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FIG. 4 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria
FIG. 4. — Juvenile to adult ontogenetic series, from left to right, of five left proximal ulnae of Didelphis virginiana; A, anterior view; B, medial view; C, lateral view. Abbreviations: HuUlm, medial humeroulnar; HuUlr, radial humeroulnar; HuUlz, zonal humeroradial; RaUlp, proximal radioulnar facet; rn, radial notch; ulalfo, ulnar annular ligamentous fossa; ulco, ulnar coronoid process; ulcolr, ulnar cranial olecranon ridge; uldtc, distal ulnar trochlear crest; ulfoa, ulnar fossa for anconeus; ulfofdp, ulnar fossa for flexor digitorum profundus; ulgr, ulnar guiding ridge; ulol, ulnar olecranon process; ulppr, ulnar proximal process; ulptc, ulnar proximal trochlear crest; ulptcl, ulnar lateral proximal trochlear crest; ulptcm, ulnar medial proximal trochlear crest; ulscs, ulnar subcutaneous surface; ultrn, ulnar trochlear notch. Scale bar: 4 mm.
FIG. 2 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria
FIG. 2. — Juvenile to adult ontogenetic series, from left to right, of five left humeri of Didelphis virginiana; A, proximal ends; B, lateral view of proximal end of largest specimen; C, medial view of proximal end of largest specimen; D, anterior view of distal ends; E, posterior view of distal ends. Abbreviations: hbgr, humeral bicipital groove; hcap, humeral capitulum; hcfo, humeral coronoid fossa; hhd, humeral head; hlec, humeral (lateral) ectepicondyle; hlecr, humeral (lateral) ectepicondylar crest; hlfcap, humeral lateral flange of capitulum; hltub, humeral lateral (greater) tuberosity; hmtub, humeral medial (lesser) tuberosity; hmec, humeral (medial) entepicondyle; hofo, humeral olecranon fossa; hplcr, humeral posterolateral crest; hpmcr, humeral posteromedial crest; hrfo, humeral radial fossa; hscf, humeral supracondyloid foramen; hscr, humeral supracondyloid ridge; htr,; HuUlz, zonal humeroulnar facet; hzcon, humeral zona conoidea; rasgr, groove for radial sesamoid. Scale bar: 4 mm.
FIG. 6 in Model-based analysis of postcranial osteology of marsupials from the Palaeocene of Itaboraí (Brazil) and the phylogenetics and biogeography of Metatheria
FIG. 6. — Details of juvenile and adult left tibiae and fibula of Didelphis virginiana; A, proximal view of juvenile tibia; B, proximal view of adult tibia; C, proximal view of adult tibia and fibula; D, distal view of adult tibia showing astragalar articulation; E, posterior view of specimen shown in D; F, anterior view of specimen shown in E. Abbreviations: ATil, lateral astragalotibial facet; ATim, medial astragalotibial facet; ATip, posterior astragalotibial facet; FeFi, femorofibular facet; FeTil, lateral femorotibial facet; FeTim, medial femorotibial facet; Fi, fibula; PfFi, parafibulofibular facet; Ti, tibia; TiFid, distal tibiofibular facet; tilc, tibial lateral condyle; tilic, tibial lateral intercondylar tubercle; timic, tibial medial condyle; tmm, tibial medial malleolus; tqfcr, tibial crest for quadratus femoris; trmgr, tibial retromalleolar groove. Scale bars: 4 mm.
