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49 results for “Bayesian phylogenetic inference”
Figure 1. Bayesian phylogenetic tree inferred from the 640 in Two new Geoplaninae species (Platyhelminthes: Continenticola) from Southern Brazil based on an integrative taxonomic approach
Figure 1. Bayesian phylogenetic tree inferred from the 640-bp of cytochrome c oxidase subunit I gene under GTR + I + G model of sequence evolution. The two new species are highlighted in light grey (Cratera ochra sp. nov.) and dark grey (Obama maculipunctata sp. nov.). Values indicate support for each node according to the maximum posterior probabilities>70% and bootstrap support values> 70%, respectively.
Fig. 6. Bayesian inference tree for 5519 in First Record of Poecilobdella nanjingensis (Hirudinida: Arhynchobdellida: Hirudinidae) from Taiwan and its Molecular Phylogenetic Position within the Family
Fig. 6. Bayesian inference tree for 5519 bp alignment positions of nuclear 18S rRNA, 28S rRNA, mitochondrial cytochrome c oxidase subunit I, and 12S rRNA markers. Numbers on nodes indicate bootstrap values for maximum likelihood and Bayesian inference posterior probabilities.
Figure 5. Bayesian Inference tree calculated with complete cox1 in Novel phylogenetic clade of avian Haemoproteus parasites (Haemosporida, Haemoproteidae) from Accipitridae raptors, with description of a new Haemoproteus species
Figure 5. Bayesian Inference tree calculated with complete cox1 (1428 bp), cox3 (753 bp), and cytb (1127 bp) sequences of haemosporidian parasites and Klossiella equi (MH203050) and Klossia razorbacki (MT084562) as the outgroup. Bayesian posterior probabilities and Maximum Likelihood bootstrap values are indicated at most nodes. The scale bar indicates the expected number of substitutions per site according to the model of sequence evolution applied.
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West) in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West)
Figure 4. Bayesian Inference phylogenetic tree inferred from 1039 in Genetic Relationships of Long-nosed Potoroos Potorous tridactylus (Kerr, 1792) from the Bass Strait Islands, with Notes on the Subspecies Potorous tridactylus benormi Courtney, 1963
Figure 4. Bayesian Inference phylogenetic tree inferred from 1039 bp of concatenated CO1 and ND2 mitochondrial DNA sequence data. Posterior probabilities for major lineages are shown. A similar tree topology was also inferred from Maximum Likelihood.
Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
<p>Intimate ecological interactions, such as those between parasites and their hosts, may persist over long time spans, coupling the evolutionary histories of the lineages involved. Most methods that reconstruct the coevolutionary history of such interactions make the simplifying assumption that parasites have a single host. Many methods also focus on congruence between host and parasite phylogenies, using cospeciation as the null model. However, there is an increasing body of evidence suggesting that the host ranges of parasites are more complex: that host ranges often include more than one host and evolve via gains and losses of hosts rather than through cospeciation alone. Here, we develop a Bayesian approach for inferring coevolutionary history based on a model accommodating these complexities. Specifically, a parasite is assumed to have a host repertoire, which includes both potential hosts and one or more actual hosts. Over time, potential hosts can be added or lost, and potential hosts can develop into actual hosts or vice versa. Thus, host colonization is modeled as a two-step process that may potentially be influenced by host relatedness. We first explore the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameter values. We then use our approach, implemented in the program RevBayes, to infer the coevolutionary history between 34 Nymphalini butterfly species and 25 angiosperm families. Our analysis suggests that host relatedness among angiosperm families influences how easily Nymphalini lineages gain new hosts.</p>
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Dodonaphy - a Software using Hyperbolic Space for Bayesian Phylogenetic Inference
<p>Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. It faces the challenging problem of moving throughout the high-dimensional space of trees. However, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences into hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference. The posterior probability is computed by decoding a neighbour joining tree from proposed embedding locations. We empirically demonstrate the fidelity of this method on eight data sets. The sampled posterior distribution recovers the splits and branch lengths to a high degree. We investigated the effects of curvature and embedding dimension on the Markov Chain's performance. Finally, we discuss the prospects for adapting this method to navigate tree space with gradients.</p> <p>This software embeds phylogenetic taxa in hyerbolic space to perform Bayesian inference. Version 1.0.0 includes a Markov Chain Monte Carlo (MCMC) that we compared to the state-of-art on eight datasets (previously published elsewhere). This package is implemented in Python3.9 with a simle command line interface provided.</p>
