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LASSO-based approach to sample sites for phylogenetic tree search.
- Source :
- Bioinformatics; 2022 Supplement, Vol. 38, pi118-i124, 7p
- Publication Year :
- 2022
-
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%5fpositions%5fsampling). 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13674803
- Volume :
- 38
- Database :
- Complementary Index
- Journal :
- Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 157915951
- Full Text :
- https://doi.org/10.1093/bioinformatics/btac252