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51 results for “multispecies coalescent”
Data from: Sky island diversification meets the multispecies coalescent – divergence in the spruce-fir moss spider (Microhexura montivaga, Araneae, Mygalomorphae) on the highest peaks of southern Appalachia
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Data from: Probabilistic species tree distances: implementing the multispecies coalescent to compare species trees within the same model-based framework used to estimate them
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Data from: A preliminary framework for DNA barcoding, incorporating the multispecies coalescent
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Data from: Displayed trees do not determine distinguishability under the network multispecies coalescent
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Data from: Bayesian inference of reticulate phylogenies under the multispecies network coalescent
The multispecies coalescent (MSC) is a statistical framework that models how gene genealogies grow within the branches of a species tree. The field of computational phylogenetics has witnessed an explosion in the development of methods for species tree inference under MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary processes such as hybridization or horizontal gene transfer. We report on a novel method for Bayesian inference of genome and species phylogenies under the multispecies network coalescent (MSNC). This framework models gene evolution within the branches of a phylogenetic network, thus incorporating reticulate evolutionary processes, such as hybridization, in addition to incomplete lineage sorting. As phylogenetic networks with different numbers of reticulation events correspond to points of different dimensions in the space of models, we devise a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under MSNC. We implemented the methods in the publicly available, open-source software package PhyloNet and studied their performance on simulated and biological data. The work extends the reach of Bayesian inference to phylogenetic networks and enables new evolutionary analyses that account for reticulation.
Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
<p>Incomplete lineage sorting (ILS) can lead to biased divergence time estimates. To explore if and how ILS has influenced the results of a recent study of worldwide phylogeny of three-spined sticklebacks (Gasterosteus aculeatus), we estimated divergence times among major clades by applying both a concatenation approach and the multispecies coalescent (MSC) model to single-nucleotide polymorphisms. To further test the influence of different calibration strategies, we applied different calibrations to the root and to younger nodes in addition to the ones used in the original study. Both the updated calibrations and the application of the MSC model influenced divergence time estimates, sometimes significantly. The new divergence time estimates were more ancient than in the previous study for older nodes, whereas the estimates of younger nodes were not strongly affected by the re-analyses. However, given the applied MSC method employs a simple substitution model and cannot account for changes in population size, we suggest that different analytical approaches and calibration strategies should be used in order to explore uncertainty in divergence time estimates. This study provides a valuable reference timeline for the ages of worldwide three-spined stickleback populations and emphasizes the need to embrace, rather than obscure, uncertainties around divergence time estimates.</p>
Data from: A multispecies coalescent model for quantitative traits
We present a multispecies coalescent model for quantitative traits that allows for evolutionary inferences at micro- and macroevolutionary scales. A major advantage of this model is its ability to incorporate genealogical discordance underlying a quantitative trait. We show that discordance causes a decrease in the expected trait covariance between more closely related species relative to more distantly related species. If unaccounted for, this outcome can lead to an overestimation of a trait's evolutionary rate, to a decrease in its phylogenetic signal, and to errors when examining shifts in mean trait values. The number of loci controlling a quantitative trait appears to be irrelevant to all trends reported, and discordance also affected discrete, threshold traits. Our model and analyses point to the conditions under which different methods should fare better or worse, in addition to indicating current and future approaches that can mitigate the effects of discordance.
Data from: Resolving conflict in eutherian mammal phylogeny using phylogenomics and the multispecies coalescent model
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Data from: A multispecies coalescent model for quantitative traits
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Data from: Bayesian inference of reticulate phylogenies under the multispecies network coalescent
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Estimating uncertainty in divergence times among three-spined stickleback clades using the multispecies coalescent
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.