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16 results for “Phylodynamics”
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
<p>The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees.</p> <p>We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (<a href="https://github.com/evolbioinfo/bdei">PyBDEI</a>) using a combination of numerical analysis methods for efficient equation resolution. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in <a href="http://www.beast2.org/">BEAST2</a> (mtbd package) and a deep learning estimator <a href="https://github.com/evolbioinfo/phylodeep">PhyloDeep</a>: the parameter values estimated by the 3 tools.<br><br>The PyBDEI and the theoretical findings behind it are described in A Zhukova, F Hecht, Y Maday, and O Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023. This dataset contains the online Appendix (Fig S1-S3 and Table S1).</p>
COVFlow: performing virus phylodynamics analyses from selected SARS-CoV-2 genome sequences
<p>This upload contains pipeline configuration files, output data, scripts and data identifiers (GISAID EPI_ISL_ID) required to reproduce the results of the article entitled "COVFlow: performing virus phylodynamics analyses from selected SARS-CoV-2 genome sequences".</p>
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
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Supplement to Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction
<p>This repository contains the supplementary files for:</p> <p>Allen BJ, Volkova Oliveria MV, Stadler T, Vaughan TG, Warnock RCM. 2024. Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction. Cambridge Prisms: Extinction.</p> <p><strong>Description of files</strong></p> <p>This repository contains the cleaned tree files, tables of age constraints, XML files for running the analyses in BEAST2, and R code to process the datasets.</p> <p>Benson1clean.tree, Benson2clean.tree, Lloyd1clean.tree, Lloyd2clean.tree - the cleaned tree files containing the phylogenies inputted into BEAST2, with tip names matched to age data from the Paleobiology Database</p> <p>Tree modifications log.xlsx - a description of the modifications made to each input phylogeny compared to their state in the data supplement of their original papers</p> <p>dinosaur_ages.csv - the raw dinosaur age data downloaded from the Paleobiology Database</p> <p>tip_constraints.csv - the dinosaur age data converted into a format to input into BEAST2, to provide age constraints for tips</p> <p>change_times.txt - a text file describing the change times for the piecewise constant trajectories in the two phylodynamic analyses</p> <p>BDSKY.xml, PiecewiseCoalescent.xml - XML files describing the BEAST2 configuration for each of the two phylodynamic models</p> <p>BDSKY_logs.zip, BDSKY_trees.zip, Coalescent_logs.zip, Coalescent_trees.zip - compressed folders containing the output log files and inferred phylogenies for each of the 16 analysed phylogenies</p> <p>Supplementary_tables.zip - Tables containing summary statistics for each of the parameters inferred in each of the models</p> <p><strong>Description of R code</strong></p> <p>The R code is subdivided into the following files:</p> <p>Wrangle_trees.R - code for checking, cleaning, and splitting the phylogenies used in the analyses</p> <p>PBDB_tip_constraints.R - code for converting the raw age data from the Paleobiology Database into a format ready for input into BEAST2 as tip constraints</p> <p>BDSKY_post_processing.R, Coalescent_post_processing.R - code for cleaning and plotting data from the BEAST2 log files, for each of the two phylodynamic models</p>
Interactive VISION reports for "KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"
<p>This repository contains VISION reports for the KP-Tracer tumors described in the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al <em>bioRxiv</em> 2021).</p> <p>In this study, single-cell lineage tracing was performed in the <em>KP</em> autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with <em>Cre</em> recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above.</p> <p>For interactive work with these objects, users must first install VISION from Github (https://github.com/YosefLab/VISION) and then launch these reports either locally or on a server. For detailed instructions on how to launch reports, and create new reports for additional clones, please view our tutorials in our public reproducibility repository at https://github.com/mattjones315/KPTracer-release. </p>
Processed data for KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"
<p>This repository contains processed data associated with the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al <em>bioRxiv</em> 2021).</p> <p>In this study, single-cell lineage tracing was performed in the <em>KP</em> autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with <em>Cre</em> recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above. </p> <p>Data here represents the derived processed data from our analysis. The specific contents are described in a README contained within the repository.</p> <p>Briefly, however, we have provided here processed AnnData objects for the KP data (sgNT) and the integrated data across three genotypes (KP, KPA, and KPL). We also provide the gene lists associated with fitness, expansion annotations for each tree, as well as plasticity scores amongst other items. </p> <p>For code used in this study, we additionally have provided a reproducibility repository at https://github.com/mattjones315/KPTracer-release. </p>
PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies
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Data from: Adaptive evolution and environmental durability jointly structure phylodynamic patterns in avian influenza viruses
Avian influenza viruses (AIVs) have been pivotal to the origination of human pandemic strains. Despite their scientific and public health significance, however, there remains much to be understood about the ecology and evolution of AIVs in wild birds, where major pools of genetic diversity are generated and maintained. Here, we present comparative phylodynamic analyses of human and AIVs in North America, demonstrating (i) significantly higher standing genetic diversity and (ii) phylogenetic trees with a weaker signature of immune escape in AIVs than in human viruses. To explain these differences, we performed statistical analyses to quantify the relative contribution of several potential explanations. We found that HA genetic diversity in avian viruses is determined by a combination of factors, predominantly subtype-specific differences in host immune selective pressure and the ecology of transmission (in particular, the durability of subtypes in aquatic environments). Extending this analysis using a computational model demonstrated that virus durability may lead to long-term, indirect chains of transmission that, when coupled with a short host lifespan, can generate and maintain the observed high levels of genetic diversity. Further evidence in support of this novel finding was found by demonstrating an association between subtype-specific environmental durability and predicted phylogenetic signatures: genetic diversity, variation in phylogenetic tree branch lengths, and tree height. The conclusion that environmental transmission plays an important role in the evolutionary biology of avian influenza viruses—a manifestation of the "storage effect"—highlights the potentially unpredictable impact of wildlife reservoirs for future human pandemics and the need for improved understanding of the natural ecology of these viruses.
Data from: Swapping birth and death: symmetries and transformations in phylodynamic models
Stochastic birth--death models provide the foundation for studying and simulating evolutionary trees in phylodynamics. A curious feature of such models is that they exhibit fundamental symmetries when the birth and death rates are interchanged. In this paper, we first provide intuitive reasons for these known transformational symmetries. We then show that these transformational symmetries (encoded in algebraic identities) are preserved even when individuals at the present are sampled with some probability. However, these extended symmetries require the death rate parameter to sometimes take a negative value. In the last part of this paper, we describe the relevance of these transformations and their application to computational phylodynamics, particularly to maximum likelihood and Bayesian inference methods, as well as to model selection.
Bayesian phylodynamic and phylogeographic analyses of invasive, hypervirulent Streptococcus agalactiae sequence type 283 dataset and R code
<p>Supplementary dataset S1, R code, and subsampled trees</p>
Data from: Swapping birth and death: symmetries and transformations in phylodynamic models
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Data from: Adaptive evolution and environmental durability jointly structure phylodynamic patterns in avian influenza viruses
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Data from: The contrasting phylodynamics of human influenza B viruses
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Data from: Phylodynamic model adequacy using posterior predictive simulations
Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy (TMA), a package for the popular BEAST2 software, that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analysing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data was best described by the birth-death SIR model.
Data from: Phylodynamic model adequacy using posterior predictive simulations
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Simulation files - Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021
<p>Simulation XML files and log files for scenarios 1–10. </p> <p>Ariane Weber, Sanni Översti, Denise Kühnert</p> <p>"Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021" </p> <p><em>Virus Evolution</em>, Volume 9, Issue 2, 2023, vead070, https://doi.org/10.1093/ve/vead070 </p>
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.