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92 results for “genomic data integration”
Data for manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry
<p>Support manuscript: A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry. </p> <p>Dataset includes 1) model and analysis, and 2) supplemental data. </p> <p>In the "model_analysis" file, we include the ecosys model source code, the modeling runs, and the modeling results. The detailed introduction is in README.md file. </p> <p><strong>Acknowledgments</strong></p> <p>We thank the EMERGE Biology Integration Institute Coordinators (members listed in Supplementary Information) for project guidance and management. This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL "Microbes Persist" Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory. </p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). </p> <p><strong>Full list of the EMERGE Biology Integration Institute Coordinators and Affiliations</strong></p> <p>Eoin L. Brodie1,2, Sarah C. Bagby3, Jeffrey P. Chanton4, Jessica G. Ernakovich5, Regis Ferriere6,7, Suzanne B. Hodgkins8, William J. Riley1, Virginia I. Rich8,9, Scott R. Saleska6, Matthew B. Sullivan8,9,10, Ruth K. Varner11, Gene W. Tyson12, Malak M. Tfaily13, Ahmed A. Zayed8,9<br> 1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Berkeley, CA 94720, USA.<br>2Department of Environmental Science, Policy and Management, University of California; Berkeley, CA 94720, USA.<br>3Department of Biology, Case Western Reserve University; Cleveland, OH, USA, 44106<br>4Earth Ocean and Atmospheric Sciences, Florida State University; Tallahassee, FL, USA<br>5Department of Natural Resources and the Environment, University of New Hampshire;<br>Durham, NH, USA 03824<br>6Department of Ecology and Evolutionary Biology, University of Arizona; Tucson, AZ,<br>85721, USA<br>7Institut de Biologie de l’ENS, Université Paris Sciences & Lettres; Paris, 75005, France<br>8Department of Microbiology, The Ohio State University; Columbus, OH, USA, 43210<br>9Center of Microbiome Science, The Ohio State University; Columbus, Ohio 43210, USA.<br>10Department of Civil, Environmental and Geodetic engineering, The Ohio State University; Columbus, Ohio 43210, USA.<br>11Department of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New Hampshire; Durham, NH 03824, USA.<br>12Centre for Microbiome Research, School of Biomedical Sciences, Queensland University<br>of Technology (QUT), Translational Research Institute; Woolloongabba, QLD, Australia<br>13Department of Environmental Science, University of Arizona; Tucson, AZ, 85721, USA</p>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
Phylogenomics and biogeography of Torreya (Taxaceae) – Integrating data from three organelle genomes, morphology, and fossils and a practical method for reducing missing data from RAD-seq
<p><span>Restriction site-associated DNA sequencing (RAD-seq) enables obtaining thousands of genetic markers for phylogenomic studies. However, RAD-seq data are subject to allele dropout (ADO) due to polymorphisms at enzyme cutting sites. We developed a new pipeline, RADADOR, to mitigate the ADO in outgroups by recovering missing loci from previously published transcriptomes in our study of a gymnosperm genus </span><em>Torreya</em><span>. Using the supplemented RAD-seq data in combination with plastome and mitochondrial gene sequences, morphology, and fossil records, we reconstructed the phylogenetic and biogeographic histories of the genus and test hypotheses on diversity anomaly in eastern Asian-North American floristic disjunction. Our results showed that our pipeline recovered many loci missing from the outgroup, and the improved data yielded a more robust phylogeny for </span><em>Torreya</em><span>. Using the fossilized-birth-death model and divergence-extinction-cladogenesis method we resolved detailed biogeographic history of </span><em>Torreya</em><span> that suggested a Jurassic origin in the Laurasia and differential speciation and extinction among continents accounting for the modern diversity anomaly biased toward Eastern Asia (EA). The history also supported a vicariance origin of the modern </span><em>Torreya</em><span> from a widespread ancestor in EA and NA in the mid-Eocene, cross-Beringia exchange in the early Paleogene before the vicariant isolation, in contrast to the "Out of NA" pattern common to gymnosperms and in contrast to the "Out of EA" hypothesis previously proposed for the genus. Furthermore, we observed phylogenetic discordance between the nuclear and plastid phylogenies on </span><em>T. jackii</em><span>, suggesting differential lineage sorting of plastid genomes among </span><em>Torreya</em><span> species or plastid genome capture in </span><em>T. jackii</em><span>.</span></p>
