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210 results for “Bayesian inference”
FIGURE 3. Bayesian majority-rule consensus tree inferred from 16S and H3 in Revision of the genus Pseudopomatias and its relatives (Gastropoda: Cyclophoroidea: Pupinidae)
FIGURE 3. Bayesian majority-rule consensus tree inferred from 16S and H3 sequences. Posterior probability percentage estimates are indicated above branches. The scale bar represents the estimated number of nucleotide substitutions per site. The tree was rooted with Pomacea insularum (not indicated).
FIGURE 4. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50 in Laimaphelenchus hyrcanus n. sp. (Nematoda: Aphelenchoididae), a new species from northern Iran
FIGURE 4. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank accession numbers are listed for each taxon.
FIGURE 8. Bayesian inference consensus using a in Revalidation of Triatoma bahiensis Sherlock & Serafim, 1967 (Hemiptera: Reduviidae) and phylogeny of the T. brasiliensis species complex
FIGURE 8. Bayesian inference consensus using a Markov chain Monte Carlo algorithm applied to mitochondrial sequences of Cyt b fragments of 510 bp. The values over the nodes refer to bootstrap value by maximum parsimony. T. dimidiata and T. infestans were used as outgroup. Accession code of GenBank in the text.
FIGURE 8. Bayesian tree inferred from D2–D3 in Molecular characterisation of five nematode species (Chromadorida, Selachinematidae) from shelf and upper slope sediments off New Zealand, with description of three new species
FIGURE 8. Bayesian tree inferred from D2–D3 of LSU sequences under the general time-reversible (GTR) + proportion of invariable sites (I) + gamma distribution (G) model. Posterior probabilities greater than or equal to 50% are given on appropriate clades. New sequences provided in the present study are shown in bold. The scale (bottom left) stands for substitutions per site.
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.
FIGURE 4. Tree from Bayesian inference for Bungarus candidus, B in A new color pattern of the Bungarus candidus complex (Squamata: Elapidae) from Vietnam based on morphological and molecular data
FIGURE 4. Tree from Bayesian inference for Bungarus candidus, B. magnimaculatus, and outgroup taxa. Values (%) at internal nodes are Bayesian posterior probabilities and bootstrap values from maximum parsimony and maximum likelihood, respectively.
FIGURE 4. Bayesian tree inferred using sequences D2–D3 in Morphological and molecular characterisation of Discotylenchus lorestanensis sp. n. (Nematoda: Tylenchidae) from Iran
FIGURE 4. Bayesian tree inferred using sequences D2–D3 region of the LSU rDNA gene. Posterior probabilities (pp) exceeding 0.5 are given on appropriate clades, bifurcations with pp above 0.95 are considered to be well-supported. Nematode species and GenBank accession numbers are listed for each taxon. Newly generated D2–D3 LSU rDNA sequences are in bold.
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p><span>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (</span><em><span>λ</span></em><sub><span>1</span></sub><span> / <em>λ</em><sub>0 </sub></span><span>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</span></p>
Data from: Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)
<p>State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (<em>λ</em><sub>1</sub> / <em>λ</em><sub>0 </sub>= 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s <em>D</em>) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.</p>
BASCULE: Bayesian inference and clustering of mutational signatures leveraging biological priors
<p>In the preprint available at https://doi.org/10.1101/2024.09.16.613266 we present BASCULE, a new method to perform Bayesian signatures deconvolution and to cluster patients from the inferred exposures. The method can deconvolve any kind of mutational signature types (SBS, DBS, ID, etc.) including as input a reference catalogue of known signatures (i.e., COSMIC), and cluster the samples joinltly from the exposures of all signature types. BASCULE is available as an R package (https://github.com/caravagnalab/bascule.git). Here we release the data and code to reproduce the analysis on synthetic and real datasets presented in the preprint, in the "synthetic_data_validation.zip" and "real_data_validation.zip", respectively.</p>
FIGURE 6. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50 in A new species of the genus Tripylina Brzeski, 1963 (Nematoda: Enoplida: Trischistomatidae) from Zhejiang Province, eastern China
FIGURE 6. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank numbers are listed for each taxon.
FIGURE 5. Bayesian tree inferred from SSU gene DNA sequences. Posterior probabilities exceeding 50 in A new species of the genus Tripylina Brzeski, 1963 (Nematoda: Enoplida: Trischistomatidae) from Zhejiang Province, eastern China
FIGURE 5. Bayesian tree inferred from SSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank numbers are listed for each taxon.
FIGURE 10. Bayesian inference phylogenetic reconstruction using the mitochondrial gene cox1 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 10. Bayesian inference phylogenetic reconstruction using the mitochondrial gene cox1 of the Hypsolebias antenori species-group. Vertical bars represent species complexes. Numbers next to nodes represent posterior probability values for the relevant nodes; values <0.5 are not shown.