Expectation-Maximization enables phylogenetic dating under a Categorical Rate Model
<div> <div> <div> <p>Dating phylogenetic trees to obtain branch lengths in the unit of time is essential for many downstream applications but has remained challenging. Dating requires inferring mutation rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a clock model that defines a distribution over rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification where the assumed parametric statistical clock model vastly differs from the true distribution. Notably, existing methods tend to assume rigid, often unimodal rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates and often leads to difficult non-convex optimization problems. To tackle these two challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the Expectation-Maximization (EM) algorithm to co-estimate rate categories and branch lengths in the time unit. Our model has fewer assumptions about the true clock model than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with nonmodal or multimodal clock models.</p> </div> </div> </div>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models
<p>Simulated datasets used in our paper "CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models" to produce figures 1bc, 1d, and 2ab. The data provided in each folder is as follows:</p> <ul> <li><strong>rate_matrices</strong> contains the classical LG rate matrix, and our 400 x 400 estimated co-evolutionary model Q2.</li> <li><strong>fig_1bc</strong> contains the simulated data used to estimate and evaluate rate matrices using the CherryML method and EM (with XRATE) as shown in Fig. 1b and c of our paper. The files and sub-directories here are: <ul> <li><strong>fig_1bc_simulated_data_families_all.txt</strong> contains the list of protein family names used to train the model. When only K families are used in Fig. 1b and c, these are the first K families of this list.</li> <li><strong>gt_tree_dir</strong> contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li><strong>msa_dir</strong> contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, without site rate variation.</li> <li><strong>gt_site_rates_dir</strong> contains the site rates used. In this case, they are all 1.</li> <li><strong>gt_likelihood_dir</strong> contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li><strong>fig_1d</strong> folder contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li><strong>gt_tree_dir</strong> contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li><strong>msa_dir</strong> contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each tree, with site rate variation.</li> <li><strong>gt_site_rates_dir</strong> contains the site rates used.</li> <li><strong>gt_likelihood_dir</strong> contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> </ul> </li> <li><strong>fig_2ab</strong> contains the simulated data used to evaluate the effect of time quantization on the CherryML method as shown in Fig. 1d of our paper. The files and sub-directories here are: <ul> <li><strong>gt_tree_dir</strong> contains the phylogenetic tree used to simulate data for each protein family. There were originally estimated running FastTree on the MSAs from the trRosetta paper, as described in our paper in detail.</li> <li><strong>msa_dir</strong> contains the simulated multiple sequence alignments (MSAs). These were simulated running the LG rate matrix down each non-contacting tree, and using Q2 for the contacting sites, all without site rate variation.</li> <li><strong>gt_site_rates_dir</strong> contains the site rates used, in this case all 1 (i.e. no site rate variation).</li> <li><strong>gt_likelihood_dir</strong> contains the log-likelihood of the original phylogenetic trees used for each family (as given by FastTree). This is irrelevant for but provided for completeness; you can safely ignore this directory.</li> <li><strong>contact_map_dir</strong> contains the simulated contact maps for each family. These were obtained by computing a maximal matching on the true contact maps derived from the trRosetta paper, as described in detail in out paper.</li> </ul> </li> </ul> <p>The exact end-to-end code which generates these simulated datasets is provided in our Github repository: <a href="https://github.com/songlab-cal/CherryML">https://github.com/songlab-cal/CherryML</a></p> <p>In fact, by default, when you try to reproduce the figures in our paper by running the `reproduce_all_figures.py` script in our repository, the data will automatically be simulated for you if it isn't already present. This can be bypassed by downloading the data here in Zenodo and changing the top of `reproduce_all_figures.py` to point to these files.</p>
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel's lambda model
<p class="MsoNormal"><span>Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman's rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel's <em>λ</em> model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, <em>R</em><sup>2</sup>, <em>p</em>-value, Pagel's <em>λ</em>, Blomberg et al.'s <em>K</em>, and the estimated <em>λ</em> in <a name="_Hlk136010442"></a>Pagel's <em>λ</em> model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher <em>λ</em></span><span> or <em>K</em> </span><span>value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.</span></p>
There and back to the present: a model-based framework to estimate phylogenetically constrained alpha diversity gradients
<p>The imprint left by niche evolution on the variation of biological diversity across spatial and environmental gradients is still debated among ecologists. Furthermore, understanding to what extent dispersal limitation may reinforce or blur such an imprint is still a gap in our ecological knowledge. In this article we introduce a simulation approach coupled to Approximate Bayesian Computation (ABC) that parameterizes both the adaptation rate of species' niche positions over the evolution of a monophyletic lineage and the intensity of dispersal limitation associated with the variation of species alpha diversity among assemblages distributed across spatial and environmental gradients. The analytical tool was implemented in the R package <em>mcfly</em>. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate the adaptation rate parameter but showed to be less precise in relation to the dispersal limitation parameter. Also, we found that increased dispersal limitation levels improved the parameterization of the adaptation rate of species' niche positions in simulated datasets. Further, we evaluated the role played by niche evolution and dispersal limitation on species alpha diversity variation of Phyllostomidae bats across the Neotropics. The framework proposed here sheds light on the links between niche evolution, dispersal limitation and gradients of biological diversity, and thereby improved our understanding of evolutionary imprints on current biological diversity patterns.</p>
