A LASSO-based approach to sample sites for phylogenetic tree search

Abstract

Motivation In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale alignments are challenging for likelihood-based phylogenetic inference programs and usually require using a powerful computer cluster. Current tools for alignment trimming prior to phylogenetic analysis do not promise a significant reduction in the alignment size and are claimed to have a negative effect on the accuracy of the obtained tree. Results Here, we propose an artificial-intelligence-based approach, which provides means to select the optimal subset of sites and a formula by which one can compute the log-likelihood of the entire data based on this subset. Our approach is based on training a regularized Lasso-regression model that optimizes the log-likelihood prediction accuracy while putting a constraint on the number of sites used for the approximation. We show that computing the likelihood based on 5% of the sites already provides accurate approximation of the tree likelihood based on the entire data. Furthermore, we show that using this Lasso-based approximation during a tree search decreased running-time substantially while retaining the same tree-search performance. Availability and implementation The code was implemented in Python version 3.8 and is available through GitHub (https://github.com/noaeker/lasso_positions_sampling). The datasets used in this paper were retrieved from Zhou et al. (2018) as described in section 3. Supplementary information Supplementary data are available at Bioinformatics online.

The Bayesian Phylogenetic Bootstrap, Application to Short Trees and Branches

“…For this type of data with low homoplasy, bootstrap supports are strongly correlated with branch lengths (Felsenstein 1985). This confirms recent results on the predictability of branch supports by machine learning (Wiegert et al 2024; Ecker et al 2024). However, the usefulness of the bootstrap remains, especially for short branches corresponding to 1 or 2 mutations, where the signal can be conflicting and blurred by the homoplasy of the data, even if it is low (Fig.…”

A machine-learning-based alternative to phylogenetic bootstrap

“…Recently, machine-learning algorithms were successfully applied in phylogenetic research, contributing to both runtime efficiency and enhanced inference accuracy. Noteworthy applications include their utilization in model selection tasks ( Abadi et al 2020 , Burgstaller-Muehlbacher et al 2023 ), inferring phylogenetic trees ( Suvorov et al 2020 ), ranking candidate trees during a tree-search ( Azouri et al 2021 ), identification of key genomic loci for elucidating a phylogenetic hypothesis ( Kumar and Sharma 2021 ), sampling of MSA positions to reduce tree-search running time ( Ecker et al 2022 ), and estimating the difficulty of the MSA ( Haag et al 2022 ). In this study, we have demonstrated the effectiveness of machine-learning algorithms for branch support estimation, a task traditionally relying on standard statistical tests.…”