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Additional datasets and code accompanying the article "Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines"
<p> </p> <p> </p> <p>Datasets and code accompanying the manuscript titled “Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines” by Rajarshi Ghosh, Ninak Oak and Sharon E. Plon.</p>
On a Microservice System Benchmark with Multiple Architected Variants for Software Engineering Research
Open the record for dataset details and reuse information.
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
<p>*.sh and *.R are codes to test rare coding variants for association with ISCAD.</p> <p>Petrazzini_etal_2024_*_level_meta_analysis.txt.gz are summary statistics of variant- and gene-level associations of rare coding variants in the exome sequences of 604,914 individuals with an in-silico score for coronary artery disease (ISCAD).</p> <p>Chromosomal positions are mapped to the GRCh38 (hg38) human genome reference.</p> <p>Directions of effect correspond to associations in the UK Biobank, the All of Us Research Program, the BioMe Biobank sample 1 and the BioMe Biobank sample 2, in that order.</p>
The expressivity of rare coding variants for blood lipids in over a million individuals
<p>The supplemental data and codes for Koyama et al. "The expressivity of rare coding variants for blood lipids in over a million individuals"</p>
Geocoded variant of United Nations Code for Trade and Transport Locations
<div></div> <div> <div> <div> <div>The <a href="https://unece.org/trade/uncefact/unlocode">United Nations Code for Trade and Transport Locations</a> dataset provides information on the codes known as "UN/LOCODE". </div> <div>This version of the dataset, based on the csv files of version 232, has extra information based on pycountry (names of administrative regions, country names, flags) and on most important on location.</div> <div> </div> <div> </div> <div>The dataset has the following extra fields:<br> <table> <tbody> <tr> <td>Column name</td> <td>Example</td> <td>Description</td> <td>Source</td> </tr> <tr> <td> <pre>alpha_2_country</pre> </td> <td> <pre>AD</pre> </td> <td>2 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>alpha_3_country</pre> </td> <td> <pre>AND</pre> </td> <td>3 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>flag_country</pre> </td> <td> <pre>🇦🇩</pre> </td> <td>unicode flag</td> <td>pycountry</td> </tr> <tr> <td> <pre>name_country</pre> </td> <td> <pre>Andorra</pre> </td> <td>Country name, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>numeric_country</pre> </td> <td> <pre>020</pre> </td> <td>Country code</td> <td>pycountry</td> </tr> <tr> <td> <pre>official_name_country</pre> </td> <td> <pre>Principality of Andorra</pre> </td> <td>Country official name</td> <td>pycountry</td> </tr> <tr> <td> <pre>score</pre> </td> <td>95</td> <td>Confidence code of geocoding [0-100/nan]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geocoded</pre> </td> <td>True</td> <td>Was the coordinate derived from geocoding [true/false]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geometry</pre> </td> <td> <pre>POINT (1.516666666666667 42.5)</pre> </td> <td>Point coordinate</td> <td>ArcGIS if geocoded else original UNLO </td> </tr> </tbody> </table> <br>The figure below shows the locations (n=23663) that have been geocoded in this dataset in green and the existing locations in pink. </div> <div><br></div> <div> </div> <div>Ownership of the original data recides at UNLO and the participants. </div> </div> </div> </div>
Data for: Raw count data, transcribed variant count data, and reference genomic annotation files for Boocock et al. 2024
<p>Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in <em>Saccharomyces cerevisiae</em> by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene <em>GPA1</em>, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.</p>
Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes
<p><strong>Supplemental files contain all necessary datasets to reproduce the analysis and figures in "Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes" Code and additional files can be found at https://github.com/FowlerLab/VEP-calibrations</strong></p>
Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance
<p>Datasets used for the manuscript: <em>Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance</em></p> <p><em>pandemic_daily_passenger_counts.tsv</em>: An overview of daily passenger arrival counts at Amsterdam Schiphol Airport per continent of origin during the study period 16-02-2020 - 04-09-2022</p> <p><em>pre-pandemic_daily_passenger_averages.tsv: </em>An overview of mean daily passenger arrival counts at Amsterdam Schiphol Airport in the pre-pandemic period 2017-2019.</p> <p><em>viral_load_data.tsv: </em>Sample metadata (sample identifier, sampling date, flow, average # particles per ml, and flow-corrected viral-load) for samples taken at the wastewater treatment plant of Amsterdam Schiphol Airport.</p> <p><em>wastewater_variant_frequencies.tsv: </em>SARS-CoV-2 lineage estimates in samples taken at the wastewater treatment plant of Amsterdam Schiphol Airport, analyzed using whole-genome tiled amplicon sequencing.</p> <p> </p> <p> </p> <p> </p>
IGM Variant Oncogenicity Classifications
<p>IGM Variant oncogenicity classification data curated using the Variation Categorizer. </p>
X-linked multi-ancestry meta-analysis reveals tuberculosis susceptibility variants