phyddle: Software for exploring phylogenetic models with deep learning
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Site-specific structure and stability constrained substitution models improve phylogenetic inference
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Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel’s lambda model
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
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Empirical data for: Extending phylogenetic regression models for comparing within-species patterns across the Tree of Life
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Expectation-Maximization enables phylogenetic dating under a Categorical Rate Model
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There and back to the present: a model-based framework to estimate phylogenetically constrained alpha diversity gradients
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Data from: Phylogenetic biogeography inference using dynamic paleogeography models and explicit geographic ranges
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A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis
<p>simulation and PC64/VRC01 input/output data files</p>
Data from: Modeling phylogenetic biome shifts on a planet with a past
<p>The spatial distribution of biomes has changed considerably over deep time, so the geographical opportunity for an evolutionary lineage to shift into a new biome may depend on how the availability and connectivity of biomes has varied temporally. To better understand how lineages shift between biomes in space and time, we developed a phylogenetic biome shift model in which each lineage shifts between biomes and disperses between regions at rates that depend on the lineage's biome affinity and location relative to the spatiotemporal distribution of biomes at any given time. To study the behavior of the biome shift model in an empirical setting, we developed a literature-based representation of paleobiome structure for three mesic forest biomes, six regions, and eight time strata, ranging from the Late Cretaceous (100 Ma) through the present. We then fitted the model to a time-calibrated phylogeny of 119 <em>Viburnum</em> species to compare how the results responded to various realistic or unrealistic assumptions about paleobiome structure. Ancestral biome estimates that account for paleobiome dynamics reconstructed a warm temperate (or tropical) origin of <em>Viburnum</em>, which is consistent with previous fossil-based estimates of ancestral biomes. Imposing unrealistic paleobiome distributions led to ancestral biome estimates that eliminated support for tropical origins, and instead inflated support for cold temperate ancestry throughout the warmer Paleocene and Eocene. The biome shift model we describe is applicable to the study of evolutionary systems beyond <em>Viburnum</em>, and the core mechanisms of our model are extensible to the design of richer phylogenetic models of historical biogeography and/or lineage diversification. We conclude that biome shift models that account for dynamic geographical opportunities are important for inferring ancestral biomes that are compatible with our understanding of Earth history.</p>
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>
Supplementary material: Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians
<p><strong>Aim: </strong>Intraspecific genetic variation is key for adaptation and survival in changing environments and is known to be influenced by many factors, including population size, dispersal, and life history traits. We investigated genetic variation within Neotropical amphibian species to provide insights into how natural history traits, phylogenetic relatedness, climatic, and geographic characteristics can explain intraspecific genetic diversity.</p> <p><strong>Location:</strong> Neotropics.</p> <p><strong>Taxon:</strong> Amphibians.</p> <p><strong>Methods: </strong>We assembled datasets using open-access databases for natural history traits, genetic sequences, phylogenetic trees, climatic, and geographic data. For each species, we calculated overall nucleotide diversity (<em>π</em>) and tested for isolation by distance (IBD) and isolation by environment (IBE). We then identified predictors of <em>π</em>, IBD, and IBE using Random Forest (RF) regression or RF classification. We also fitted phylogenetic generalized linear mixed models (PGLMMs) to predict<em> π</em>, IBD, and IBE.</p> <p><strong>Results:</strong> We compiled 4,052 mitochondrial DNA sequences from 256 amphibian species (230 frogs and 26 salamanders), georeferencing 2,477 sequences from 176 species that were not linked to occurrence data. RF regressions and PGLMMs were congruent in identifying range size and precipitation (σ) as the most important predictors of <em>π</em>, influencing it positively. RF classification and PGLMMs identified minimum elevation as an important predictor of IBD; most species without IBD tended to occur at higher elevations. Maximum latitude and precipitation (σ) were the best predictors of IBE, and most species without IBE occur at lower latitudes and in areas with more variable precipitation.</p> <p><strong>Main conclusions: </strong>This study identified predictors of genetic variation in Neotropical amphibians using both machine learning and phylogenetic methods. This approach was valuable to determine which predictors were congruent between methods. We found that species with small ranges or living in zones with less variable precipitation tended to have low genetic diversity. We also showed that Western Mesoamerica, Andes, and Atlantic Forest biogeographic units harbor high diversity across many species that should be prioritized for protection. These results could play a key role in the development of conservation strategies for Neotropical amphibians.</p>
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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.