Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty | JoVE Visualize

Area of Science:

Computational Biology
Phylogenetics
Machine Learning in Bioinformatics

Background:

Phylogenetic inference using maximum likelihood relies on heuristic tree searches that can be computationally intensive.
Dataset difficulty, related to the number and distinctness of optimal tree topologies, impacts search efficiency and convergence.
Machine learning methods can predict phylogenetic dataset difficulty, offering potential for optimizing inference strategies.

Purpose of the Study:

To develop and implement an adaptive tree search heuristic in RAxML-NG that adjusts search thoroughness based on predicted phylogenetic dataset difficulty.
To improve the computational efficiency of phylogenetic tree inference by tailoring search strategies to dataset characteristics.
To evaluate the performance and accuracy of the adaptive heuristic across a large number of empirical and simulated datasets.

Main Methods:

Implemented an adaptive tree search heuristic in RAxML-NG, modifying search intensity based on predicted dataset difficulty.
Utilized machine learning predictions of dataset difficulty to guide the adaptive search strategy.
Tested the adaptive heuristic on 9,515 empirical and 5,000 simulated multiple sequence alignments (MSAs) with varying difficulty levels.

Main Results:

The adaptive heuristic achieved substantial speedups, particularly on easy and difficult datasets (53% of MSAs), with average speedups exceeding 10×.
Approximately 94% of trees inferred using the adaptive strategy were statistically indistinguishable from those obtained with the standard RAxML-NG strategy.
The adaptive strategy effectively leverages dataset difficulty predictions to optimize tree search efficiency without compromising topological accuracy.

Conclusions:

The adaptive tree search heuristic offers a significant improvement in computational efficiency for phylogenetic inference, especially for datasets with extreme difficulty.
This approach provides a practical method to accelerate phylogenetic analyses by intelligently allocating computational resources based on data properties.
The adaptive strategy demonstrates the value of integrating machine learning-based difficulty prediction into heuristic search algorithms for large-scale phylogenomic studies.