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2,880 results for “variant”
Large scale across-breed genome-wide association study reveals a variant in HMGA2 associated with inguinal cryptorchidism risk in dogs
<p class="MsoNormal"><span>Cryptorchidism is the most common congenital sex development disorder in dogs. Despite this, little progress has been made in understanding its genetic background. Extensive genetic testing of dogs through consumer and veterinary channels using a high-density SNP genotyping microarray coupled with links to clinical records presents the opportunity for a large-scale genome-wide association study to elucidate the molecular risk factors associated with cryptorchidism in dogs. Using an inter-breed genome-wide association study approach, a significant statistical association on canine chromosome 10 was identified, with the top SNP pinpointing a variant of <em>HMGA2 </em>previously associated with adult weight variance. In further analysis we show that incidence of cryptorchidism is skewed towards smaller dogs in concordance with the identified variant's previous association with adult weight. This study represents the first putative variant to be associated with cryptorchidism in dogs.</span></p>
PPS3D: A 3D Variant of the Primordial Particle System
<p>We demonstrate here that the morphogenetic Primordial Particle System, which was originally defined for two dimensions only, can also operate with minimal adaptations in a three-dimensional setting, producing similar life-like structures and dynamics.</p> <p>This video showcases various properties of a Primordial Particle System in 3D. More information about the 2D "original" PPS can be found in this video: https://youtu.be/makaJpLvbow and in the publication "How a life-like system emerges from a simple particle motion law" ( doi: <a href="https://dx.doi.org/10.1038%2Fsrep37969">10.1038/srep37969</a>).</p>
Oncogenic role of a developmentally regulated NTRK2 splice variant
<p>Abstract</p> <p>Temporally-regulated alternative splicing choices are vital for proper development yet the wrong splice choice may be detrimental. Here we highlight a novel role for the neurotrophin receptor splice variant TrkB.T1 in neurodevelopment, embryogenesis, transformation, and oncogenesis across multiple tumor types in both humans and mice. TrkB.T1 is the predominant NTRK2 isoform across embryonic organogenesis and forced over-expression of this embryonic pattern causes multiple solid and nonsolid tumors in mice in the context of tumor suppressor loss. TrkB.T1 also emerges the predominant NTRK isoform expressed in a wide range of adult and pediatric tumors, including those harboring TRK fusions. Affinity purification-mass spectrometry (AP-MS) proteomic analysis reveals TrkB.T1 has distinct interactors with known developmental and oncogenic signaling pathways such as Wnt, TGF-ß, Hedgehog, and Ras. From alterations in splicing factors to changes in gene expression, the discovery of isoform specific oncogenes with embryonic ancestry has the potential to shape the way we think about developmental systems and oncology.</p>
Whole genome sequence variant discovery from Ethiopian Boran, N'Dama and Holstein cattle
<p>Whole genome sequence variants (SNPs) from forty samples of each Ethiopian Boran, N'Dama and Holstein cattle breeds which were utilised in the Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle study by Riggio et al., 2022, (in review). The NDama sequences included samples from Guinea (n =21), Nigeria (n =10) and Senegal (n = 9); and the Boran samples from Ethiopia (n = 30) and Kenya (n = 10). Variants discovery followed the protocol described in the Material and Method section of the paper.</p>
GlottisNetV2 - Time variant training and testing data
<p>Here we provide time variant training and testing data for the GlottisNetV2 study. In particular, this dataset relies on the benchmark for automatic glottis segmentation (BAGLS dataset). This three-dimensional data (time, y, x) is used for experiments involving deep neural networks capable of processing time-variant data. </p> <p>Each folder contains videos each with the following data:</p> <ul> <li>Endoscopic video as mp4 (*.mp4)</li> <li>Glottis segmentation as mask-file (hdf5 container, *.mask)</li> <li>Glottis segmentation as mp4 file (*_mask.mp4)</li> <li>Metadata as JSON file (*.meta)</li> <li>Glottal midline annotation as JSON file (*.points)</li> </ul>
Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation
<p>SNV of 23 parental barley inbreds of the double round robin population (DRR) (<a href="https://doi.org/10.1111/pbi.13746">https://doi.org/10.1111/pbi.13746</a>) used in the publication "Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation". SV, INDELs, and additional data are available via figshare (https://doi.org/10.6084/m9.figshare.16802473).</p>
Project Adotto Whole-Genome Variants
<p>Squared-off project-level VCF on GRCh38 of 172 haplotype-resolved long-read assemblies from 86 samples of 78 individuals. Variants stored in compressed BCF and split by chromosome. Created as part of a project attempting to catalog tandem-repeat regions in Human genomes. Details on the project can be found on the <a href="https://github.com/ACEnglish/adotto">github</a>.</p>
Training Data for 'somatic variants discovery'
<p>The data provided here are part of a Galaxy Training Network tutorial created by Björn Grüning to detect hCNVS in defined WES chromosomal regions.</p>
List of intron retention associated variants from Sequence Read Archive
<p>List of intron retention associated variants via <a href="https://github.com/friend1ws/iravnet">IRAVNet</a> applied to 219,615 transcriptome sequence data from Sequence Read Archive</p>
Raw data: Association and functional analysis of angiotensin-converting enzyme 2 gene genetic variants with the pathogenesis of pre-eclampsia
<p class="MsoNormal"><span>These data were generated to investigate the association and functional analysis of angiotensin-converting enzyme 2 genetic variants with the pathogenesis of pre-eclampsia(PE). This study conducted a case-control study involving 327 PE patients and 591 healthy pregnant women to explore the associations between candidate variants in the ACE2 gene variants and the pathogenesis of PE.This study collected clinical samples and data, and used logistic regression, false positive report rate, multi factor dimension reduction, functional analysis and other analysis methods to process the research data. </span>Potential functional ACE2 gene variants (rs2106809 A>G, rs6632677 G>C, and rs2074192 C>T) were selected and genotyped using kompetitive allele-specific PCR. The strength of the associations between the studied genetic variants and the risk of PE were evaluated using odds ratios (ORs) and corresponding 95% confidence intervals (CIs).<span> Finally,it showed that the rs2106809 A>Gis significantly associated with the risk of PE via individual locus effects and/or complex gene-gene and gene-environment interactions.</span><span> </span></p>
Data for: Immunogenicity of SARS-CoV-2 spike antigens derived from Beta & Delta variants of concern
<p>Using our strongly immunogenic SmT1 SARS-CoV-2 spike antigen platform, we developed novel antigens based on the Beta & Delta variants of concern. These antigens elicited higher neutralizing antibody activity to the corresponding variant than comparable vaccine formulations based on the original reference strain, while a multivalent vaccine generated cross-neutralizing activity to all three variants. This suggests that while current vaccines may be effective at reducing severe disease to existing variants of concern, variant-specific antigens, whether in a mono- or multivalent vaccine, may be required to induce optimal immune responses and reduce infection against arising variants.</p>
REVEL (Rare Exome Variant Ensemble Learner) Scores
<p>REVEL is an ensemble method for predicting the pathogenicity of missense variants in the human genome. For more information, see <a href="https://sites.google.com/site/revelgenomics/">https://sites.google.com/site/revelgenomics/</a> and <a href="https://dx.doi.org/10.1016/j.ajhg.2016.08.016">https://dx.doi.org/10.1016/j.ajhg.2016.08.016</a>.</p>
Phosphorylated histone variant γH2Av is associated with chromatin insulators in Drosophila
