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448 results for “phylogenetic inference”
CLDF dataset derived from Satterthwaite-Phillips' "Phylogenetic Inference of the Tibeto-Burman Languages" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Satterthwaite-Phillips, Damian (2011) Phylogenetic inference of the Tibeto-Burman languages or on the usefuseful of lexicostatistics (and "megalo"-comparison) for the subgrouping of Tibeto-Burman. Stanford: Stanford University.</p> </blockquote>
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. 2 in Phylogenetic Relationships Of Malayan And Malagasy Pygmy Shrews Of The Genus Suncus (Soricomorpha: Soricidae) Inferred From Mitochondrial Cytochrome B Gene Sequences
Fig. 2. The neighbour-joining (A) and Bayesian (B) trees for Suncus inferred from 1140 base-pairs of cytochrome b gene sequence. Bootstrap and posterior probability values are given above branches.
Fig. 1 in Phylogenetic Relationships Of Malayan And Malagasy Pygmy Shrews Of The Genus Suncus (Soricomorpha: Soricidae) Inferred From Mitochondrial Cytochrome B Gene Sequences
Fig. 1. Male Malayan pygmy shrew (Suncus malayanus) captured in the Cameron Highlands, Pahang, Peninsular Malaysia, in a pitfall trap set on the forest floor. Notice the characteristic large ears and dark fine pelage.
Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
<p>Phylogenetic reconstruction and introgression detection rely on an assumption about the probability distribution of gene tree topologies. Recently, evidence has emerged that population size differences can affect the probability distribution of gene tree topologies in the presence of purifying selection. Here, using the population genetic simulator SLiM, we provide evidence that in the presence of purifying selection, population size differences can lead to biases in phylogenetic inference. We also provide evidence that in the presence of purifying selection, population size differences can cause statistics used for introgression detection to exhibit patterns resembling those caused by introgression. In addition, we present a theoretical analysis showing that the occurrence of population size–dependent gene tree distributions is an inherent consequence of purifying selection. Our work underscores the importance of considering the potential confounding effect of purifying selection on phylogenetic inference and introgression detection.</p>
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.
The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2
<p>Bayesian phylogenetic inference requires a tree prior, which models the underlying diversification process which gives rise to the phylogeny. Existing birth-death diversification models include a wide range of features, for instance lineage-specific variations in speciation and extinction rates. While across-lineage variation in speciation and extinction rates is widespread in empirical datasets, few heterogeneous rate models have been implemented as tree priors for Bayesian phylogenetic inference. As a consequence, rate heterogeneity is typically ignored when reconstructing phylogenies, and rate heterogeneity is usually investigated on fixed trees. In this paper, we present a new BEAST2 package implementing the cladogenetic diversification rate shift (ClaDS) model as a tree prior. ClaDS is a birth-death diversification model designed to capture small progressive variations in birth and death rates along a phylogeny. Unlike previous implementations of ClaDS, which were designed to be used with fixed, user-chosen phylogenies, our package is implemented in the BEAST2 framework and thus allows full phylogenetic inference, where the phylogeny and model are co-estimated from a molecular alignment. Our package provides all necessary components of the inference, including a new tree object and operators to propose moves to the MCMC. It also includes a graphical interface through BEAUti. We validate our implementation of the package by comparing the produced distributions to simulated data, and show an empirical example of the full inference, using a cetaceans dataset.</p>
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
<p>Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological datasets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in datasets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological datasets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded dataset on palaeognathid birds. We find that in small, simulated datasets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger datasets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a dataset, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space.</p>
Text-fig. 6. Stratigraphic and phylogenetic placement inferred for fossil Fraxinus fruits. Only Fraxinus fossil fruits identified on the section level are included. The black color represents selected fossil fruits from published literature (excluding some Eocene North American occurrences not assigned to section), the red color represents the fossil fruits from the Lühe flora, Yunnan, Southwest China. The phylogenetic relationships are based on Hinsinger et al. (2013). in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 6. Stratigraphic and phylogenetic placement inferred for fossil Fraxinus fruits. Only Fraxinus fossil fruits identified on the section level are included. The black color represents selected fossil fruits from published literature (excluding some Eocene North American occurrences not assigned to section), the red color represents the fossil fruits from the Lühe flora, Yunnan, Southwest China. The phylogenetic relationships are based on Hinsinger et al. (2013).
CMAPLE: efficient phylogenetic inference in the pandemic era
<p>This supplementary data contains testing scripts and input/output data for benchmarking, validating, and assessing the code quality of CMAPLE.</p>
Fig. 1 in Echinoderm model systems, homology, and phylogenetic inference: comment and reply to Paul (2021)
Fig. 1. Tree comparison between two phylogenetic inference methods with bootstrap support at the nodes. A. Phylogenetic hypothesis from Paul (2021) inferred via maximum parsimony. B. Phylogenetic hypothesis inferred via maximum likelihood.
