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

Bioinformatics. 2022 Jun 24;38(Suppl 1):i118-i124. doi: 10.1093/bioinformatics/btac252.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence*
  • Likelihood Functions
  • Phylogeny
  • Software*