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Data from: Population structure of two rabies hosts relative to the known distribution of rabies virus variants in Alaska
For pathogens that infect multiple species the distinction between reservoir hosts and spillover hosts is often difficult. In Alaska, three variants of the arctic rabies virus exist with distinct spatial distributions. We test the hypothesis that rabies virus variant distribution corresponds to the population structure of the primary rabies hosts in Alaska, arctic foxes (Vulpes lagopus) and red foxes (V. vulpes) in order to possibly distinguish reservoir and spill over hosts. We used mitochondrial DNA (mtDNA) sequence and nine microsatellites to assess population structure in those two species. mtDNA structure did not correspond to rabies virus variant structure in either species. Microsatellite analyses gave varying results. Bayesian clustering found 2 groups of arctic foxes in the coastal tundra region, but for red foxes it identified tundra and boreal types. Spatial Bayesian clustering and spatial principal components analysis identified 3 and 4 groups of arctic foxes, respectively, closely matching the distribution of rabies virus variants in the state. Red foxes, conversely, showed eight clusters comprising 2 regions (boreal and tundra) with much admixture. These results run contrary to previous beliefs that arctic fox show no fine-scale spatial population structure. While we cannot rule out that the red fox is part of the maintenance host community for rabies in Alaska, the distribution of virus variants appears to be driven primarily by the artic fox Therefore we show that host population genetics can be utilized to distinguish between maintenance and spillover hosts when used in conjunction with other approaches.
Data from: MC1R variants affect the expression of melanocortin and melanogenic genes and the association between melanocortin genes and coloration
The melanocortin-1 receptor (MC1R) gene influences coloration by altering the expression of genes acting downstream in the melanin synthesis. MC1R belongs to the melanocortin system, a genetic network coding for the ligands that regulate MC1R and other melanocortin receptors controlling different physiological and behavioural traits. The impact of MC1R variants on these regulatory melanocortin genes was never considered, even though MC1R mutations could alter the influence of these genes on coloration (e.g. by decreasing MC1R response to melanocortin ligands). Using barn owl growing feathers, we investigated the differences between MC1R genotypes in the (co)expression of six melanocortin and nine melanogenic-related genes and in the association between melanocortin gene expression and phenotype (feather pheomelanin content). Compared to the MC1R rufous allele, responsible for reddish coloration, the white allele was not only associated with an expected lower expression of melanogenic-related genes (TYR, TYRP1, OCA2, SLC45A2, KIT, DCT) but also with a lower MC1R expression and a higher expression of ASIP, the MC1R antagonist. More importantly, the expression of PCSK2, responsible for the maturation of the MC1R agonist, α-melanocyte-stimulating hormone, was positively related to pheomelanin content in MC1R white homozygotes but not in individuals carrying the MC1R rufous allele. These findings indicate that MC1R mutations not only alter the expression of melanogenic-related genes but also the association between coloration and the expression of melanocortin genes upstream of MC1R. This suggests that MC1R mutations can modulate the regulation of coloration by the pleiotropic melanocortin genes, potentially decoupling the often-observed associations between coloration and other phenotypes.
Data from: Increased transgenerational epigenetic variation, but not predictable epigenetic variants, after environmental exposure in two apomictic dandelion lineages
DNA methylation is one of the mechanisms underlying epigenetic modifications. DNA methylations can be environmentally induced and such induced modifications can at times be transmitted to successive generations. However, it remains speculative how common such environmentally induced transgenerational DNA methylation changes are and if they persist for more than one offspring generation. We exposed multiple accessions of two different apomictic dandelion lineages of the Taraxacum officinale group (Taraxacum alatum and T. hemicyclum) to drought and salicylic acid (SA) treatment. Using methylation-sensitive amplified fragment length polymorphism markers (MS-AFLPs) we screened anonymous methylation changes at CCGG restriction sites throughout the genome after stress treatments and assessed the heritability of induced changes for two subsequent unexposed offspring generations. Irrespective of the initial stress treatment, a clear buildup of heritable DNA methylation variation was observed across three generations, indicating a considerable background rate of heritable epimutations. Less evidence was detected for environmental effects. Drought stress showed some evidence for accession-specific methylation changes, but only in the exposed generation and not in their offspring. By contrast, SA treatment caused an increased rate of methylation change in offspring of treated plants. These changes were seemingly undirected resulting in increased transgenerational epigenetic variation between offspring individuals, but not in predictable epigenetic variants. While the functional consequences of these MS-AFLP-detected DNA methylation changes remain to be demonstrated, our study shows that (1) stress-induced transgenerational DNA methylation modification in dandelions is genotype and context-specific; and (2) inherited environmental DNA methylation effects are mostly undirected and not targeted to specific loci.
