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15,277 results for “GENETIC”
Simulated genetic data in a hierarchical metapopulation structure
<p>The data are linked to a research article entitled: “<em>Interactions between microenvironment, selection and genetic architecture drive multiscale adaptation in a simulation experiment” </em>in<em> Journal of Evolutionary Biology</em> (see References).</p> <p>In this research on multiscale adaptation, we simulated a hierarchical metapopulation structure with four populations, two environments per population and three patches per environment, in a two-step procedure:</p> <ul> <li>an initialization step without selection, with eight combinations of mutation type, selfing rate and QTL number parameters (2 modes each); out of 200,000 simulated generations in each case, we chose one with appropriate characteristics as a starting point for the next step;</li> <li>a selection step with all possible combinations of the following parameters: environmental pattern (4 modes), environmental range (5 modes), selection intensity (4 modes), fecundity (3 modes).</li> </ul> <p>This resulted in 240 scenarios for each initialized metapopulation, i.e. 1,920 scenarios in total. Each scenario was replicated 10 times, i.e. 19,200 simulation runs.</p> <p>The archive includes all data needed to reproduce the simulations and analyses, or to re-use the simulated metapopulations for other analyses. It has the following structure (further detailed below):</p> <ol> <li><strong>NemoScripts directory </strong>contains the <em>Nemo </em>input files used to perform simulations for the initialization step and the selection step;</li> <li><strong>RScripts directory </strong>contains the <em>R</em> scripts to read the <em>Nemo </em>output files, compute synthetic variables(*), and produce the figures as they appear in the publication and supplementary material (*: long computations, therefore we also directly provide those synthetic variables in the Data directory);</li> <li><strong>Data directory </strong>contains the <em>Nemo </em>output files, the synthetic variables, and other data needed to reproduce the figures; this directory can be used as a working directory for the <em>R</em> scripts (recommended).</li> </ol> <p>Running the following command in a terminal <strong><em>tar –xzvf Archive_PC_SOM_IS_FL.tar</em></strong> will create a directory named <strong><em>Archive_PC_SOM_IS_FL</em></strong>, which detailed content is described in the <strong><em>README.pdf</em></strong> file.<br> Warning: the extracted archive is large (460Go, >40,000 files) and extraction may take some time.</p>
Data from: Absence of genetic isolation across highly fragmented landscape in the ant Temnothorax nigriceps
<p><strong>This README accompanies data_genotyping.txt</strong></p> <p> </p> <p><strong><em>Associate publication : </em></strong></p> <p>Absence of genetic isolation across highly fragmented landscape in the ant Temnothorax nigriceps</p> <p>M. Cordonnier<sup>a</sup>, D. Felten<sup>a</sup>, A. Trindl<sup>a</sup>, J. Heinze<sup>a</sup>*, A. Bernadou<sup>a</sup>*</p> <p><sup>a</sup>Lehrstuhl für Zoologie / Evolutionsbiologie, Univ. Regensburg</p> <p>*Equal contribution</p> <p> </p> <p>****************************** CONTENTS *******************************</p> <p>The data can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted in other ways.</p> <p> </p> <p>This file includes a description of the variables.</p> <p>***********************************************************************</p> <p>Variable names and descriptions</p> <p> </p> <p><strong>Sample:</strong> ID of the sampled nest</p> <p><strong>Location:</strong> Population of the sampled nest</p> <p> </p> <p><strong>List of genotypes </strong></p> <p>Microsatellite primers used in the study</p> <table> <tbody> <tr> <td> </td> <td> <p>Annealing temperature [°C]</p> </td> <td> <p>Orientation</p> </td> <td> <p>Sequence of primers</p> </td> </tr> <tr> <td> <p>LX GT218</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GTTCTTGCGCGGATGCATAC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-TGTACTCGCGTGTCTATCGG-3’</p> </td> </tr> <tr> <td> <p>Ant3993</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-TGATCCGCTCTTAAAATTTAGATGGA-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ACTTTCCGCRGCATTAAACATTTTCTT-3’</p> </td> </tr> <tr> <td> <p>L-18</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-TGAATTTGGATGGCGGTAGAC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ACCTAATGCACGCTTTAGAAT-3’</p> </td> </tr> <tr> <td> <p>LXA