<p>Chromatin insulators are responsible for orchestrating long-range interactions between enhancers and promoters throughout the genome and align with the boundaries of topologically associating domains (TADs). Here, we demonstrate an interaction between proteins that associate with the gypsy insulator and the phosphorylated histone variant H2Av (γH2Av), normally a marker of DNA double strand breaks. Gypsy insulator components colocalize with γH2Av throughout the genome, in polytene chromosomes and in diploid cells in which Chromatin IP data shows it is enriched at TAD boundaries. Mutation of insulator components prevents stable H2Av phosphorylation in polytene chromatin and phosphatase inhibition strengthens the association between insulator components and γH2Av and rescues γH2Av localization in insulator mutants. We also show that γH2Av is a component of insulator bodies, and that phosphatase activity is required for insulator body dissolution after recovery from osmotic stress. Together, our results indicate a novel mechanism linking the H2A variant γH2Av to insulator function. </p>
GHGA_sarek3_variant_calling_Agilent200M_WES
<p>Using the sarek pipeline default values. Aligned to HG38 using bwa, and dragmap. Variants are called using either haplotypecaller, strelka, deepvariant, or freebayes. Data input was Agilent 200M WES reads. Sample A006850052 (GIAB: HG001)</p>
Database of Rural Technological Trajectories, their variants and territories featured by peasantries of the Brazilian Northern Region based on Agricultural Censuses and special tabulations for the Economy of Agroforestry Systems (2006 and 2017)
<p><strong>Introduction</strong></p> <p>This database contains the string variables at municipal level that qualifies the techno-productive trajectories (TT) of the Brazilian Northern Region Agrarian Economy, their technological variants (TTV) and territories based on peasantries. The TTs and TTVs were defined and theoretically justified by Costa (2021, p. 217-219).</p> <p><strong>Delimitation of Technological Trajectories, their variants and territories featured by peasantries</strong></p> <p>The TTs are designed by a method that combines <em>differentiation and structural signification</em> of rural production in each territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU).</p> <p><em>Structural differentiation</em> (Phase 1) is necessary because production systems activities play different roles, depending on the systems production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 440 and 441 of Costa (2021).</p> <p>In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These patterns are semantically associated with TTs or TTPs<em> structures</em> that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work. The codes of variable “Technological Trajectories” in this database are “CamponêsT1” for “Peasant Trajectory.T1” in Costa, 2021; “CamponêsT2” for “Peasant Trajectory.T2”; “CamponêsT3” for “Peasant Trajectory.T3”; “PatronalT4” for “Employer.T4”; “PatronalT5” for “Employer.T5”; “PatronalT7” for “Employer.T7”.</p> <p>In turn, the procedures to get the technological variants of TTs (TTVs) for census years 2006 and 2017 are described in Costa, 2021, p. 447-451. The codes of variable “Technological Variants” in this database are “IQ” for “CI = Chemical Intensity” in Costa, 2021; “IM” for “MI = Mechanical Intensity”; “IT” for “LI = Labour Intensity”; “IPst” for “PI = Pasture Improvement”; “IReb” for “HI = Herd improvement”; “Crg” for “LoadC=Load Capacity of Pasture”; “SAF-F” for “AFSs-F = AFSs with the presence of forest management”; “SAF-A” for “AFSs-A = Artificially developed AFSs”; “+” after the attribute for “Attribute clearly verified; “–“ for “Attribute clearly absent”; “0” for “an uncertain attribute”.</p> <p>The Brazilian Northern Region encompasses the municipalities of the federative states Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins. A municipality is codified by the variable “Peasantry” as “ACaboclo_Originário”, meaning a territory of an “original caboclo peasantry (OcP in English or CbO in Portuguese)”, if founded before 1880; “BCaboclo_Forâneo”, meaning a territory of an ”immigrant caboclo peasants (IcP or CbF)”, if founded between 1880 and 1910; “CAgrícola_Forâneo, meaning a territory of a “post-ruber immigrant agricultural peasantry (IpR or FpB)”, if founded between 1910 and 1960; and “DContemporâneo”, meaning a “recent peasantry (ReP or ReC)”, if founded since 1960.</p> <p>The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 2006 and 2017 Agricultural Censuses. The following special cases were handled:</p> <ul> <li>In the Agricultural Census 2017 credit data were not available. However, the Central Bank of Brazil informs for that year total rural credit for family-based and non-family-based agriculture and livestock by municipality.