Data from: A functional regulatory variant of MYH3 influences muscle fiber-type composition and intramuscular fat content in pigs
Muscle development and lipid accumulation in muscle critically affect meat quality in livestock. Yet, the underlying genetic factors for myofiber-type specification and intramuscular fat (IMF) accumulation remain to be elucidated. Using two independent intercrosses between Western breeds and Korean native pigs (KNPs) and a joint linkage-linkage disequilibrium analysis, we here identified a 488.1-kb region on porcine chromosome 12 that affects both reddish meat color (a*) and IMF. In this critical region, only the MYH3 gene, encoding myosin heavy chain 3, was found to be preferentially overexpressed in the skeletal muscle in KNPs. Subsequently, MYH3-transgenic mice demonstrated that this gene controls both myofiber-type specification and adipogenesis in skeletal muscle. We discovered a structural variant in the promotor/regulatory region of MYH3 for which Q allele carriers exhibited significantly higher values of a* and IMF than q allele carriers. Furthermore, chromatin immunoprecipitation and co-transfection assays showed the structural variant in the 5'-UTR of MYH3 abrogated binding of the myogenic regulatory factors (MYF5, MYOD, MYOG, and MRF4). The allele distribution of the MYH3 among worldwide pig populations indicated that the MYH3 Q allele is of Asian origin and likely predates domestication. In conclusion, we identified a functional regulatory sequence variant in the porcine MYH3 that provides novel insights into the genetic basis of the regulation of myofiber type ratios and associated changes in IMF in pigs. The MYH3 variant can play an important role to improve pork quality in current breeding programs.
Fish Tail Point Variant XCB-105-2860
Classic Fish Tail Point Variant. XCB-105-2860. 400 BCE-100 CE XCB-105 Adamagan (Aleut for place of walrus hunters) is at the head of Morzhovoi Bay, western Alaska Peninsula. It is a massive village with multiple occupations. When it was occupied 400 BCE-100 CE, it was the largest village in the Arctic with an estimated 1000 people. It also has limited occupations dated 2200-1700 BCE, 1000-600 BCE, and 900-1100 CE. The Western Alaska Peninsula artifacts are presented as a result of the research conducted under grants NSF 9630072, NSF 9814086, NSF 9996372, NSF 9996415, NSF 1139266, NSF 1321411. H. Maschner, Principal Investigator. These artifacts were scanned with either a Faro Edge Arm or a Minolta Vivid 9i. Processed in Geomagic or Polyworks. 2-8 photos were used for texture in Geomagic Wrap. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing and publication completed at Global Digital Heritage. Source: Objaverse 1.0 / Sketchfab
Histone H1 variants in Arabidopsis are subject to numerous post-translational modifications, both conserved and previously unknown in histones, suggesting complex functions of H1 in plants - raw data
<p>Data from HPLC coupled with mass spectrometer acquired during experiments.<br /> </p>
Dataset for "NanoVar: a Comprehensive Workflow for Structural Variant Detection to uncover the Genome's Hidden Patterns"
<h2><strong>Output Files for Long-Read Structural Variant and Repeat Analysis in Colorectal Cancer Samples (HRR698464, HRR698460, C586, C588)</strong></h2> <h3>Description:</h3> <p>This Zenodo dataset includes comprehensive output files generated during the application of a long-read sequencing analysis protocol for structural variant (SV) detection and repeat element characterization in colorectal cancer samples. The dataset is organized into two main directories:</p> <p><strong>1. HRR698464_MSI-H_Tumor</strong><br>This directory contains all primary output files generated from the analysis pipeline applied to the MSI-H tumor sample HRR698464 (also referred to as patient C586.T). Each subdirectory corresponds to a specific stage in the protocol:</p> <ul> <li>NanoPlot_output<br>Output from Stage 1 – Quality assessment of raw reads using NanoPlot.