Integrated data-driven reannotation of the Kluyveromyces marxianus genome reveals an expanded protein coding repertoire
<p>Supplementary data for Fenton et al. 2022. </p> <table> <tbody> <tr> <td>Supplementary Table</td> <td>ID</td> <td>Table Description</td> </tr> <tr> <td>supplementary table 1</td> <td>S1</td> <td>Transcript Start Site (TSS) metrics</td> </tr> <tr> <td>supplementary table 2</td> <td>S2</td> <td>Polyadenylation Site (PAS) metrics</td> </tr> <tr> <td>supplementary table 3</td> <td>S3</td> <td>NTE candidates </td> </tr> <tr> <td>supplementary table 4</td> <td>S4</td> <td>MTS candidates</td> </tr> <tr> <td>supplementary table 5</td> <td>S5</td> <td>iORFs candidates</td> </tr> <tr> <td>supplementary table 6</td> <td>S6</td> <td>uORFs candidates</td> </tr> <tr> <td>supplementary table 7</td> <td>S7</td> <td>ouORFs candidates</td> </tr> <tr> <td>supplementary table 8</td> <td>S8</td> <td>aORFs candidates</td> </tr> <tr> <td>supplementary table 9</td> <td>S9</td> <td>tRNA copy numbers</td> </tr> <tr> <td>supplementary table 10</td> <td>S10</td> <td>novel gene periodicity scores</td> </tr> <tr> <td>supplementary table 11</td> <td>S11</td> <td>description of novel genes</td> </tr> <tr> <td>supplementary table 12</td> <td>S12</td> <td>comparison of published genomes</td> </tr> <tr> <td>supplementary table 13</td> <td>S13</td> <td>table corrections</td> </tr> <tr> <td>supplementary table 14</td> <td>S14</td> <td>start codon corrections</td> </tr> <tr> <td>supplementary table 15</td> <td>S15</td> <td>Genes with splicing (at least one intron)</td> </tr> </tbody> </table>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Phylogenomics and biogeography of Torreya (Taxaceae) – Integrating data from three organelle genomes, morphology, and fossils and a practical method for reducing missing data from RAD-seq
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Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
Open the record for dataset details and reuse information.
Data from: Integrative genomic analysis in African American children with asthma finds 3 novel loci associated with lung function
<p>Bronchodilator drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of bronchodilator medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken prior to bronchodilator medication has been extensively studied, and the genetics of the bronchodilator response itself has received some attention. However, few studies have focused on the genetics of post-bronchodilator lung function. To address this gap, we analyzed lung function phenotypes in 1,103 subjects from the Study of African Americans, Asthma, Genes, and Environment (SAGE), a pediatric asthma case-control cohort, using an integrative genomic analysis approach that combined genotype, locus-specific genetic ancestry, and functional annotation information. We integrated genome-wide association study (GWAS) results with an admixture mapping scan of three pulmonary function tests (FEV1, FVC, and FEV1/FVC) taken before and after albuterol bronchodilator administration on the same subjects, yielding six traits. We identified 18 GWAS loci, and 5 additional loci from admixture mapping, spanning several known and novel lung function candidate genes. Most loci identified via admixture mapping exhibited wide variation in minor allele frequency across genotyped global populations. Functional fine-mapping revealed an enrichment of epigenetic annotations from peripheral blood mononuclear cells, fetal lung tissue, and lung fibroblasts. Our results point to three novel potential genetic drivers of pre- and post-bronchodilator lung function: ADAMTS1, RAD54B, and EGLN3. </p>
Input data of manuscript "CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells"
<p>This is the directory containing input data necessary to reproduce analyses presented in the manuscript:</p> <blockquote> <p><strong>CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells</strong><br> Shadi Darvish Shafighi, Szymon M Kiełbasa, Julieta Sepúlveda Yáñez, Ramin Monajemi, Davy Cats, Hailiang Mei, Roberta Menafra, Susan Kloet, Hendrik Veelken, Cornelis A.M. van Bergen, Ewa Szczurek</p> </blockquote>
FISH datasets used in Zou et al. integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure
