Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
865
datasets available to search
ShareScore release 0.9.0
Dataset results
865 results for “population genomics”
[Dataset] Data for the course "Population Genomics" at Aarhus University
<p>Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the <a href="https://hds-sandbox.github.io/">health data science sandbox</a>. This <a href="https://hds-sandbox.github.io/PopulationGenomicsCourse/">webpage</a> shows the latest version of the course material.</p> <ol> <li> Data.tar.gz Contains the datasets and executable files for some of the softwares<br> You can unpack by simply doing<br> tar -zxf Data.tar.gz -C ./<br> This will create a folder called Data with the uncompressed material inside</li> <li>Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by <ol> <li>creating the folder Course_Env: mkdir Course_Env</li> <li>untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env</li> <li>Activate the environment: conda activate ./Course_Env</li> <li>Run the unpacking script (it can take quite some time to get it done): conda-unpack</li> </ol> </li> <li>Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.</li> <li>environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands: <ol> <li>conda env create -f environment_with_args.yml -p ./Course_Env</li> <li>conda activate ./Course_Env</li> </ol> </li> </ol> <p> </p> <p>The data is connected to the following repository:<a href="https:// github.com/hds-sandbox/Popgen_course_aarhus."> https://github.com/hds-sandbox/Popgen_course_aarhus.</a> The original course material from Prof Kasper Munch is at <a href="https://github.com/kaspermunch/PopulationGenomicsCourse">https://github.com/kaspermunch/PopulationGenomicsCourse</a>.</p> <p> </p> <p><strong>Description</strong></p> <p>The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.</p> <p>The participants must at the end of the course be able to:</p> <ul> <li>Identify an experimental platform relevant to a population genomic analysis.</li> <li>Apply commonly used population genomic methods.</li> <li>Explain the theory behind common population genomic methods.</li> <li>Reflect on strengths and limitations of population genomic methods.</li> <li>Interpret and analyze results of population genomic inference.</li> <li>Formulate population genetics hypotheses based on data</li> </ul> <p>The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.</p> <p>Curriculum</p> <p>The curriculum for each week is listed below. "Coop" refers to a set of <a href="https://github.com/cooplab/popgen-notes/releases/download/v1.2/minicoop.pdf">lecture notes by Graham Coop</a> that we will use throughout the course.</p> <p>Course plan</p> <ol> <li>Course intro and overview: <ul> <li>Coop chapters 1, 2, 3, <a href="https://www.nature.com/articles/nature18964">Paper: Genome Diversity Project</a></li> </ul> </li> <li>Drift and the coalescent: <ul> <li>Coop chapter 4; <a href="https://www.nature.com/articles/ng.3036">Paper: Platypus</a></li> <li>Exercise: Read mapping and base calling</li> </ul> </li> <li>Recombination: <ul> <li>Lecture: <a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0455">Review: Recombination in eukaryotes</a>, <a href="https://www.nature.com/articles/s41576-020-0240-1">Review: Recombination rate estimation</a></li> <li>Exercise: Phasing and recombination rate</li> </ul> </li> <li>Population strucure and incomplete lineage sorting: <ul> <li>Lecture: Coop chapter 6, <a href="https://doi.org/10.1146/annurev-genet-120213-092532">Review: Incomplete lineage sorting</a></li> <li>Exercise: Working with VCF files</li> </ul> </li> <li>Hidden Markov models: <ul> <li>Lecture: Durbin chapter 3, <a href="https://www.nature.com/articles/nature07331">Paper: population structure</a></li> <li>Exercise: Inference of population structure and admixture</li> </ul> </li> <li>Ancestral recombination graphs: <ul> <li>Lecture: <a href="https://bmcgenomdata.biomedcentral.com/articles/10.1186/1471-2156-7-16">Paper: Approximating the ARG</a>, <a href="https://www.nature.com/articles/s41588-019-0484-x">Paper: Tree inference</a></li> <li>Exercise: ARG dashboard exercises + Inference of trees along sequence</li> </ul> </li> <li>Past population demography: <ul> <li>Lecture: Coop chapter 4, <a href="https://www.nature.com/articles/nature10231">Paper: PSMC</a>, revisit <a href="https://www.nature.com/articles/s41588-019-0484-x">Paper: Tree inference</a></li> <li>Exercise: Inferring historical populations</li> </ul> </li> <li>Direct and linked selection: <ul> <li>Lecture: Coop chapters 12, 13, revisit <a href="https://www.nature.com/articles/s41588-019-0484-x">Paper: Tree inference</a></li> </ul> </li> <li>Admixture: <ul> <li>Lecture: <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007349">Review: Admixture</a>, <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007641">Paper: Admixture inference</a></li> <li>Exercise: Detecting archaic ancestry in modern humans</li> </ul> </li> <li>Genome-wide association study (GWAS): <ul> <li>Lecture: <a href="https://github.com/cooplab/popgen-notes/releases/download/v1.2/release_popgen_notes.pdf">Coop lecture notes</a> 99-120</li> <li>Exercise: GWAS quality control</li> </ul> </li> <li>Heritability: <ul> <li>Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)</li> <li>Exercise: Association testing</li> </ul> </li> <li>Evolution and disease: <ul> <li>Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)</li> <li>Exercise: Estimating heritability</li> </ul> </li> </ol>
Sponge diversification in marine lakes: implications for phylogeography and population genomic studies on sponges
