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501 results for “Phylogenetic tree”
Supplementary phylogenetic trees of Babesia bigemina based on partial sequences of both genes Rap-1a and gp45, with SH-aLRT support values (%), aBayes support, and ultrafast bootstrap support (%).
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Data from: An efficient independence sampler for updating branches in Bayesian Markov chain Monte Carlo sampling of phylogenetic trees
Sampling tree space is the most challenging aspect of Bayesian phylogenetic inference. The sheer number of alternative topologies is problematic by itself. In addition, the complex dependency between branch lengths and topology increases the difficulty of moving efficiently among topologies. Current tree proposals are fast but sample new trees using primitive transformations or re-mappings of old branch lengths. This reduces acceptance rates and presumably slows down convergence and mixing. Here, we explore branch proposals that do not rely on old branch lengths but instead are based on approximations of the conditional posterior. Using a diverse set of empirical data sets, we show that most conditional branch posteriors can be accurately approximated via a Γ distribution. We empirically determine the relationship between the logarithmic conditional posterior density, its derivatives, and the characteristics of the branch posterior. We use these relationships to derive an independence sampler for proposing branches with an acceptance ratio of ∼90% on most data sets. This proposal samples branches between 2× and 3× more efficiently than traditional proposals with respect to the effective sample size per unit of runtime. We also compare the performance of standard topology proposals with hybrid proposals that use the new independence sampler to update those branches that are most affected by the topological change. Our results show that hybrid proposals can sometimes noticeably decrease the number of generations necessary for topological convergence. Inconsistent performance gains indicate that branch updates are not the limiting factor in improving topological convergence for the currently employed set of proposals. However, our independence sampler might be essential for the construction of novel tree proposals that apply more radical topology changes.
Data from: Quantifying MCMC exploration of phylogenetic tree space
In order to gain an understanding of the effectiveness of phylogenetic Markov chain Monte Carlo (MCMC), it is important to understand how quickly the empirical distribution of the MCMC converges to the posterior distribution. In this paper we investigate this problem on phylogenetic tree topologies with a metric that is especially well suited to the task: the subtree prune-and-regraft (SPR) metric. This metric directly corresponds to the minimum number of MCMC rearrangements required to move between trees in common phylogenetic MCMC implementations. We develop a novel graph-based approach to analyze tree posteriors and find that the SPR metric is much more informative than simpler metrics that are unrelated to MCMC moves. In doing so we show conclusively that topological peaks do occur in Bayesian phylogenetic posteriors from real data sets as sampled with standard MCMC approaches, investigate the efficiency of Metropolis-coupled MCMC (MCMCMC) in traversing the valleys between peaks, and show that conditional clade distribution (CCD) can have systematic problems when there are multiple peaks.
Data from: A Poissonian model of indel rate variation for phylogenetic tree inference
While indel rate variation has been observed and analyzed in detail, it is not taken into account by current indel-aware phylogenetic reconstruction methods. In this work, we introduce a continuous time stochastic process, the geometric Poisson indel process, that generalizes the Poisson indel process by allowing insertion and deletion rates to vary across sites. We design an efficient algorithm for computing the probability of a given multiple sequence alignment based on our new indel model. We describe a method to construct phylogeny estimates from a fixed alignment using neighbor joining. Using simulation studies, we show that ignoring indel rate variation may have a detrimental effect on the accuracy of the inferred phylogenies, and that our proposed method can sidestep this issue by inferring latent indel rate categories. We also show that our phylogenetic inference method may be more stable to taxa subsampling than methods that either ignore indels or indel rate variation.
FIGURE 7. Maximum likelihood phylogenetic tree inferred from a in Three uncharted endemicearthworm species of the genus Eutyphoeus (Oligochaeta Octochaetidae) from Mizoram, India
FIGURE 7. Maximum likelihood phylogenetic tree inferred from a dataset of 609 positions.
Phylogenetic tree
<p>The phylogenetic trees constructed with the cp_genus-tRNA and cp_genus-rRNA datasets.</p>
Figure 3. Phylogenetic trees obtained from morphological data. A in Combined-data phylogenetics and character evolution of Clitellata (Annelida) using 18S rDNA and morphology
Figure 3. Phylogenetic trees obtained from morphological data. A, phylogenetic tree obtained from one of the three replicate Bayesian inference runs of the somatic data set. Posterior probabilities of ± 0.85 are indicated in front of the nodes. B, phylogenetic tree obtained from one of the three replicate Bayesian inference runs of the spermatozoal data set. Posterior probabilities ± 0.85 are indicated in front of the nodes.
Supplementary material 3 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S3
Supplementary material 9 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S9
Supplementary material 1 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S1
Supplementary material 7 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S7
Supplementary material 21 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Table S2
Supplementary material 8 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S8
Supplementary material 19 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S4
Supplementary material 18 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S3
Supplementary material 17 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S2
Supplementary material 16 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
Figure S1
Supplementary material 11 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S11
Supplementary material 15 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S15
Supplementary material 4 from: Rajter Ľ, Dunthorn M (2021) Ciliate SSU-rDNA reference alignments and trees for phylogenetic placements of metabarcoding data. Metabarcoding and Metagenomics 5: e69602. https://doi.org/10.3897/mbmg.5.69602
File S4
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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.