<p>This upload contains the FISH datasets used in Zou et al. integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure.</p> <p>If you use the datasets, we would be grateful if you cited the following paper:</p> <p>Zou, C., Zhang, Y., Ouyang, Z. (2016) HSA: integrating multi-track Hi-C data for genome-scale reconstruction of 3D chromatin structure. Genome Biology, 17: 40.</p>
VCF files and regression analyses for: Assessing fine-scale pondscape connectivity with amphibian eyes: an integrative approach using genomic and capture-mark-recapture data
<p><span>In the face of habitat loss, preserving functional connectivity is essential to maintain genetic diversity and the demographic dynamics required for the viability of biotic communities. This requires knowledge of the dispersal behavior of target species, which can be modeled as kernels, or probability density functions of dispersal distances at increasing geographic distances. We present an integrative approach to investigate the relationships between genetic connectivity and demographic parameters in organisms with low vagility focusing on five syntopic pond-breeding amphibians. We genotyped 1,056 individuals of two anuran and three urodele species (1,732–3,913 SNPs per species) from populations located in a landscape comprising 64 ponds to characterize fine-scale genetic structure in a comparative framework and combined this genetic data with information obtained in a previous two-year capture-mark-recapture (CMR) study. Specifically, we contrasted graphs reconstructed from genomic data with connectivity graphs based on dispersal kernels and demographic information obtained from CMR data from previous studies and assessed the effects of population size, population density, geographical distances, inverse movement probabilities and the presence of habitat patches potentially functioning as stepping stones on genetic differentiation. Our results suggest a significant influence of local population sizes on patterns of genetic connectivity at small spatial scales. In addition, m</span><span>ovement records and cluster-derived kernels provide robust inferences on most likely dispersal paths that are consistent with </span><span>genomic inferences on genetic connectivity. The integration of genetic and CMR data holds great potential for understanding genetic connectivity at spatial scales relevant to individual organisms, with applications for the implementation of management actions at the landscape level. </span></p>
Data from: Pan-evolutionary and regulatory genome architecture delineated by integrated macro- and microsynteny approach
<p>Based on the published algorithms or tools developed by our and other groups, we introduce a detailed protocol for the most comprehensive and up-to-date genome synteny pipeline (called PanSyn) and provides step-by-step instructions as well as application examples for demonstrating how to use it. PanSyn pipeline includes three major modules (microsynteny analysis, macrosynteny analysis, and integrated micro & macro analysis). PanSyn not only fills a gap of lacking a user-friendly, highly-customized tool for genome macrosynteny analysis but also allows for integrated pan-evolutionary and regulatory analysis of genome microsyntenty and macrosynteny which are not yet available in any public synteny software or tools. PanSyn has been tested under Linux system. PanSyn has multiple subroutines. Users only need to simply modify the configuration file and corresponding command parameters to execute them. Outputs include vector diagrams that are suitable for custom modification. <br> </p>
Data from: Genomic integrity of Phyciodes butterfly species in a region of contact (Lepidoptera: Nymphalidae)
<p>Crescent butterflies of the <em>Phyciodes tharos </em>group have a long-standing reputation for taxonomic difficulty.<em> </em>We assessed species boundaries in the <em>P. tharos </em>group using genome-wide SNP data, focusing on sampling in western Canada where four species (<em>P. tharos</em>, <em>P. cocyta</em>, <em>P. pulchella,</em> and <em>P. batesii</em>) have classically been recognized. Phylogenetic and cluster-based analyses confirm that there are indeed four species based on the maintenance of genomic integrity in the presence of occasional hybridization and low levels of gene flow. Mitochondrial COI does not consistently distinguish species, with haplotypes extensively shared between species. Here, we provide data matrices and tree files from the phylogenetic and cluster-based analyses.</p>