</li> <li>SAMtools_output<br>BAM file processing outputs from Stage 2 – Alignment of long reads to the reference genome using SAMtools.</li> <li>NanoVar_output<br>Output from Stage 3 – Structural variant calling using NanoVar.</li> <li>VCF_filtering_output<br>Output from Stage 4 – Filtering of structural variants using SURVIVOR and BCFtools; includes the filtered VCF files.</li> <li>NanoINSight_output<br>Output from Stage 5 – Characterization of repeat elements using NanoINSight.</li> <li>VEP_output<br>Output from Stage 6 – Annotation of structural variants using Ensembl Variant Effect Predictor (VEP).</li> </ul> <p> </p> <p><strong>2. Additional_output_files</strong><br>This directory contains supplementary output files used for comparison and visualization in Figures 4–7 of the associated publication. These include:</p> <ul> <li>HRR698460.NanoPlot.report.html<br>NanoPlot quality summary of a lower-quality tumor sample (HRR698460), used in Figure 4 for comparison with HRR698464.</li> <li>C586.N.nanovar.pass.vcf<br>NanoVar VCF output for the matched normal sample of patient C586, used to filter somatic calls in Stage 4.</li> <li>C586.N.nanovar.pass.report.html<br>NanoVar summary report of the normal sample of C586; used in Figure 5a.</li> <li>C588.N.nanovar.pass.vcf<br>NanoVar VCF output of the MSS normal sample (C588) for comparison with the MSI-H patient (C586).</li> <li>C588.N.nanovar.pass.report.html<br>NanoVar summary report of the MSS normal sample; used in Figure 5b.</li> <li>C588.T.nanovar.pass.vcf<br>NanoVar VCF output of the MSS tumor sample (C588); used in comparative analyses with the MSI-H sample.</li> <li>C588.T.nanovar.pass.report.html<br>NanoVar summary report of the MSS tumor sample; used in Figure 5b.</li> <li>MSS.tumor.unique.vcf<br>VCF file of somatic SVs in the MSS sample, generated by comparing matched tumor and normal pairs.</li> <li>MSS.tumor.unique.RepeatMasker.tbl<br>RepeatMasker output annotating somatic insertions in the MSS tumor sample; used in Figure 6.</li> <li>MSS.tumor.unique.vep.html<br>Ensembl VEP annotation report of somatic SVs in the MSS patient; used in Figures 7a and 7b.</li> <li>This dataset supports reproducibility and transparency of the protocol and offers a valuable resource for researchers interested in long-read-based SV detection, repeat annotation, and comparative cancer genomics.</li> </ul>
Spatially Variant Super-Resolution (SVSR) benchmarking dataset
<p>The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 real low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding real noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers. The associated paper can be found here: <a title="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf" href="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf">https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf</a></p>
Supplementary Data for 'Machine learning detection of SARS-CoV-2 high-risk variants'
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Quartet DNA benchmark sets for germline small variants and structural variants
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Fig. 2 in Two Color Variants ofSternidius alpha(Say) (Coleoptera: Cerambycidae) Show Dissimilar Cytochrome Oxidase I Genes
Fig. 2. Maximum Likelihood phylogeny inferred from barcode data of 14 Sternidius alpha specimens. Figures above the nodes indicate bootstrap support values in percentages.
Fig. 1 in Two Color Variants ofSternidius alpha(Say) (Coleoptera: Cerambycidae) Show Dissimilar Cytochrome Oxidase I Genes
Fig. 1. Location map of the Sternidius alpha specimens used in this study. The smaller circles denote a single specimen caught at those sites, while the bigger circles denote three specimens caught at those sites.