</li> <li>Comparing the production of manioc in the 2006 census with the production of manioc flour in the same year and with the historical production, we arrived at errors in three municipalities in Pará: in Moju a manioc production of 498,907 t is recorded adding the two sets of data (Peasant and Employer), when in fact it is 42,132; in São Miguel do Guamá the figure of 392,784 t is recorded when it actually is 175,941; 204,216 is recorded in Viseu and 106,287 is actual figure. Corrections were made using the proportion manioc/manioc flour prevailing in other municipalities in same microregion.</li> <li>In the census there are the value of the production of manioc and the value of the production of manioc flour. Since we are dealing with the same producer, if we consider in gross value of production or income aggregations both products, we incur double counting. In such cases, the value related to manioc flour was considered.</li> <li>The 2006 census has information on fishing restricted to the monetary income from the sale of fish, as a complementary income variable. The information does not incorporate the value of fish consumed in the establishment. Therefore, it is not a variable equivalent to the GVP of all other products considered. In turn, the 2017 census provides data on fish production as part of livestock (the gross value of fish production in captivity, which makes up the gross value of livestock production) but does not maintain the fish sales variable from the previous census. This income is contained in the variable “other producer income”, in which, none of the other possibilities listed (esgargot, etc.) are adhered to T2 (IBGE, Censo Agropecuário de 2017. Rio de Janeiro, IBGE, 2018). Therefore, two things were done to incorporate fisheries: a) for 2006 the GVP of fishery production was considered the variable "fish sale" under the heading "other producer income" divided by 1 minus the self-consumption rate 31% (Costa et al, 2022); b) for 2017, the GVP of fisheries production resulted from the division of the variable “other producer's incomes” by the same denominator of the operation described in “a”.</li> </ul> <p><strong>The dataset is organized as:</strong></p> <p>1. Data set with variables delimiting TT, TTV and Peasantry</p> <p><em>2006_NorthRegion_TechVariants.csv</em><br> <em>2017_ NorthRegion_TechVariants.csv</em>.</p> <p>In each table the column names are self-explanatory.</p> <p>2. Dataset with special tabulation or the agroforestry systems economy represented by Peasant Trajectory.T2</p> <p><strong> Gross Value of Production</strong></p> <p><em> Table1_NorthRegion_T2_GVP.csv <br> </em>Table 1a – Gross Value of Productios (GVP) by products of AFSs-F and peasantry 2006 and 2017<br> Table 1b – Gross Value of Productios (GVP) by products of AFSs-A and peasantry, 2006 and 201<br> Table 1c – Gross Value of Productios (GVP) by products of T2, technological variant, and peasantry, 2006 and 2017 </p> <p><strong> Real Product</strong></p> <p> Real Product” (RP): For each year (i), the vector of produced quantities (Qi) multiplied by a vector of fixed prices (P1): variation of RP is explained exclusively by the variation of Q. <br> <em>Table2_NorthRegion_T2_RealProduct.csv</em><br> Table 2a – T2 Production by technological variant and peasantry, 2006<br> Table 2b – T2 Production Value (=Real Product) by technological variant, and peasantry, 2006 in R$ 1,000 currents<br> Table 2c – T2 Implicit prices by technological variant, and peasantry, 2006, R$ 1.000 currents (each cel in Table 2b divided by corresponding cel in Table 2a)<br> Table 2d – T2 Production by technological variant, and peasantry, 2017<br> Table 2e – T2 Real Product1 by technological variant, and peasantry, 2017 in R$ 1,000 from 2006 (each cel in Table 2c multiplied by corresponding cel in Table 2d)</p> <p><strong> Key variables</strong><br> <em> Table3_NorthRegion_T2_KeyVariables.csv</em><br> Table 3 – Key variables of T2 economy by peasantry, 2006 and 2017</p> <p><strong>Reference:</strong></p> <p>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2).</p> <p>COSTA, F. A., FEIJÃO,, L. G., ALMEIDA, I. C., NOGUEIRA, K. N. S., AMERICO, M. C. (2022). Database of a Riverine Economy in Mocajuba, Low Tocantins, Pará, Amazonia, Brazil [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7121336</p>