GT1</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GTGGCGACCAATTCTGCAAG-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-GCAGGACCAGCATCAAATGACAG-3’</p> </td> </tr> <tr> <td> <p>2MS17</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-CAGCCTCTATTTTGTTCGAAG-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-TTTACTGCGGCTCCATAATC-3’</p> </td> </tr> <tr> <td> <p>2MS46</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GCTCACTACTATGCTGCCAGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-CTTTCCTGCAAACCACGTGT-3’</p> </td> </tr> <tr> <td> <p>2MS60</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-TATGCGCCGGACAATAATCGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-GTTCATTGTCCGAGGCGCAGC-3’</p> </td> </tr> <tr> <td> <p>2MS67</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GAAGATTCGTCAGGATGCAGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-AACTCTCGCTGGCAAGCGAGC-3’</p> </td> </tr> <tr> <td> <p>2MS82</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-AAAAGAGCATGCAACAGGTCAGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-TTTCTTAAGTCGCAAGCGAGC-3’</p> </td> </tr> <tr> <td> <p>2MS87</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GGAACCTCACTCAACCTCGGT-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ACGCGGACTACTTTAACCGGA-3’</p> </td> </tr> <tr> <td> <p>2MS91</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-AAAGTCTCGGAGTGGCTTTGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ATTCTCGTCCATTTGTTCTAA-3’</p> </td> </tr> <tr> <td> <p>Ant11893</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-CAGGCTCGGRACGTTAATGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-GGTGCCGACGTCTAGCTAGC-3’</p> </td> </tr> </tbody> </table> <p> </p> <p>Missing data are encoded “-9”.</p> <p> </p> <p>****************************** CONTACTING *****************************</p> <p>Contact me at:</p> <p> </p> <p>Marion Cordonnier</p> <p>e-mail: marion.cordonnier@hotmail.com</p> <p> </p> <p>***********************************************************************</p> <p> </p>
First genetic data for the Critically Endangered Cuban endemic Zapata Rail Cyanolimnas cerverai, and the taxonomic implications
<p>Data associated with the publication First genetic data for the Critically Endangered Cuban endemic Zapata Rail <em>Cyanolimnas cerverai</em>, and the taxonomic implications.</p>
Non-genetically-based intraspecific differentiation for heavy metal tolerance in the copper moss Scopelophila cataractae
<p>We used next-generation sequencing to study DNA methylation and gene expression changes in plants from four clonal populations of the metallophyte moss <em>Scopelophila cataractae</em> experimentally exposed to either Cd or Cu. For this we performed reduced representation bisulfite DNA sequencing and RNA sequencing. </p>
Defining the genes required for survival of Mycobacterium bovis in the bovine host offers novel insights into the genetic basis of survival of pathogenic mycobacteria
<p>Supplementary dataset from "<strong><em>Defining the genes required for survival of Mycobacterium bovis in the bovine host offers novel insights into the genetic basis of survival of pathogenic mycobacteria</em></strong>"</p> <p> </p> <p><strong>Supplementary Figure legends</strong></p> <p><strong>Figure S1. Illustration of the transposon insertions around the <em>M. bovis </em>genome. </strong>Sequencing of the input library showed that transposon insertions were evenly distributed around the genome and 27,419 of the permissible 66,931 thymine–adenine dinucleotide (TA) sites contained an insertion representing an insertion density of ~41%. The outer ring are the genomic coordinates, the blue lines represent transposon insertions and the gray boxes indicate regions of that did not have any insertions. Plot made with Circlize (Gu et al, 2014).</p> <p> </p> <p><strong>Figure S2. Diversity of the output library isolated from lung and thoracic lymph node lesions compared to the input library. </strong>On average, libraries recovered from lung lesions contained 14,456 unique mutants and those recovered from the lymph nodes contained an average of 16,210 unique mutants. Insertion density is represented as a proportion of the TA sites that contained insertions. The numbers on the x-axis refer to the sequencing file from that sample and come from individual animals (Bioproject ID: PRJNA816175, Submission ID: SUB11067380).</p> <p> </p> <p><strong>Figure S3. Volcano plots showing the distribution of log<sub>2</sub> fold-changes and -log<sub>10</sub> of adjusted p-values for representative lung (A) and lymph node (B) samples. </strong>Adjusted p-values (BH-fdr correction) < 0.000001 cluster at the limits of the plot and precision reflects the number of resampling iterations (10,000).</p> <p> </p> <p><strong>Figure S4. Scatterplot of mean log<sub>2</sub> fold change per gene for all lung samples against all thoracic lymph node samples</strong>. Correlation between mean log<sub>2</sub> fold change among genes between the tissues was calculated with Spearman's ranked correlation, = 0.878, p-value < 2.2e-16.</p> <p> </p> <p><strong>Figure S5. Fold-changes caused by transposon insertions in <em>RD1<sup>BCG</sup></em> and <em>RD1<sup>MIC</sup> </em>in the lungs and lymph nodes of infected cattle. </strong>Boxplot for log<sub>2 </sub>fold-changes in genes of the RD1<sup>BCG</sup> region. Samples with adjusted p-values (BH-fdr corrected) <0.05 are indicated with purple points. Gene names highlighted in magenta have fewer than 5 TA sites located in the gene; too few to determine the statistical significance of changes in insertion levels with this method.</p> <p> </p> <p><strong>Supplementary Tables </strong></p> <p><strong>Table S1. Sequencing statistics of the input and output transposon libraries. </strong>The numbers in the column labelled “filename” refers to the sequencing file from that sample and come from individual animals (Bioproject ID: PRJNA816175, Submission ID: SUB11067380).</p> <p> </p> <p><strong>Table S2. Tissues collected and scored for gross pathology. </strong>Tissues from head and neck lymph nodes (from the right and left sub-mandibular lymph nodes, the right and left medial retropharyngeal lymph nodes), thoracic lymph nodes (the right and left bronchial lymph nodes, the cranial tracheobronchial lymph nodes, the cranial and caudal mediastinal lymph nodes) and from lung lesions, were collected and scored.</p> <p> </p> <p><strong>Table S3. Log<sub>2</sub> fold-changes for insertions across the entire genome of <em>M. bovis</em> AF2122/97. </strong>Cells are coloured according to log<sub>2</sub> fold-change. Refer to the text for the gene groups in individual tabs.</p> <p> </p> <p><strong>Table S4. </strong>Custom transposon sequencing primers and adaptors used in sequencing of the transposon libraries.</p> <p> </p>
Microsatellite genotypes for «Genetic diversity and spatial genetic structure support the specialist‑generalist variation hypothesis in two sympatric woodpecker species»
<p>Species are often arranged along a continuum from “specialists” to “generalists”. Specialists typically use fewer resources, occur in more patchily distributed habitats and have overall smaller population sizes than generalists. Accordingly, the specialist-generalist variation hypothesis (SGVH) proposes that populations of habitat specialists have lower genetic diversity and are genetically more differentiated due to reduced gene flow compared to populations of generalists. Here, expectations of the SGVH were tested by examining genetic diversity, spatial genetic structure and contemporary gene flow in two sympatric woodpecker species differing in habitat specialization. Compared to the generalist great spotted woodpecker (<em>Dendrocopos major</em>), lower genetic diversity was found in the specialist middle spotted woodpecker (<em>Dendrocoptes medius</em>). Evidence for recent bottlenecks was revealed in some populations of the middle spotted woodpecker, but in none of the great spotted woodpecker. Substantial spatial genetic structure and a significant correlation between genetic and geographic distances were found in the middle spotted woodpecker, but only weak spatial genetic structure and no significant correlation between genetic and geographic distances in the great spotted woodpecker. Finally, estimated levels of contemporary gene flow did not differ between the two species. Results are consistent with all but one expectations of the SGVH. This study adds to the relatively few investigations addressing the SGVH in terrestrial vertebrates.</p>
Functional genomics analysis to disentangle the role of genetic variants in major depression - Supplementary Tables
<p>This entry contains the data generated by the study "Functional genomics analysis to disentangle the role of genetic variants in major depression" that are part of the Supplementary information of the article describing the study.</p> <p>The entry contains the following data:</p> <p><strong>Supplementary Tables S1-S7</strong></p> <p>Supplementary Table S1. Summary of resources.</p> <p>Supplementary Table S2. Causal GVs for MD.</p> <p>Supplementary Table S3. pGenes functional and disease enrichment analysis.</p> <p>Supplementary Table S4. Fine-mapped MD causal GVs disease enrichment analysis.</p> <p>Supplementary Table S5. Colocalizing GWAS-eQTLs association to disease.</p> <p>Supplementary Table S6. TFBS analysis.</p> <p>Supplementary Table S7. GVs state annotation. </p>
The antique genetic plight of the Mediterranean monk seal (Monachus monachus)
