Dataset Information

RAxML Grove: An empirical Phylogenetic Tree Database.

ABSTRACT: The assessment of novel phylogenetic models and inference methods is routinely being conducted via experiments on simulated as well as empirical data. When generating synthetic data it is often unclear how to set simulation parameters for the models and generate trees that appropriately reflect empirical model parameter distributions and tree shapes. As a solution, we present and make available a new database called 'RAxML Grove' currently comprising more than 60,000 inferred trees and respective model parameter estimates from fully anonymized empirical data sets that were analyzed using RAxML (Stamatakis (2014)) and RAxML-NG (Kozlov et al. (2019) on two web servers. We also describe and make available two simple applications of RAxML Grove to exemplify its usage and highlight its utility for designing realistic simulation studies and analyzing empirical model parameter and tree shape distributions. RAxML Grove is freely available at https://github.com/angtft/RAxMLGrove.

SUBMITTER: Hohler D

PROVIDER: S-EPMC8896636 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

RAxML Grove: an empirical phylogenetic tree database.

Höhler Dimitri D Pfeiffer Wayne W Ioannidis Vassilios V Stockinger Heinz H Stamatakis Alexandros A

Bioinformatics (Oxford, England) 20220301 6

<h4>Summary</h4>The assessment of novel phylogenetic models and inference methods is routinely being conducted via experiments on simulated as well as empirical data. When generating synthetic data it is often unclear how to set simulation parameters for the models and generate trees that appropriately reflect empirical model parameter distributions and tree shapes. As a solution, we present and make available a new database called 'RAxML Grove' currently comprising more than 60 000 inferred tre ...[more]

PMID: 34962976

Similar Datasets

Project description:Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

Dataset Information

RAxML Grove: An empirical Phylogenetic Tree Database.

Publications

RAxML Grove: an empirical phylogenetic tree database.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets