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371 results for “Likelihood”
Training and test data with scripts for simulation-trained deep learning and likelihood-based phylogeography comparisons
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Genotype likelihood (beagle file) and genotype (vcf) files of North Atlantic and Black Sea Harbour porpoises (Phocoena phocoena)
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Data and code from: Decision-making in the wild: Urgency and complexity drive feeding decision speed and the likelihood of revising a choice in a sex-dependent manner in great tit (<em>Parus major</em>) parents
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Budworms, beetles, and wildfire: disturbance interactions influence the likelihood of insect-caused disturbances at a subcontinental scale
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Insights from Fisher's geometric model on the likelihood of speciation under different histories of environmental change
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Full-likelihood genomic analysis clarifies a complex history of species divergence and introgression: the example of the erato-sara group of Heliconius butterflies
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A Likelihood-Ratio Test for Lumpability of Phylogenetic Data: Is the Markovian Property of an Evolutionary Process Retained in Recoded DNA?
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Phylogenetic analysis of characters with dependencies under maximum likelihood
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Data from: The influence of the number of tree searches on maximum likelihood inference in phylogenomics
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Data for: Diversification models conflate likelihood and prior, and cannot be compared using conventional model-comparison tools
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Online phylogenetics using parsimony produces slightly better trees and is dramatically more efficient for large SARS-CoV-2 phylogenies than de novo and maximum-likelihood approaches
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Fig. 2 Maximum likelihood tree inferred from the combined cmdA, tef1 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 2 Maximum likelihood tree inferred from the combined cmdA, tef1, tub, rpb1, and rpb2 sequence datasets of the Fusarium fujikuroi species complex (FFSC) including eight Indonesian isolates (indicated in blue). Bootstrap support values and Bayesian posterior probabilities are given at each node. The tree is rooted to Fusarium nirenbergiae (CBS 744.97) and F. oxysporum (CBS 716.74).
FIGURE 13. Maximum Likelihood tree inferred using the Cox1 in The identity of the invasive yellow-striped terrestrial planarian found recently in Europe: Caenoplana variegata (Fletcher & Hamilton, 1888) or Caenoplana bicolor (Graff, 1899)?
FIGURE 13. Maximum Likelihood tree inferred using the Cox1 dataset. Values at nodes correspond to BP support values (left) and PP from the Bayesian analysis (right). Vertical bars at right correspond to the molecular species delimitation methods assignations (purple: ABGD; orange: bPTP).
FIGURE5. Maximum likelihood tree based on the Kimura 2-parameter model of the COI sequences from the Siphamia species with P. kauderni as the outgroup. Tree shown here has the highest log likelihood following 10 000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches, branch lengths are measured in the number of substitutions per site and all positions containing gaps and missing data have been eliminated. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE5. Maximum likelihood tree based on the Kimura 2-parameter model of the COI sequences from the Siphamia species with P. kauderni as the outgroup. Tree shown here has the highest log likelihood following 10 000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches, branch lengths are measured in the number of substitutions per site and all positions containing gaps and missing data have been eliminated.