<p>This repository contains all the scripts and most of the intermediary files necessary to replicate the analyses of the preprint "The antique genetic plight of the Mediterranean monk seal (<em>Monachus monachus</em>)" available at:</p> <p>https://biorxiv.org/cgi/content/short/2021.12.23.473149v3</p> <p>This version of the data has been revised according to two rounds of review by two reviewers' comments and suggestions during a submission process at Proceedings of the Royal Society B.</p> <p>Within each of the different zipped folders a readme.txt file briefly explains how the analyses are organized.</p> <p> </p> <p> </p> <p>We thank all the collectors and museums listed in Table S1 for providing access to genetic samples. We are grateful to Sophie Courjal and the staff of the “Plateau technique - Biologie moléculaire et microbiologie” at EDB for their assistance during lab work, to P. Kyritsis [Archipelagos], for his logistic help, to I. Carvalho for early comments on the manuscript and two anonymous reviewers who significantly helped to improve the manuscript. This work was funded by the Fondation Prince Albert II de Monaco (project “Génétique de la conservation du phoque moine de Méditerranée”). The Genotoul bioinformatics (Bioinfo Genotoul) platforms provided computing resources. JS was supported by PANGO-GO (ANR-17-CE02-0001), and LABEX TULIP (ANR-10-LABX-0041).</p>
Corpus and list of keywords from Improving sustainable crop protection using population genetics concepts
<p>Corpus extracted in April 2021 from the ISI Web of Science portal (https://www.webofscience.com) with the following request: ‘Plant AND Resistan* AND Durab*’. A first corpus of 2522 articles was built considering all publication years for this extraction. This collection was then refined by categories to remove articles outwith the scope of our search (e.g. related to durable resistant materials for constructions). We also kept only articles cited at least once. The final corpus was composed of 1783 articles from 1979 to 2021:</p> <ul> <li>CORPUS_plant_resistance_durability.zip</li> </ul> <p>List of keywords used for the network presented in the article:</p> <ul> <li>keywords_list.csv</li> </ul>
Even short‐distance dispersal over a barrier can affect genetic differentiation in Gyraulus, an island freshwater snail
<p>Supplementary dataset for a published paper, "Saito T., Sasaki T., Tsunamoto Y., Uchida S., Satake K., Suyama Y., <em>et al.</em> (2022). Even short‐distance dispersal over a barrier can affect genetic differentiation in <em>Gyraulus</em> , an island freshwater snail. <em>Freshwater Biology</em> <strong>67</strong>, 1971–1983. <a href="https://doi.org/10.1111/fwb.13990">https://doi.org/10.1111/fwb.13990</a>"</p>
Lab513/CyberSwitch: Real time control of a genetic toggle switch
<p>Release 1.0 | CyberSwitch | Master Branch</p> <p>This repository contains the code and data that were used in the paper:</p> <p>Lugagne, J.-B., Carillo, S. S., Kirch, M., Köhler, A., Batt, G., & Hersen, P. (2017). Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nature Communications. </p> <p>This article is accessible in open access : http://rdcu.be/A0lH</p> <p> </p>
Role of environmental factors in the genetic structure of a highly mobile seabird
<p><strong>Aim:</strong> Environmental features can act as selection pressures and barriers to gene flow between populations. The genetic structuring of highly mobile but philopatric seabirds creates a paradox, and the role of oceanographic and geographic variables is still poorly understood. In this study, we investigate the influence of environmental and geographic variables in the genetic and phenotypic diversity of a pantropical seabird breeding in islands and archipelagos separated by different geographic distances, up to thousand kilometers, and which differ in environmental characteristics.</p> <p><strong>Location:</strong> Islands and archipelagos in the southwestern Atlantic Ocean.</p> <p><strong>Taxon:</strong> <em>Sula dactylatra</em>, Lesson, 1831 (masked booby)<em>.</em></p> <p><strong>Methods:</strong> The population structure of the species was accessed through mitochondrial and nuclear DNA. To test Isolation by Environment (IBE) <em>vs</em>. by Distance (IBD), sea surface temperature, primary productivity, and salinity, as well as isotopic niche based on carbon and nitrogen, and distances between colonies and from the continent, were used. We also tested the correlation between the genetic structure and the morphometry of individuals in each colony.</p> <p><strong>Results:</strong> We identified the presence of low genetic structure between populations. Nevertheless, differences were identified between inshore and offshore colonies, with the influence of landscape characteristics of these two types of environment. The morphometric and isotopic niche variations are consistent with this segregation.</p> <p><strong>Main conclusions:</strong> Environmental variables of coastal and oceanic environments seem to influence the genetic structure of masked boobies, even though it is low in the SW Atlantic Ocean, highlighting the role of environmental heterogeneity in shaping biodiversity.</p>
Data for 'Genetic variation in trophic avoidance shows fruit flies are generally attracted to bacterial pathogens'