A dual selection system for directed evolution to identify allosteric transcription factor PobR variants responsive to different aromatic compounds
<p>This dataset includes all the raw data of our characterization experiments during the work titled “A dual selection system for directed evolution to identify allosteric transcription factor PobR variants responsive to different aromatic compounds”.</p>
SARS-CoV-2 variants
<p>This dataset is a temporary upload and will be removed after double-blind peer review process is finished.</p>
Nitzschia sp. Nitz4 variant calling
<p>BAM file of read alignments used to call variants in the Nitzschia sp. Nitz4 genome. The BAM file was processed to mark duplicates and add read group names.</p>
Data from: Common protein-coding variants and the racing phenotype in galloping racehorse breeds
<p>Centuries-long selection for system-wide morphological, physiological, and metabolic adaptations has led to extreme athletic phenotypes among geographically diverse horse breeds. Here, we identify genes contributing to exercise adaptation in racehorses by applying genomics approaches for racing performance, an end-point athletic phenotype. Using an integrative genomics strategy to first combine population genomics results with skeletal muscle exercise and training transcriptomic data, followed by whole-genome resequencing of Asian horses, we identify protein-coding variants in genes of interest in galloping racehorse breeds (Arabian, Mongolian and Thoroughbred). A core set of genes, <em>G6PC2</em>, <em>HDAC9</em>, <em>KTN1</em>, <em>MYLK2</em>, <em>NTM</em>, <em>SLC16A1</em> and <em>SYNDIG1</em>, with central roles in muscle, metabolism, and neurobiology, are key drivers of the racing phenotype. Although racing potential is a multifactorial trait, the genomic architecture shaping the common athletic phenotype in horse populations bred for racing provides evidence for the influence of protein-coding variants in fundamental exercise-relevant genes. Variation in these genes may therefore be exploited for genetic improvement of horse populations towards specific types of racing.</p>
CWL run of Somatic Variant Calling Workflow (CWLProv 0.5.0 Research Object)
<p>The somatic variant calling workflow included in this case study is designed by <a href="http://bcb.io/">Blue Collar Bioinformatics (bcbio)</a>, a community-driven initiative to develop best-practice pipelines for variant calling, RNA-seq and small RNA analysis workflows. According to the documentation, the goal of this project is to facilitate the automated analysis of high throughput data by making the resources quantifiable, analyzable, scalable, accessible and reproducible.</p> <p>All the underlying tools are containerized, facilitating software use in the workflow. The somatic variant calling workflow defined in CWL is available on GitHub and equipped with a well defined test dataset.</p> <p>This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwlprov/">https://pypi.org/project/cwlprov/</a> to explore</p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built on:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <p>To run the workflow:<br> </p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223 git clone https://github.com/FarahZKhan/bcbio_test_cwlprov cd bcbio_test_cwlprov/somatic/somatic-workflow/ cwltool --provenance somaticwf_0.5.0_mac main-somatic.cwl main-somatic-samples.json</code></pre> <p>To package the research object:<br> </p> <pre><code class="language-bash">zip -r somaticwf_0.5.0_mac.zip somaticwf_0.5.0_mac/ sha256sum somaticwf_0.5.0_mac.zip > somaticwf_0.5.0_mac.zip.sha256</code></pre> <p>The <a href="https://github.com/FarahZKhan/bcbio_test_cwlprov">cloned git repository</a> is a fork of <a href="https://github.com/bcbio/test_bcbio_cwl">https://github.com/bcbio/test_bcbio_cwl</a>. It was obtained using:</p> <pre><code class="language-bash">wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz</code></pre> <p>The content is from an archived version from the documentation here: <a href="https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers">https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers</a></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.