Supplementary data for: From genomics to integrative taxonomy? The case study of Pocillopora corals
<p><span>With the advent of genomics, sequencing thousands of loci from hundreds of individuals now appears feasible at reasonable costs, allowing complex phylogenies to be resolved. This is particularly relevant for cnidarians, for which insufficient data due to the small number of currently available markers, coupled with difficulties in inferring gene trees and morphological incongruences, encrypts species boundaries, thereby blurring the study and conservation of these organisms. Yet, can genomics alone be used to delimit species in an integrative taxonomic context? Here, focusing on the coral genus <em>Pocillopora</em>, which plays key roles in Indo-Pacific reef ecosystems but has challenged taxonomists for decades, we explored and discussed the usefulness of multiple criteria (genetics, morphology, biogeography and symbiosis ecology) to delimit species of this genus. Phylogenetic inferences, clustering approaches and species delimitation methods based on genome-wide single-nucleotide polymorphisms (SNPs) were first used to resolve <em>Pocillopora</em> phylogeny and propose genomic species hypotheses from 356 colonies sampled across the Indo-Pacific (western Indian Ocean, tropical southwestern Pacific and south-east Polynesia). These species hypotheses were then compared to previous genetic evidences, as well as to evidences based on morphology, biogeography and symbiosis. Genomics allowed to delimit 21 species hypotheses where only seven are currently recognised based on current taxonomy. Moreover, 13 species were strongly supported by all approaches, either confirming their currently recognised species status, or supporting the presence of new species that need to be formally described. Some of the other genomic species hypotheses were supported by biogeographic or symbiosis evidences, but additional investigations are needed to state on their species status. Altogether, our results support (1) the obsolescence of macromorphology (i.e., overall colony and branches shape) but the relevance of micromorphology (i.e., corallite structures) to refine <em>Pocillopora</em> species limits, (2) the need to identify molecularly species prior to their study, as morphology can blur species identification on the field, (3) the relevance of the mtORF (coupled with other markers in some cases) as a diagnostic marker of most species, and (4) the need for a taxonomical revision in the <em>Pocillopora</em> genus. These results give new insights into the usefulness of multiple criteria for resolving <em>Pocillopora</em> species limits and will ultimately provide helpful insights for the conservation of the species from this scleractinian genus.</span></p>
Data and scripts for the manuscript of svaRetro and svaNUMT: modular packages for annotating retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data
<p>This upload include data and scripts supporting the results described in the manuscript of <em>svaRetro and svaNUMT: modular packages for annotating retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data</em><em>. </em>Detailed description of the contents can be found in README.txt.</p>
Concatenated SNP data: Integrative taxonomy of the lizards Cercosaura ocellata species complex (Reptilia: Gymnophthalmidae) based on morphological and genomic data
<p>Concatenated unliked SNP data in phylip format used in 'Integrative taxonomy of the lizards Cercosaura ocellata species complex (Reptilia: Gymnophthalmidae) based on morphological and genomic data' study.</p>
Raw genomic RadSeq data from: Integrative taxonomy of the lizards Cercosaura ocellata species complex (Reptilia: Gymnophthalmidae) based on morphological and genomic data
<p>Raw genomic RadSeq data from individuals used in 'Integrative taxonomy of the lizards Cercosaura ocellata species complex (Reptilia: Gymnophthalmidae) based on morphological and genomic data' study.</p>
RatXcan: A framework for cross-species integration of genome-wide association and gene expression data