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.
Figure 3. Phylogenetic tree inferred from cytochrome B in Phlebotomus (Paraphlebotomus) chabaudi and Phlebotomus riouxi: closely related species or synonyms?
Figure 3. Phylogenetic tree inferred from cytochrome B data of Phlebotomus chabaudi and Ph. riouxi specimens. We added to the analysis the sequences of Ph. chabaudi published by Tabbabi et al. (2014). The phylogram results from bootstrapped data sets obtained using the PhyML 3.0 program [21] using GTR (general time reversible) + G distribution (gamma distribution of rates with four rate categories). The tree was visualized using the TreeDyn program, version 198.3 [7]. The percentages above the branches are the frequencies with which a given branch appeared in 500 bootstrap replications. Only bootstrap values higher than 50% on the early branches are shown. A sequence of Ph. sergenti (AF161216) was used as the outgroup. The sequences marked by * were published by Tabbabi et al. (2014); R = sequences found in specimens morphologically characterized as Ph. riouxi. C = sequences found in specimens morphologically characterized as Ph. chabaudi. RC = sequences found in specimens morphologically characterized as Ph. chabaudi or Ph. riouxi. Int = sequences found in specimens morphologically characterized as intermediate between Ph. riouxi and Ph. chabaudi.
Fig. 34. Phylogenetic network inferred from 1,476 in Morphology, Ciliary Pattern and Molecular Phylogeny of Trachelophyllum brachypharynx Levander, 1894 (Litostomatea, Haptoria, Spathidiida)
Fig. 34. Phylogenetic network inferred from 1,476 nucleotide characters of 69 litostomatean taxa, using the NeighborNet algorithm and the uncorrected distances. Numbers along the edges indicate bootstrap support values coming from 1,000 replicates. Only bootstraps> 50% and relevant to this study are shown. The scale bar indicates three substitutions per one thousand nucleotide positions.
Fig. 9 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 9. Evolutionary hypothesis of interrelationships among the four free-living litostomatean lineages studied. This scenario was suggested on the basis of morphology and the consensus secondary structure of the ITS2 molecules. CK – circumoral kinety, DB – dorsal brush, OB – oral bulge, OO – oral bulge opening, P – proboscis, PE – perioral kinety, PR – preoral kineties, SK – somatic kineties.
Fig. 5 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 5. Quartet likelihood-mapping showing distribution of phylogenetic signal in the 18S-A and the CON-1 alignment for three possible relationships among the four main free-living litostomatean lineages studied. The corners of the triangles show the percentage of fully resolved trees, i.e., phylogenetically informative signal. The rectangular areas show the percentage of trees that are in conflict. The central triangle shows the percentage of unresolved star-like trees, i.e., phylogenetically uninformative signal. Coding of free-living litostomatean lineages: H – Haptorida, P – Pleurostomatida, R – Rhynchostomatia, S – Spathidiida.
Fig. 4 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 4. Super-network of 66 free-living litostomatean taxa constructed from 80 randomly selected post-burn-in trees from the Bayesian inference of the 18S-A–D, ITSR-C and ITSR-D as well as the CON-1 and CON-2 alignments. The super-network was constructed in the program SplitsTree, using the Z-closure option, tree size weighted mean, ten runs, and the refined heuristic technique. For details on taxa and characteristics of the alignments analyzed, see Supplementary Table S1 and S2.
Fig. 3 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 3. Phylogeny based on the 18S rRNA gene and the ITS1-5.8S-ITS2 region of 56 free-living litostomatean taxa (alignment CON-1). Posterior probabilities for the Bayesian inference and bootstrap values for maximum likelihood were mapped onto the 50% majority rule ML tree. Dashes indicate posterior probabilities below 0.50 and ML bootstrap values below 50%. The scale bar indicates five substitutions per ten nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the CON-1 alignment, see Supplementary Table S1 and S2.
Fig. 1 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 1. Phylogeny based on the 18S rRNA gene of 64 free-living litostomatean taxa (alignment 18S-A). Posterior probabilities for Bayesian inference and bootstrap values for maximum likelihood were mapped onto the 50% majority rule Bayesian consensus tree. Dashes indicate ML bootstrap values below 50%. Sequences in bold were obtained during this study. The scale bar indicates two substitutions per one hundred nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the 18S-A alignment, see Supplementary Table S1 and S2.
Fig. 8 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 8. Structure logo of ITS2 helices II and III in various higher litostomatean taxa. The height of a base is proportional to its frequency in multiple sequence alignments.
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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.