan Ly-Trong¹, Chris Bielow², Nicola De Maio³

¹School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia.

Molecular Biology and Evolution

|June 27, 2024

Summary

We developed CMAPLE, an optimized C++ tool and library for Maximum Parsimonious Likelihood Estimation (MAPLE), enhancing phylogenetic inference for genomic epidemiology. This improves large-scale pathogen analysis for pandemic preparedness.

Keywords:

epidemiology maximum likelihood models of sequence evolution phylogenetics phylogenomics

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Published on: February 5, 2014

Area of Science:

Genomic Epidemiology
Computational Biology
Phylogenetics

Background:

Phylogenetic inference is crucial for understanding pathogen evolution and transmission during pandemics.
Existing methods may face scalability challenges with large genomic datasets.
Maximum Parsimonious Likelihood Estimation (MAPLE) was previously introduced for pandemic-scale analysis.

Purpose of the Study:

To enhance the performance and scalability of the MAPLE method.
To introduce CMAPLE software, a C++ reimplementation of MAPLE.
To provide a CMAPLE library for integrating MAPLE into other phylogenetic tools.

Main Methods:

Developed CMAPLE software using C++ for optimized performance.
Created a CMAPLE library with APIs for broader integration.
Integrated CMAPLE into the IQ-TREE 2 software package.

Main Results:

CMAPLE offers significant performance and scalability improvements over the original MAPLE.
The CMAPLE library facilitates seamless integration into existing bioinformatics workflows.
Successful integration into IQ-TREE 2 enables wider community adoption.

Conclusions:

CMAPLE represents a significant advancement in phylogenetic inference for genomic epidemiology.
These tools enhance capabilities for large-scale pathogen genomic analysis.
The advancements contribute to improved preparedness for future pandemics.