<p>Data for paper</p> <p><span>RatXcan: A framework for cross-species integration of genome-wide association and gene expression data</span></p> <p><span>Natasha Santhanam</span><span><span>1</span></span><span><span>†</span></span><span>, Sandra Sanchez-Roige</span><span><span>2,3,4</span></span><span><span>†</span></span><span>, Sabrina Mi</span><span><span>2</span></span><span>, Yanyu Liang</span><span><span>1</span></span><span>, Apurva S. Chitre</span><span><span>2</span></span><span>, Daniel Munro</span><span><span>2</span></span><span>, Denghui Chen</span><span><span>2</span></span><span>, Riyan Cheng</span><span><span>2</span></span><span>, Jianjun Gao</span><span><span>2</span></span><span>, Angel Garcia-Martinez</span><span><span>6</span></span><span>, Anthony M. George</span><span><span>5</span></span><span>, Alexander F. Gileta</span><span><span>2</span></span><span>, Wenyan Han</span><span><span>6</span></span><span>, Katie Holl</span><span><span>7</span></span><span>, Alesa Hughson</span><span><span>8</span></span><span>, Christopher P. King</span><span><span>9</span></span><span>, Alexander C. Lamparelli</span><span><span>9</span></span><span>, Connor D. Martin</span><span><span>5</span></span><span>, Festus Nyasimi</span><span><span>1</span></span><span>, Celine L. St. Pierre</span><span><span>2</span></span><span>, Sarah Sumner</span><span><span>1</span></span><span>, Jordan Tripi</span><span><span>9</span></span><span>, Tengfei Wang</span><span><span>6</span></span><span>, Hao Chen</span><span><span>6</span></span><span>, Shelly Flagel</span><span><span>8</span></span><span>, Keita Ishiwari</span><span><span>5,10</span></span><span>, Paul Meyer</span><span><span>5,9</span></span><span>, Oksana Polesskaya</span><span><span>2</span></span><span>, Laura Saba</span><span><span>11</span></span><span>, Leah C. Solberg Woods</span><span><span>12</span></span><span>, Abraham A. Palmer</span><span><span>2,3</span></span><span>*, Hae Kyung Im</span><span><span>1</span></span><span>*</span></p> <p><span> </span></p> <p><span>[1] Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA</span></p> <p><span>[2] Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[3] Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, 92093, USA</span></p> <p><span>[4] Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA</span></p> <p><span>[5] University at Buffalo, Clinical and Research Institute on Addictions University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[6] University of Tennessee Health Science Center, Department of Pharmacology, Addiction Science and Toxicology, Memphis, TN, 38120, USA</span></p> <p><span>[7] Medical College of Wisconsin, Department of Pediatrics, Milwaukee, WI, 53226, USA</span></p> <p><span>[8] University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA</span></p> <p><span>[9] University at Buffalo, Department of Psychology, Buffalo, NY, 14260, USA</span></p> <p><span>[10] University at Buffalo, Pharmacology and Toxicology University at Buffalo, Buffalo, NY, 14203, USA</span></p> <p><span>[11] University of Colorado Anschutz Medical Campus, Department of Pharmaceutical Sciences, Aurora, CO 80045, USA</span></p> <p><span>[12] Wake Forest University School of Medicine, Department of Internal Medicine, Winston-Salem, NC, 27157, USA</span></p>
Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone
Hybrid zones provide unique opportunities to examine reproductive isolation and introgression in nature. We utilized 45,384 Single Nucleotide Polymorphism (SNP) loci to perform association mapping of 14 floral, vegetative, and ecological traits that differ between Iris hexagona and Iris fulva, and to investigate, using a Bayesian Genomic Cline (BGC) framework, patterns of genomic introgression in a large and phenotypically diverse hybrid zone in southern Louisiana. Many loci of small effect-size were consistently found to be associated with phenotypic variation across all traits, and several individual loci were revealed to influence phenotypic variation across multiple traits. Patterns of genomic introgression were quite heterogeneous throughout the Louisiana Iris genome, with I. hexagona alleles tending to be favored over those of I. fulva. Loci that were found to have exceptional patterns of introgression were also found to be significantly associated with phenotypic variation in a small number of morphological traits. However, this was the exception rather than the rule, as most loci that were associated with morphological trait variation were not significantly associated with excess ancestry. These findings provide insights into the complexity of the genomic architecture of phenotypic differences and are a first step towards identifying loci that are associated with both trait variation and reproductive isolation in nature.
Data from: Local adaptation in mainland anole lizards: Integrating population history and genome-environment associations
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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.