Full_variant_map for GWAS munging
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Phased VCF outputs for "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing"
<p>The collection of raw and processed VCF files for the paper titled: "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing".</p><p>Raw VCF files from DeepVariant, pbsv, and TRGT are included in the unphased subfolder. Outputs from WhatsHap and HiPhase are in their own subfolders. Three replicates of HG002 are included in all subfolders. Layout is as follows:</p><blockquote><p>hiphase_vcfs</p><p>├── all_vcfs.md5</p><p>├── hiphase</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz</p><p>│ └── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz.tbi</p><p>├── unphased</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep1.GRCh38.trgt.vcf.gz</p><p>│ ├── HG002-rep1.GRCh38.trgt.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep2.GRCh38.trgt.vcf.gz</p><p>│ ├── HG002-rep2.GRCh38.trgt.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.deepvariant.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.pbsv.vcf.gz</p><p>│ ├── HG002-rep3.GRCh38.pbsv.vcf.gz.tbi</p><p>│ ├── HG002-rep3.GRCh38.trgt.vcf.gz</p><p>│ └── HG002-rep3.GRCh38.trgt.vcf.gz.tbi</p><p>└── whatshap</p><p> ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz</p><p> ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p> ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz</p><p> ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p> ├── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz</p><p> └── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz.tbi</p><p>3 directories, 43 files</p></blockquote>
A method to investigate Pathological Variant load of Mitochondrial DNA in single human Mesenchyma stem cells
<p>Mesenchymal stem cells (MSCs) are the parent cells to many cells of the musculoskeletal system including osteoblasts. Previous work has shown that in mouse models mitochondrial DNA (mtDNA) pathological variants lead to dysfunction of the respiratory chain of these osteoblasts and the premature development of age related osteoporosis. An increased rate of respiratory chain deficiency has also been observed in human osteoblasts. mtDNA mutates at an enhanced rate compared to nuclear DNA and has been linked to the ageing process in humans. </p> <p>We have developed and optimised an experimental pipeline to isolate single MSCs using flow cytometry before performing next generation sequencing to analyse the pathogenic variant load of 13 patients aged 22-88 to assess if pathogenic variants exist in mesenchymal stem cells and if there are age related changes.</p> <p>Across all patients and all cells mtDNA pathological variants were present at similarly low levels. As per previous studies we found increased pathogenic variants within the mtDNA control region (D-Loop). However overall, there was no significant difference in the distribution of variants across the rest of the genome in all patients. Although a higher proportion of non-synonymous variants were seen this was not statistically significant.</p> <p>We have shown that it is possible to isolate and sequence individual MSCs and observe mtDNA pathological variants. This gives a snapshot in time of the variant load for each patient. Due to low sample numbers compared to other studies investigate mtDNA in other cell types, we did not observe any age related increase in pathological variants. However this adds further evidence that clonally expanded, somatic mtDNA variants are common and could contribute to age related disease including osteoporosis.</p>
Izembek Point Variant XCB-003-286
Izembek Point Variant XCB-003-286 XCB-003, or IZM-003, is a site tested by A.P. McCartney in 1971. It is the type-site for the Izembek Phase. A small village with a unique whalebone house and many artifacts similar to those in the Bristol Bay region. It dates approximately 1200-1350 CE. The Alaska Peninsula artifacts are presented as a result of the research conducted under grants NSF 9630072, NSF 9814086, NSF 9996372, NSF 9996415, NSF 1139266, NSF 1321411. H. Maschner, Principal Investigator. These artifacts were scanned with either a Faro Edge Arm or a Minolta Vivid 9i. Processed in Geomagic or Polyworks. 2-8 photos were used for texture in Geomagic Wrap. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing and publication completed at Global Digital Heritage. 1. McCartney, A.P. 1974 Prehistoric Cultural Integration along the Alaska Peninsula. APUA 16(1):59-84. 2. Maschner: doi: 10.1353/arc.2011.0007 Arctic Anthro. January 1, 2004 vol. 41 no. 2 98-111 Source: Objaverse 1.0 / Sketchfab
The immunopathological landscape of human pre-TCRα deficiency: from rare to common variants
