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SFREEMAP - A simulation-free tool for stochastic mapping

BACKGROUND: Stochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-...

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Autores principales: Pasqualin, Diego, Barbeitos, Marcos, Silva, Fabiano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322606/
https://www.ncbi.nlm.nih.gov/pubmed/28228094
http://dx.doi.org/10.1186/s12859-017-1554-7
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author Pasqualin, Diego
Barbeitos, Marcos
Silva, Fabiano
author_facet Pasqualin, Diego
Barbeitos, Marcos
Silva, Fabiano
author_sort Pasqualin, Diego
collection PubMed
description BACKGROUND: Stochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-time Markov Chain simulations whose parameters are difficult to adjust and subjected to inherent inaccuracy. Thus, researchers must run a large number of simulations in order to obtain adequate estimates. Although execution time tends to be relatively small when simulations are performed on a single tree assumed to be the “true” topology, it may become an issue if analyses are conducted on several trees, such as the ones that make up posterior distributions obtained via Bayesian phylogenetic inference. Working with such distributions is preferable to working with a single tree, for they allow the integration of phylogenetic uncertainty into parameter estimation. In such cases, detailed character mapping becomes less important than parameter integration across topologies. Here, we present an R-based implementation (SFREEMAP) of an analytical approach to obtain accurate, per-branch expectations of numbers of state transitions and dwelling times. We also introduce an intuitive way of visualizing the results by integrating over the posterior distribution and summarizing the parameters onto a target reference topology (such as a consensus or MAP tree) provided by the user. RESULTS: We benchmarked SFREEMAP’s performance against make.simmap, a popular R-based implementation of stochastic mapping. SFREEMAP confirmed theoretical expectations outperforming make.simmap in every experiment and reducing computation time of relatively modest datasets from hours to minutes. We have also demonstrated that SFREEMAP returns estimates which were not only similar to the ones obtained by averaging across make.simmap mappings, but also more accurate, according to simulated data. We illustrate our visualization strategy using previously published data on the evolution of coloniality in scleractinian corals. CONCLUSION: SFREEMAP is an accurate and fast alternative to ancestral state reconstruction via simulation-based stochastic mapping. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1554-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-53226062017-03-01 SFREEMAP - A simulation-free tool for stochastic mapping Pasqualin, Diego Barbeitos, Marcos Silva, Fabiano BMC Bioinformatics Software BACKGROUND: Stochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-time Markov Chain simulations whose parameters are difficult to adjust and subjected to inherent inaccuracy. Thus, researchers must run a large number of simulations in order to obtain adequate estimates. Although execution time tends to be relatively small when simulations are performed on a single tree assumed to be the “true” topology, it may become an issue if analyses are conducted on several trees, such as the ones that make up posterior distributions obtained via Bayesian phylogenetic inference. Working with such distributions is preferable to working with a single tree, for they allow the integration of phylogenetic uncertainty into parameter estimation. In such cases, detailed character mapping becomes less important than parameter integration across topologies. Here, we present an R-based implementation (SFREEMAP) of an analytical approach to obtain accurate, per-branch expectations of numbers of state transitions and dwelling times. We also introduce an intuitive way of visualizing the results by integrating over the posterior distribution and summarizing the parameters onto a target reference topology (such as a consensus or MAP tree) provided by the user. RESULTS: We benchmarked SFREEMAP’s performance against make.simmap, a popular R-based implementation of stochastic mapping. SFREEMAP confirmed theoretical expectations outperforming make.simmap in every experiment and reducing computation time of relatively modest datasets from hours to minutes. We have also demonstrated that SFREEMAP returns estimates which were not only similar to the ones obtained by averaging across make.simmap mappings, but also more accurate, according to simulated data. We illustrate our visualization strategy using previously published data on the evolution of coloniality in scleractinian corals. CONCLUSION: SFREEMAP is an accurate and fast alternative to ancestral state reconstruction via simulation-based stochastic mapping. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1554-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-22 /pmc/articles/PMC5322606/ /pubmed/28228094 http://dx.doi.org/10.1186/s12859-017-1554-7 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Pasqualin, Diego
Barbeitos, Marcos
Silva, Fabiano
SFREEMAP - A simulation-free tool for stochastic mapping
title SFREEMAP - A simulation-free tool for stochastic mapping
title_full SFREEMAP - A simulation-free tool for stochastic mapping
title_fullStr SFREEMAP - A simulation-free tool for stochastic mapping
title_full_unstemmed SFREEMAP - A simulation-free tool for stochastic mapping
title_short SFREEMAP - A simulation-free tool for stochastic mapping
title_sort sfreemap - a simulation-free tool for stochastic mapping
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322606/
https://www.ncbi.nlm.nih.gov/pubmed/28228094
http://dx.doi.org/10.1186/s12859-017-1554-7
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