Dataset Information
A fast and memory-efficient implementation of the transfer bootstrap.
Ontology highlight
ABSTRACT: Motivation
Recently, Lemoine et al. suggested the transfer bootstrap expectation (TBE) branch support metric as an alternative to classical phylogenetic bootstrap support for taxon-rich datasets. However, the original TBE implementation in the booster tool is compute- and memory-intensive.Results
We developed a fast and memory-efficient TBE implementation. We improve upon the original algorithm by Lemoine et al. via several algorithmic and technical optimizations. On empirical as well as on random tree sets with varying taxon counts, our implementation is up to 480 times faster than booster. Furthermore, it only requires memory that is linear in the number of taxa, which leads to 10× to 40× memory savings compared with booster.Availability and implementation
Our implementation has been partially integrated into pll-modules and RAxML-NG and is available under the GNU Affero General Public License v3.0 at https://github.com/ddarriba/pll-modules and https://github.com/amkozlov/raxml-ng. The parallel version that also computes additional TBE-related statistics is available at: https://github.com/lutteropp/raxml-ng/tree/tbe.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Lutteropp S
PROVIDER: S-EPMC7141843 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
A fast and memory-efficient implementation of the transfer bootstrap.
Bioinformatics (Oxford, England) 20200401 7
<h4>Motivation</h4>Recently, Lemoine et al. suggested the transfer bootstrap expectation (TBE) branch support metric as an alternative to classical phylogenetic bootstrap support for taxon-rich datasets. However, the original TBE implementation in the booster tool is compute- and memory-intensive.<h4>Results</h4>We developed a fast and memory-efficient TBE implementation. We improve upon the original algorithm by Lemoine et al. via several algorithmic and technical optimizations. On empirical as ...[more]