<p class="MsoNormal"><span>The relative influence of geography, currents and environment on gene flow within sessile marine species remains an open question. Detecting subtle genetic differentiation at small scales is challenging in benthic populations due to large effective population sizes, general lack of resolution in genetic markers, and because barriers to dispersal often remain elusive. Marine lakes can circumvent confounding factors by providing discrete and replicated ecosystems. Using high-resolution double digest restriction-site associated DNA sequencing (4,826 Single Nucleotide Polymorphisms, SNPs), we genotyped populations of the sponge <em>Suberites diversicolor </em><span>(n=125) to test the relative importance of spatial scales (1-1,400km), local environmental regimes, and permeability of seascape barriers in shaping population genomic structure. </span>With the SNP dataset we show strong intra-lineage population structure, even at scales <10km (average F<sub>ST</sub> = 0.63), that was not detected previously using single markers. Most variation was explained by differentiation between populations (AMOVA: 48.8%) with signatures of population size declines and bottlenecks per lake. Though the populations were strongly structured, we did not detect significant effects of geographic distance, local environments, or degree of connection to the sea on population structure, suggesting mechanisms such as founder events with subsequent priority effects may be at play. We show that the inclusion of morphologically cryptic lineages that can be detected with the COI marker can reduce the obtained SNP set by almost 90%. Future work on sponge genomics should confirm that only one lineage is included. Our results call for a reassessment of poorly dispersing benthic organisms that were previously assumed to be highly connected based on low-resolution markers.</span></p>
Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years
<p>Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we developed the Temporal Population Structure (TPS), the first DNA-based dating method for ancient genomes ranging from the Late Mesolithic to modern-days, and applied it to 3,591 ancient and 1,307 modern Eurasians. We show that TPS predictions align with their known dates and correctly account for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We demonstrated how TPS improved the ability to study phenotypic traits over time.</p>
Tracing the introduction of the invasive common myna using population genomics
<p>The common myna (<em>Acridotheres tristis</em>) is one of the most invasive bird species in the world, yet its colonisation history is only partly understood. We identified the introduction history and population structure, and quantified the genetic diversity of myna populations from the native range in India and the introduced populations in New Zealand, Australia, Fiji, Hawaii, and South Africa, based on thousands of single nucleotide polymorphism markers in 814 individuals. We were able to identify the source population of mynas in several invasive locations: mynas from Fiji and Melbourne, Australia, were likely founded by individuals from a subpopulation in Maharashtra, India, while mynas in Hawaii and South Africa were likely independently founded by individuals from other localities in India. Our findings suggest that New Zealand mynas were founded by individuals from Melbourne, which, in turn, were founded by individuals from Maharashtra. We identified two genetic clusters among New Zealand mynas, divided by New Zealand's North Island's axial mountain ranges, confirming previous observations that mountains and thick forests may form barriers to myna dispersal. Our study provides a foundation for other population and invasion genomic studies and provides useful information for the management of this invasive species.</p>
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
<p>Described in 2009, the Patch-nosed Salamander (<em>Urspelerpes brucei</em>) is a miniature species of lungless salamander with a geographic range of only ~45 km<sup>2</sup>. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of <em>U. brucei</em> and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of <em>U. brucei</em>, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which <em>U. brucei</em> lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.</p>
Associated Dataset for Genome-wide DNA methylation patterns in bumble bee (Bombus vosnesenskii) populations from spatial-environmental range extremes
<p>The dataset contains the final methylation call set (n=14,627,533), variant calling file for population genomics analyses, analysis codes/scripts, and other associated files related to the research (Constitutive and variable patterns of genome-wide DNA methylation in populations from spatial-environmental range extremes of the bumble bee <em>Bombus vosnesenskii)</em>. Raw WGBS reads generated in this study have been deposited and are currently available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under NCBI BioProject PRJNA956115.</p>
High quality, chromosome-scale genome assemblies: Comparisons of three Diaphorina citri (Asian Citrus Psyllid) geographic populations
Open the record for dataset details and reuse information.
Structural genomic variation in the inbred Scandinavian wolf population contributes to the realized genetic load but is positively affected by immigration
Open the record for dataset details and reuse information.
Data from: Genomic landscapes of divergence among island bird populations: evidence of parallel adaptation but at different loci?
Open the record for dataset details and reuse information.
Population genomics reveals demographic history and climate adaptation in Japanese Arabidopsis halleri
Open the record for dataset details and reuse information.
Data from: Population genomics reveals multiple drivers of population differentiation in a sex-role-reversed pipefish
Open the record for dataset details and reuse information.
Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity
Open the record for dataset details and reuse information.
Sponge diversification in marine lakes: implications for phylogeography and population genomic studies on sponges
Open the record for dataset details and reuse information.
Population genomic signatures of the oriental fruit moth related to the Pleistocene climates
Open the record for dataset details and reuse information.
Genomic variation in the Black-throated Green Warbler (Setophaga virens) suggests divergence in a disjunct Atlantic Coastal Plain population (S. v. waynei)
Open the record for dataset details and reuse information.
Population genomics of a forest fungus reveals high gene flow and climate adaptation signatures
Open the record for dataset details and reuse information.
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
Open the record for dataset details and reuse information.
Data from: Genome wide assessment of genetic variation and population distinctiveness of the pig family in South Africa
Open the record for dataset details and reuse information.
Habitat association predicts population connectivity and persistence in flightless beetles: a population genomics approach within a dynamic archipelago
Open the record for dataset details and reuse information.
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.