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203 results for “Divergence times”
Data for 'Priors and Posteriors in Bayesian Timing of Divergence Analyses: the Age of Butterflies Revisited'
<p>Data and results for Chazot et al. (2018) 'Priors and Posteriors in Bayesian Timing of Divergence Analyses: the Age of Butterflies Revisited.'</p> <p>S1. List of taxa and Genbank accession codes.</p> <p>S2. Molecular matrix for the core analysis (S2a), the reduced dataset (S2b) and the dataset with a mitochondrial fragment (S2c).</p> <p>S3. RAxML topology (S3a) and time-calibrated tree (S3b) obtained from the core analysis. In S3a, numbers at the nodes indicate rapid-bootstrap support values. In S3b, node ages are the median of node age posterior distributions.</p> <p>S4. Tree obtained when using only deep-level fossil calibrations. Node ages are the median of node age posterior distributions.</p> <p>S5. Tree obtained when using only shallow-level fossil calibrations. Node ages are the median of node age posterior distributions.</p> <p>S6. Tree obtained from the reduced dataset. Node ages are the median of node age posterior distributions.</p> <p>S7. Tree obtained when using exponential fossil calibration priors. Node ages are the median of node age posterior distributions.</p> <p>S8. Tree obtained when adding a mitochondrial gene fragment. Node ages are the median of node age posterior distributions.</p> <p>S9. Tree obtained when using fossil information only modeled using lognormal priors. Node ages are the median of node age posterior distributions.</p> <p>S10. Tree obtained when using the host-plant ages obtained from Foster et al. (2017). In S10a node ages are the median of node age posterior distributions, while in S10b the node ages are the mode the mode of the kernel density estimate of the posterior distribution.</p> <p>S11. Tree obtained when using a Yule prior instead of Birth-Death tree prior. Node ages are the median of node age posterior distributions.</p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects
<p>This uploads contains the datasets, phylogenetic tree and associated R code used to generate the results reported in the article: "Divergence time and environmental similarity predict the strength of morphological convergence in stick and leaf insects" published in Proceedings of the National Academy of Sciences USA (2024).<br>A detailed explanation of datasetS1 can be found in the supplementary data of the article. </p>
PAReTT: a Python package for the Automated Retrieval and management of divergence time data from the TimeTree resource for downstream analyses (Dataset)
<p>Dataset for article by the same title submitted the the <em>Journal of Molecular Evolution</em>.</p>
Estimation of species divergence times in presence of cross-species gene flow
<p>Cross-species introgression can have significant impacts on phylogenomic reconstruction of species divergence events. Here, we used simulations to show how the presence of even a small amount of introgression can bias divergence time estimates when gene flow is ignored in the analysis. Using advances in analytical methods under the multispecies coalescent (MSC) model, we demonstrate that by accounting for incomplete lineage sorting and introgression using large phylogenomic data sets this problem can be avoided. The multispecies-coalescent with-introgression (MSci) model is capable of accurately estimating both divergence times and ancestral effective population sizes, even when only a single diploid individual per species is sampled. We characterize some general expectations for biases in divergence time estimation under three different scenarios: 1) introgression between sister species, 2) introgression between non-sister species, and 3) introgression from an unsampled (i.e., ghost) outgroup lineage. We also conducted simulations under the isolation-with-migration (IM) model, and found that the MSci model assuming episodic gene flow was able to accurately estimate species divergence times despite high levels of continuous gene flow. We estimated divergence times under the MSC and MSci models from two published empirical datasets with previous evidence of introgression, one of 372 target enrichment loci from baobabs (<em>Adansonia</em>), and another of 1,000 transcriptome loci from fourteen species of the tomato relative, <em>Jaltomata</em>. The empirical analyses not only confirm our findings from simulations, demonstrating that the MSci model can reliably estimate divergence times, but also show that divergence time estimation under the MSC can be robust to the presence of small amounts of introgression in empirical datasets with extensive taxon sampling.</p>
Scalable Bayesian divergence time estimation with ratio transformations
