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

Noa Ecker, Dana Azouri, Ben Bettisworth, Alexandros Stamatakis, Yishay Mansour, Itay Mayrose*, Tal Pupko*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)I118-I124
StatePublished - 1 Jul 2022


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