<p>Raw data dn R code for the analysis of data dn generation of all figures in the above referenced paper. Descriptions of each data file are included wihtin the R script. </p>
Online Appendix for PhD Thesis Titled "Dissecting Causal Relationships and Molecular Mechanisms in Disease using Genetic Risk Profiles"
<p>This repository contains 23 tables and two figures, which are too big to be included in the Appendix section of my thesis document.</p> <p>The second version includes additional summary statistics of metabolite-PGS associations which can be found at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.</p>
CLDF dataset derived from Peiros' "Genetic classification of Austro-Asiatic languages" from 2004
<p>Cite the source of the dataset as:</p> <blockquote> <p>Peiros, I. I. (2004): Genetičeskaja klassifikacija avstroaziatskix jazykov [Genetic classification of Austro-Asiatic languages]. Russian State University for the Humanities, Russian State University for the Humanities, Moscow.</p> </blockquote>
Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm
<p>Used data and obtained results for the paper Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm.</p> <p>The reader can find the following files :</p> <ul> <li>configuration_types.csv contains the cloud resource types (the name is the EC2 instance for database, and for the BPM engine separated by an underscore), their price and their capacity</li> <li>tenants_uni.csv contains the customers and their minimum and maximum BPM task throughput</li> <li>results_[number of tenants]_seg.csv files contain the results for the previous heuristic (segmentation only)</li> <li>results_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the iterative heuristic tests</li> <li>solver_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the restricted model solved tests</li> </ul>
A plant biodiversity effect resolved to a single genetic locus - datasets
<p>Despite extensive evidence that biodiversity promotes plant community productivity, progress towards understanding the mechanistic basis of this effect remains slow, impeding the development of predictive ecological theory and agricultural applications<em>. </em>Here, we analysed non-additive interactions between genetically divergent Arabidopsis accessions in experimental plant communities. By combining methods from ecology and genetics, we identified a major effect locus that promotes complementarity amongst genotypes and above-ground productivity in mixed communities. In experiments with near-isogenic lines, we show that this diversity effect can act independently of other genomic regions and be resolved to a single locus representing less than 0.3% of the genome. Using plant-soil-feedback experiments, we demonstrate that allelic diversity also causes genotype-specific soil legacy responses in a subsequent growing period. Our work thus shows that positive diversity effects can be linked to single Mendelian factors, and that a range of complex community properties, some of which manifest themselves even after the original community has disappeared<strong>, </strong>can have a simple, single cause. This may pave the way to novel breeding strategies, focussing on phenotypic properties that manifest themselves beyond isolated individuals, i.e. at a higher level of biological organisation.</p>
Orthodontically Induced External Apical Root Resorption with genetic and non-genetic factors
<p>Orthodontically Induced External Apical Root Resorption (OIEARR) dataset with genetic and non-genetic factors from a sample of 195 patients previously submitted to orthodontic treatment. Methodology for patient selection is described in published related works as well as materials and methods. Percentage of OIEARR were assessed in maxillary teeth: the four incisors and the two canines. Ten clinical and treatment variables were recorded: gender, age, treatment duration, premolar extractions, skeletal pattern, Hyrax appliance, functional appliance, overjet, anterior open bite and tongue thrust. Single nucleotide polymorphisms (SNPs) of six genes : rs114363 from <em>IL1B</em>; rs3102735 from <em>TNFRSF11B</em>, encoding OPG; rs1059703 from <em>I</em><em>RAK1</em>; rs315952 from <em>IL1RN</em>; rs1805034 from <em>TNFRSF11A</em>, encoding RANK; rs1718119 from <em>P2RX7</em>.</p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
Retrotransposon-based genetic variation of Poa annua populations from contrasting climate conditions
<p>Raw photographs of agarose electrophoresis. Material: six Poa annua populations. Method: inter-Primer Binding Site (iPBS) markers This is the documentation of studies described in the manuscript entitled "Retrotransposon-based genetic variation of Poa annua populations from contrasting climate conditions" accepted for publication in PeerJ journal (decision received on 02.04.2019)</p>
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.