<div class="page"> <div class="layoutArea"> <div class="column"> <p><span>Divergence time estimation is crucial to provide temporal signals for dating bio</span><span>logically important events, from species divergence to viral transmissions in space and </span><span>time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic </span><span>studies have analyzed hundreds to thousands of sequences. Such large-scale analyses</span><span> </span><span>challenge divergence time reconstruction by requiring inference on highly-correlated</span><span> </span><span>internal node heights that often become computationally infeasible. To overcome this</span><span> </span><span>limitation, we explore a ratio transformation that maps the original </span><span>N - </span><span>1 internal</span><span> </span><span>node heights into a space of one height parameter and </span><span>N - </span><span>2 ratio parameters. To</span><span> </span><span>make the analyses scalable, we develop a collection of linear-time algorithms to com</span><span>pute the gradient and Jacobian-associated terms of the log-likelihood with respect to </span><span>these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio trans</span><span>form in a Bayesian framework to learn the divergence times in four pathogenic viruses</span><span> </span><span>(West Nile virus, rabies virus, Lassa virus and Ebola virus) and the coralline red algae.</span><span> </span><span>Our method both resolves a mixing issue in the West Nile virus example and improves</span><span> </span><span>inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well</span><span> </span><span>as for the algae example. Our method now also makes it computationally feasible to</span><span> </span><span>incorporate mixed-effects molecular clock models for the Ebola virus example, confirms</span><span> </span><span>the findings from the original study and reveals clearer multimodal distributions of the</span><span> </span><span>divergence times of some clades of interest.</span></p> </div> </div> </div>
Data from: Cryptic diversity in the Mexican highlands: thousands of UCE loci help illuminate phylogenetic relationships, species limits and divergence times of montane rattlesnakes (Viperidae: Crotalus)
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Data from: Responses of activity rhythms to temperature cues evolve in Drosophila populations selected for divergent timing of eclosion
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Data from: Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana: estimates of selection and QTL mapping
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Data from: Chronospaces: an R package for the statistical exploration of divergence times promotes the assessment of methodological sensitivity
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Divergence time estimation using ddRAD data and an isolation-with-migration model applied to water vole populations of Arvicola
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Local adaptation from afar: migratory bird populations diverge in the initiation of reproductive timing while wintering in sympatry
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Fossil-calibrated inference of divergence times among the Volvocine algae enables reconstruction of the steps that led to differentiated multicellularity
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Supplementary material for: Impact of ghost introgression on coalescent-based species tree inference and estimation of divergence time
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Scalable Bayesian divergence time estimation with ratio transformations
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Insights from empirical analyses and simulations on using multiple fossil calibrations with relaxed clocks to estimate divergence times
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Delayed adaptive radiation among New Zealand stream fishes: joint estimation of divergence time and trait evolution in a newly delineated island species flock
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Estimation of species divergence times in presence of cross-species gene flow
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Data from: Calibrating divergence times on species trees versus gene trees: implications for speciation history of Aphelocoma jays
Estimates of the timing of divergence are central to testing the underlying causes of speciation. Relaxed molecular clocks and fossil calibration have improved these estimates; however, these advances are implemented in the context of gene trees, which can overestimate divergence times. Here we couple recent innovations for dating speciation events with the analytical power of species trees, where multilocus data are considered in a coalescent context. Divergence times are estimated in the bird genus Aphelocoma to test whether speciation coincided with mountain uplift or glacial cycles. Gene trees and species trees show general agreement that diversification began in the Miocene amid mountain uplift. However, dates from the multilocus species tree are more recent, occurring predominately in the Pleistocene, consistent with theory that divergence times can be significantly overestimated with gene-tree based approaches that do not correct for genetic divergence that predates speciation. In addition to coalescent stochasticity, Haldane's Rule could account for differences in timing estimates between mitochondrial and nuclear genes. By incorporating a fossil calibration applied to the species tree, in addition to the process of gene lineage coalescence, the present approach provides a more biologically realistic framework for dating speciation events, and hence for testing the links between diversification and specific biogeographic and geologic events.
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