Cargando…
A LASSO-based approach to sample sites for phylogenetic tree search
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 phyloge...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9236582/ https://www.ncbi.nlm.nih.gov/pubmed/35758778 http://dx.doi.org/10.1093/bioinformatics/btac252 |
_version_ | 1784736565945171968 |
---|---|
author | Ecker, Noa Azouri, Dana Bettisworth, Ben Stamatakis, Alexandros Mansour, Yishay Mayrose, Itay Pupko, Tal |
author_facet | Ecker, Noa Azouri, Dana Bettisworth, Ben Stamatakis, Alexandros Mansour, Yishay Mayrose, Itay Pupko, Tal |
author_sort | Ecker, Noa |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9236582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92365822022-06-29 A LASSO-based approach to sample sites for phylogenetic tree search Ecker, Noa Azouri, Dana Bettisworth, Ben Stamatakis, Alexandros Mansour, Yishay Mayrose, Itay Pupko, Tal Bioinformatics ISCB/Ismb 2022 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. Oxford University Press 2022-06-27 /pmc/articles/PMC9236582/ /pubmed/35758778 http://dx.doi.org/10.1093/bioinformatics/btac252 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | ISCB/Ismb 2022 Ecker, Noa Azouri, Dana Bettisworth, Ben Stamatakis, Alexandros Mansour, Yishay Mayrose, Itay Pupko, Tal A LASSO-based approach to sample sites for phylogenetic tree search |
title | A LASSO-based approach to sample sites for phylogenetic tree search |
title_full | A LASSO-based approach to sample sites for phylogenetic tree search |
title_fullStr | A LASSO-based approach to sample sites for phylogenetic tree search |
title_full_unstemmed | A LASSO-based approach to sample sites for phylogenetic tree search |
title_short | A LASSO-based approach to sample sites for phylogenetic tree search |
title_sort | lasso-based approach to sample sites for phylogenetic tree search |
topic | ISCB/Ismb 2022 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9236582/ https://www.ncbi.nlm.nih.gov/pubmed/35758778 http://dx.doi.org/10.1093/bioinformatics/btac252 |
work_keys_str_mv | AT eckernoa alassobasedapproachtosamplesitesforphylogenetictreesearch AT azouridana alassobasedapproachtosamplesitesforphylogenetictreesearch AT bettisworthben alassobasedapproachtosamplesitesforphylogenetictreesearch AT stamatakisalexandros alassobasedapproachtosamplesitesforphylogenetictreesearch AT mansouryishay alassobasedapproachtosamplesitesforphylogenetictreesearch AT mayroseitay alassobasedapproachtosamplesitesforphylogenetictreesearch AT pupkotal alassobasedapproachtosamplesitesforphylogenetictreesearch AT eckernoa lassobasedapproachtosamplesitesforphylogenetictreesearch AT azouridana lassobasedapproachtosamplesitesforphylogenetictreesearch AT bettisworthben lassobasedapproachtosamplesitesforphylogenetictreesearch AT stamatakisalexandros lassobasedapproachtosamplesitesforphylogenetictreesearch AT mansouryishay lassobasedapproachtosamplesitesforphylogenetictreesearch AT mayroseitay lassobasedapproachtosamplesitesforphylogenetictreesearch AT pupkotal lassobasedapproachtosamplesitesforphylogenetictreesearch |