<div> <p>We describe humans with rare biallelic loss-of-function PTCRA variants impairing pre–a T cell receptor (pre-TCRa) expression. Low circulating naive ab T cell counts at birth persisted over time, with normal memory ab and high gd T cell counts. Their TCRa repertoire was biased, which suggests that noncanonical thymic differentiation pathways can rescue ab T cell development. Only a minority of these individuals were sick, with infection, lymphoproliferation, and/or autoimmunity. We also report that 1 in 4000 individuals from the Middle East and South Asia are homozygous for a common hypomorphic PTCRA variant. They had normal circulating naive ab T cell counts but high gd T cell counts. Although residual pre-TCRa expression drove the differentiation of more ab T cells, autoimmune conditions were more frequent in these patients compared with the general population.</p> </div>
NanoVarBench variant truthset files
<p>These tarballs contain the variant truthsets used for each sample in our <a href="https://doi.org/10.1101/2024.03.15.585313">paper</a> "<em>Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data</em>".</p> <p>Each directory contains the following files:</p> <ul> <li> <code><sample>.bed</code> - A BED file of all regions in the genome</li> <li><code><sample>.repetitive_regions.bed </code>- A BED file of all repetitive regions of the genome (see the paper for details of how these were identified).</li> <li><code><sample>.unique_regions.bed</code> - Non-repetitive regions of the genome. This is the result of performing <code>bedtools complement -i <repetitive BED> -g <faidx of mutref></code></li> <li><code>ani.tsv</code> - skani output from skani search for the sample's assembly against all of the downloaded genomes for that species. The last three columns are not from skani. They are completeness_percentile, completeness, and contamination metrics, all obtained from NBCI for each assembly accession.</li> <li><code>apply.vcf.gz</code> - the variants that were applied to the sample's reference assembly.</li> <li><code>apply.vcf.gz.csi</code> - VCF index for the above VCF</li> <li><code>dnadiff.vcf.gz</code> - Variants between the sample and donor genome from mummer4</li> <li><code>minimap2.vcf.gz</code> - Variants between the sample and donor genome from minimap2</li> <li><code>mutdonor.fna</code> - the FASTA file of the selected variant donor</li> <li><code>mutreference.fna</code> - the sample's reference assembly with the apply.vcf.gz applied to it. This is the genome that the sample's read are aligned to for calling variants</li> <li><code>mutreference.fna.fai</code> - the faidx of the above genome</li> <li><code>reference.fna </code>- the reference assembly of the sample. These are also available on GenBank, but are included here for interoperability</li> <li><code>truth.vcf.gz</code> - the truthset of variants. This is essentially apply.vcf.gz with the REF and ALT invert and the POS adjusted for the difference in position between the sample and donor assemblies. (See <a href="https://github.com/mbhall88/NanoVarBench/blob/31d9f07e9de04f0accdf30b666d69ae71069559a/workflow/scripts/create_mutref.py#L986-L1012" target="_blank" rel="noopener">this script</a>)</li> <li><code>vcfstats.txt</code> - VCF statistics produced by paftools.js vcfstat on the truth VCF</li> </ul> <p>For information about each sample, refer to the <a href="https://github.com/mbhall88/NanoVarBench/blob/31d9f07e9de04f0accdf30b666d69ae71069559a/config/pep/samples.csv" target="_blank" rel="noopener">samplesheet</a> and <a href="https://doi.org/10.1101/2024.03.15.585313" target="_blank" rel="noopener">paper</a>.</p>
Source data of the MultiSTAAR manuscript "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies".
<p>This dataset serves as the source data for Figures 2-3 and Extended Data Figures 1-2 of the MultiSTAAR manuscript titled "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies". MultiSTAAR is a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies.<br><br><strong>Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and ncRNA multi-trait analysis of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerids (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Figure 3.</strong> TOPMed genetic region (2-kb sliding window) unconditional multi-trait analysis results of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) using TOPMed data (<em>n</em> = 61,838).<br><br><strong>Extended Data Figure 1.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of fasting glucose (FG) and fasting insulin (FI) using TOPMed data (<em>n</em> = 21,731).<br><br><strong>Extended Data Figure 2.</strong> Manhattan plots and Q-Q plots for unconditional gene-centric coding, noncoding and genetic region (2-kb sliding window) multi-trait analysis of C-reactive protein (CRP), interleukin 6 (IL-6), lipoprotein-associated phospholipase A2 (Lp-PLA2) activity, and lipoprotein-associated phospholipase A2 (Lp-PLA2) mass using TOPMed data (<em>n</em> = 9,380).</p>
In silico analysis dataset for HPDL missense variants
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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.