FIGURE 2. Bayesian inference tree for 8,074 in Terrestrial predatory leeches of the genus Orobdella (Hirudinea: Erpobdelliformes: Orobdellidae) endemic to the Southern Russian Far East: a new species of the genus from Primorsky Krai, Russia
FIGURE 2. Bayesian inference tree for 8,074 bp of nuclear 18S rRNA, 28S rRNA, and H3, and mitochondrial COI, tRNACys, tRNAMet, 12S rRNA, tRNAVal and 16S rRNA, tRNALeu and ND1 markers. Numbers on nodes indicate bootstrap (BS) values for maximum likelihood ≥ 60% and Bayesian posterior probabilities (PP) ≥ 0.90. Double asterisks denote nodes with BS = 100%, PP = 1.0; single asterisks denote nodes with BS ≥ 80%, PP ≥ 0.95. Numbers in parentheses represent the mid-body somite annulation of each species.
Fig. 5. Combined ITSand trnT-F phylogenybasedonmaximum parsimonyand Bayesian inference. Shadedsectionof thetree highlightsspecies with x in Canary grasses (Phalaris, Poaceae): Molecular phylogenetics, polyploidy and floret evolution
Fig. 5. Combined ITSand trnT-F phylogenybasedonmaximum parsimonyand Bayesian inference. Shadedsectionof thetree highlightsspecies with x = 6, and names shown in bold denote polyploid species. * = nodes collapsed in the strictconsensus maximum parsimony tree. • = unknown chromosome number. Floret types follow the structure defined in Fig. 2. A = annual and P = perennial habit.
FIGURE. The Bayesian tree of the Adaintum pedatum complex based on chloroplast markers and corresponding rhizome type. Support values (Bayesian inference posterior probability (BIPP) (upper) ≥ 0.5, and maximum likelihood bootstrap support (MLBS) (nether) ≥ 50%) are shown above the main branches, the thickened branches indicate MLBS=100 and BIPP=1. Yellow bar means erect rhizome; blue bar means creeping rhizome; gray bar means decumbent or short-creeping rhizome. in Adiantum japonicum, a new species of the Adiantum pedatum complex (Pteridaceae) from Japan
FIGURE. The Bayesian tree of the Adaintum pedatum complex based on chloroplast markers and corresponding rhizome type. Support values (Bayesian inference posterior probability (BIPP) (upper) ≥ 0.5, and maximum likelihood bootstrap support (MLBS) (nether) ≥ 50%) are shown above the main branches, the thickened branches indicate MLBS=100 and BIPP=1. Yellow bar means erect rhizome; blue bar means creeping rhizome; gray bar means decumbent or short-creeping rhizome.
FIGURE 2. Bayesian Inference phylogenetic tree including species from Brachycephalus didactylus and B in New species of flea-toad, genus Brachycephalus (Anura: Brachycephalidae) from the Atlantic Forest of Espírito Santo, Brazil
FIGURE 2. Bayesian Inference phylogenetic tree including species from Brachycephalus didactylus and B. ephippium groups occurring northward the state of São Paulo, based on 12S + 16S (1,152 bp, 42 terminals) genes, under GTR+G model of nucleotide substitution. We highlight the three phenotypic groups. Tip labels of Brachycephalus puri sp. nov. are in bold. Numbers close to nodes are Bayesian posterior probabilities (BPP). Values below 0.7 not shown.
Figure 1. Bayesian inference tree built using COI, 16S rDNA and ITS2 in Integrated taxonomy reveals multiple species in the Dendrobaena byblica (Rosa, 1893) complex (Oligochaeta: Lumbricidae)
Figure 1. Bayesian inference tree built using COI, 16S rDNA and ITS2 sequences. The numbers indicate the posterior probabilities. The colours show the two main clades.
FIG. 5. Bayesian phylogenetic tree inferred from D2D3 in First record of Bursaphelenchus hildegardae Braasch et al., 2006 (Nematoda) in New Zealand with updated information on morphology, sequencing and a key to species of the eggersi-group
FIG. 5. Bayesian phylogenetic tree inferred from D2D3 gene DNA sequences of Bursaphelenchus hildegardae. Posterior probabilities greater than 50% are given on appropriate clades. Nematode species, GenBank accession numbers and locations are listed for each taxon, if known.
FIGURE Phylogenetic relationships of the Coelastrella genus inferred from the 18S-ITS1-5.8S-ITS2 region. The Neighbor-Joining (NJ), Maximum Likelihood (ML) bootstrap values and Bayesian posterior probabilities (PP) are presented at the nodes (NJ/ML/PP). Only values above 75 are shown. Strains provided in this study are indicated in bold font. Authentic strains marked with asterisks. The scale bar represents the number of substitutions per site. The GenBank accession numbers of Coelastrella can be found in the Table 3. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE Phylogenetic relationships of the Coelastrella genus inferred from the 18S-ITS1-5.8S-ITS2 region. The Neighbor-Joining (NJ), Maximum Likelihood (ML) bootstrap values and Bayesian posterior probabilities (PP) are presented at the nodes (NJ/ML/PP). Only values above 75 are shown. Strains provided in this study are indicated in bold font. Authentic strains marked with asterisks. The scale bar represents the number of substitutions per site. The GenBank accession numbers of Coelastrella can be found in the Table 3.
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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)
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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.