Sighting record data to estimate extinction likelihood for North American and Hawaiian birds
<p>This is an updated version of Table S1 from Elphick, C.S., D.L. Roberts, and J.M. Reed. 2010. Estimated dates of recent extinctions for North American and Hawaiian birds. Biological Conservation 143: 617-624.</p> <p>The original version (Version 1) used in the paper was published as supplemental material to the paper and is available at:</p> <p>https://www.sciencedirect.com/science/article/pii/S0006320709004911?via%3Dihub#app1</p> <p>This updated version (Version 2) was previously posted online at:</p> <p>http://hydrodictyon.eeb.uconn.edu/people/birdlab/Elphick_et_al_2010_supplement_18_Jan_10_update.pdf</p> <p>The table Sighting data used in analyses for Elphick et al. 2010. The citation number for each year within a sighting record is given by the superscript. We include data for species (or populations) that fit our analysis goals, but for which too few data were available to analyze in the hopes that others will be able to find additional sighting data. This data set is modified from that originally published on-line to incorporate additional information that has subsequently come to light (changes highlighted in blue). No changes in the statistical results that arose from the inclusion of new information (see bottom of the document).</p> <p>Yes, it's annoying its a .doc file (sorry - expediency is winning here ...)</p>
LDhat coalescent likelihood lookup table for 320 sequences with theta = 0.01
<p>The LDhat `complete` tool uses the method of Fearnhead and Donnelly (2001) to calculate coalescent likelihoods (via importance sampling) for all possible two-locus haplotype configurations for a given number of sequences for a grid of recombination rate (rho) values. See the <a href="https://github.com/auton1/LDhat">LDhat documentation</a>. We considered the case of 320 sequences, for which there are 708400 configurations.</p> <p>Generating this table was computationally expensive (approximately 60,000 CPU hours), hence the interest in sharing it for reuse. The included sbatch job script illustrates how the computations were done (with GNU Parallel). The script could be adapted to compute a similar table on Linux clusters that use Slurm for cluster management/job scheduling.</p>
A minimal yet flexible likelihood framework to assess correlated evolution
<p>An evolutionary process is reflected in the sequence of changes through time of any trait (e.g. morphological, molecular). Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. A wide range of parametric methods have previously been proposed to detect correlated evolution but they often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In its current implementation, the method takes as input a phylogenetic tree together with mapped discrete evolutionary events on it and then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power when compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 γ-enterobacteria. We show that even with datasets of less than 100 species, the method performs well in parameter estimation and in the selection of evolutionary scenario.</p>
Supplementary figures for SpeciesTopoTestR: likelihood-based tests of species trees
<p class="MsoBodyText"><span><span><span><span><span><span><span><span><span><span><span>Likelihood-based tests of phylogenetic trees are a foundation of modern systematics, and examples of such tests are among the most widely-referenced scientific literature of all time. Over the past decade, an enormous wealth and diversity of model-based approaches have been developed for phylogenetic inference of both gene trees and species trees. However, while many techniques exist for conducting formal likelihood-based tests of gene trees, such frameworks are comparatively underdeveloped and underutilized for testing species-level hypotheses. To date, widely-used tests of tree topology are designed to assess the fit of classical models of molecular sequence data and individual gene trees, and thus, are not readily applicable to the problem of species tree inference. We derive several analogous likelihood-based approaches for testing species-level topologies using modern species tree models and algorithms for maximum likelihood estimation under the multispecies coalescent. For the purpose of comparing support for species-level relationships, these tests leverage the statistical procedures of their original gene tree-based counterparts that have a long history for testing phylogenetic hypotheses at single loci. We discuss and demonstrate a number of applications, limitations, and important considerations of these tests using simulated and empirical phylogenomic datasets that include both bifurcating topologies and reticulate network models of species relationships. Finally, we introduce the open-source R package <i>SpeciesTopoTestR </i>(<u>Species</u> <u>Topo</u>logy <u>Test</u>s in <u>R</u>) that includes a suite of functions for conducting formal likelihood-based tests of species topologies. </span></span></span></span></span></span></span></span></span></span></span></p>
FIGURE 1. Maximum likelihood tree for cytb and ITS2 in Contributions to Disholcaspis Dalla Torre And Kieffer (Hymenoptera: Cynipidae: Cynipini)
FIGURE 1. Maximum likelihood tree for cytb and ITS2, for known species of Disholcaspis and unidentified specimens. Names starting with "D_" represent specimens from Nicholls et al. (2017). Names starting with USNM are newly collected; those in blue (only) can be assigned to previously-described species based on phylogenetic placement, genetic distances, and host plant data. Bootstrap values above 50% are shown to the left of the nodes.
FIGURE 1. Maximum Likelihood tree showing phylogenetic relationships among 124 in New insights on the systematics and reproductive behaviour in tree frogs of the genus Feihyla, with description of a new related genus from Asia (Anura, Rhacophoridae)
FIGURE 1. Maximum Likelihood tree showing phylogenetic relationships among 124 representative taxa from all recognised genera of the subfamily Rhacophorinae. Relationships are inferred based on 1,937 bp of mitochondrial (12SrRNA, tRNAVAL, 16SrRNA) and nuclear (RHO and RAG1) genes. Numbers above and below the branches indicate Bayesian Posterior Probabilities and RAxML bootstrap support values, respectively.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.