FCCC LOGO Faculty Publications
Tao Q , Barba-Montoya J , Kumar S
Data-driven speciation tree prior for better species divergence times in calibration-poor molecular phylogenies
Bioinformatics. 2021 Jul 12;37(Suppl_1) :i102-i110
PMID: 34252953    PMCID: PMC8275332    URL: https://www.ncbi.nlm.nih.gov/pubmed/34252953
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MOTIVATION: Precise time calibrations needed to estimate ages of species divergence are not always available due to fossil records' incompleteness. Consequently, clock calibrations available for Bayesian dating analyses can be few and diffused, i.e. phylogenies are calibration-poor, impeding reliable inference of the timetree of life. We examined the role of speciation birth-death (BD) tree prior on Bayesian node age estimates in calibration-poor phylogenies and tested the usefulness of an informative, data-driven tree prior to enhancing the accuracy and precision of estimated times. RESULTS: We present a simple method to estimate parameters of the BD tree prior from the molecular phylogeny for use in Bayesian dating analyses. The use of a data-driven birth-death (ddBD) tree prior leads to improvement in Bayesian node age estimates for calibration-poor phylogenies. We show that the ddBD tree prior, along with only a few well-constrained calibrations, can produce excellent node ages and credibility intervals, whereas the use of an uninformative, uniform (flat) tree prior may require more calibrations. Relaxed clock dating with ddBD tree prior also produced better results than a flat tree prior when using diffused node calibrations. We also suggest using ddBD tree priors to improve the detection of outliers and influential calibrations in cross-validation analyses.These results have practical applications because the ddBD tree prior reduces the number of well-constrained calibrations necessary to obtain reliable node age estimates. This would help address key impediments in building the grand timetree of life, revealing the process of speciation and elucidating the dynamics of biological diversification. AVAILABILITY AND IMPLEMENTATION: An R module for computing the ddBD tree prior, simulated datasets and empirical datasets are available at https://github.com/cathyqqtao/ddBD-tree-prior.
1367-4811 Tao, Qiqing Barba-Montoya, Jose Kumar, Sudhir R01 GM126567/GM/NIGMS NIH HHS/United States 1661218/National Science Foundation/ GM0126567-03/NH/NIH HHS/United States Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. Bioinformatics. 2021 Jul 12;37(Suppl_1):i102-i110. doi: 10.1093/bioinformatics/btab307.