Data and supplementary information from: Sequential bayesian phylogenetic inference
<p>The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously certain results. Here, we formally develop and test the sequential approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior produced in the previous step. The sequential approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be more efficient computationally than the traditional joint approach when multiple models are being tested. We show that the sequential approach is identical in practice to the joint approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into time-calibrate phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of insect divergence times estimation using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Gamma+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different-node calibration strategies and show that both, the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies, impact divergence time estimates. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approach for divergence time estimation, including branch lengths estimation from other software.</p>
Dodonaphy - a Software using Hyperbolic Space for Bayesian Phylogenetic Inference
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Data and supplementary information from: Sequential bayesian phylogenetic inference
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Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
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Data from: Using parsimony-guided tree proposals to accelerate convergence in Bayesian phylogenetic inference
<p>Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as `convergence') and in estimating the correct proportions of the different types of them (known as `mixing'). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical datasets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these datasets, ranging in size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.</p>
FIGURE 5 Bayesian phylogenetic tree inferred from SSU gene DNA sequences. Posterior probabilities great than 50 in New Zealand species of the genus Tripyla Bastian, 1865 (Nematoda: Triplonchida: Tripylidae). I: A new species, a new record and key to long-tailed species
FIGURE 5 Bayesian phylogenetic tree inferred from SSU gene DNA sequences. Posterior probabilities great than 50% are given on appropriate clades. Nematode species, GenBank numbers, locations are listed for each taxon if known.
FIGURE 6 Bayesian phylogenetic tree inferred from LSU gene DNA sequences. Posterior probabilities greater than 50 in New Zealand species of the genus Tripyla Bastian, 1865 (Nematoda: Triplonchida: Tripylidae). I: A new species, a new record and key to long-tailed species
FIGURE 6 Bayesian phylogenetic tree inferred from LSU gene DNA sequences. Posterior probabilities greater than 50% are given on appropriate clades. Nematode species, GenBank numbers, locations are listed for each taxon if known.
FIGURE 1. Bayesian phylogenetic tree inferred from SSU gene DNA sequences. Posterior probabilities great than 50 in New Zealand species of the genus Tripyla Bastian, 1865 (Nematoda: Triplonchida: Tripylidae). II: Two new, a known species and key to species
FIGURE 1. Bayesian phylogenetic tree inferred from SSU gene DNA sequences. Posterior probabilities great than 50% are given on appropriate clades. Nematode species, GenBank numbers, locations are listed for each taxon if known.
FIGURE 7. Bayesian phylogenetic tree inferred from SSU rRNA gene sequences. Posterior probabilities greater than 50 in Description of Trischistoma abharensis n. sp. (Nematoda: Trischistomatidae) and first record of Tripylella intermedia (Bütschli, 1873) Brzeski & Winiszewska-Ślipinska, 1993 (Nematoda: Tripylidae) from Iran
FIGURE 7. Bayesian phylogenetic tree inferred from SSU rRNA gene sequences. Posterior probabilities greater than 50% are given on appropriate clades. Nematode species, GenBank accession numbers, and locations are listed for each taxon if known. The accession no. AY284737 was originally deposited in GenBank as Paratripyla sp., but it was used as Tripylella sp. by van Megen et. al. (2009).
FIGURE 8. Bayesian phylogenetic tree inferred from D2D3 in First record of the root knot nematode, Meloidogyne minor in New Zealand with description, sequencing information and key to known species of Meloidogyne in New Zealand
FIGURE 8. Bayesian phylogenetic tree inferred from D2D3 gene DNA sequences of Meloidogyne minor. Posterior probabilities greater than 50% are given on appropriate clades. Nematode species, GenBank accession numbers and locations are listed for each taxon, if known.
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DANDI Archive for NWB datasets
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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.