<p>Globally, tuberculosis (TB) presents with a clear male bias that cannot be completely accounted for by environment, behaviour, socioeconomic factors, or the impact of sex hormones on the immune system. This suggests that genetic and biological differences, which may be mediated by the X chromosome, further influence the observed male sex bias. The X chromosome is heavily implicated in immune function and yet has largely been ignored in previous association studies. Here we report the first multi-ancestry X chromosome specific meta-analysis on TB susceptibility. We identified X-linked TB susceptibility variants using seven genotyping data sets and 20,255 individuals from diverse genetic ancestries. Sex-specific effects were also identified in polygenic heritability between males and females along with enhanced concordance in direction of genetic effects for males but not females. These sex-specific genetic effects were supported by a sex-stratified and combined meta-analysis conducted using the X chromosome specific XWAS software and a multi-ancestry analysis using the MR-MEGA software. Seven significant associations were identified. Two in the overall analysis (rs6610096, rs7888114) and a second for the female specific analysis (rs4465088) including all data sets. For the ancestry specific meta-analysis three significant associations were identified for males in the Asian cohorts (rs1726176, rs5939510, rs1726203) and one in females for the African cohort (rs2428212). Several genomic regions previously associated with TB susceptibility were reproduced in this study, along with strong ancestry-specific effects. These results support the hypothesis that the X chromosome and sex-specific effects could significantly impact the observed male bias in TB incidence rates globally. </p>
Comprehensive identification of pathogenic variants in retinoblastoma by long- and short-read sequencing
<p>Retinoblastoma (RB) is the most common intraocular malignancy in childhood. The causal variants in RB are mostly characterized by previously used short-read sequencing (SRS) analysis, which has technical limitations in identifying structural variants (SVs) and phasing information. Long-read sequencing (LRS) technology has significant advantages over SRS in detecting SVs, phased genetic variants, and methylation. In this study, we comprehensively characterized the genetic landscape of RB using combinatorial LRS and SRS of 16 RB tumors and 16 matched blood samples. We detected a total of 232 somatic SVs, with an average of 14.5 SVs per sample across the cohort. We identified 20 distinct pathogenic variants, including <span>three</span> novel small variants and <span>five</span> <span>somatic</span> SVs. Furthermore, our analysis shows that the vast majority (93.8%) of <em>RB1</em>-disrupted patients fit the two-hit hypothesis through diverse types, including the biallelic hypermethylated promoter as well as small and large compound heterozygous mutations which were missing in SRS analysis. By constructing the evolutionary history of all genetic variants, we reveal the evolution trends that <em>RB1</em> disruption early and followed by copy number changes, including amplifications of Chr2p, and deletions of Chr16q, during RB tumorigenesis. Altogether, we characterize the comprehensive genetic landscape of RB, providing novel insights into the genetic alterations and mechanisms contributing to RB initiation and development. Our work also establishes a framework to analyze genomic landscape of cancers based on LRS data. <br><br>We took IGV screenshots of SV and SNV/indels. The IGV naming conventions for SV is: sample name, chromosome 1, start, chromosome 2, end, SV type, SV length, SV breakpoint. The IGV naming convention for SNV/indels is: sample name, chromosome, position, reference allele, alternate allele.</p>
Dataset 2. Variant Distribution of Majority Molecular Phenotype Classifications Relative to All Classifications
<p>A variant may occur in multiple individuals, tissue types, or splice sites. A consensus (majority) molecular phenotype classification for the variant is displayed on ValidSpliceMut, generated by observing all affected cases and associated classifications. For each expression validated variant, the phenotypes are classified as per Figure 2 of Shirley et al. 2019 (<a href="https://doi.org/10.12688/f1000research.17204.3" target="_blank" rel="noopener">https://doi.org/10.12688/f1000research.17204.3</a>). The bins on the horizontal axis of these histograms represent the count of splicing variants with the most frequent classification divided by the total number of cases affected by a variant. Bar height represents the number of variants having the majority classification within the appropriate bin. </p> <p>Below each histogram is a table that describes the distribution of majority molecular phenotypes in each bin. The three bins consist of mutations designated as variants that cause allele-specific alternative splicing (category 1), those that are likely aberrant (category 2) and mutations that are aberrant (category 3). For a full description of how these molecular phenotype classifications are determined, see Shirley <em>et al. </em>2019. Figure 2 of the paper describes the algorithm we use to ensure that variants with no effect on splicing have been eliminated previous to this analysis.</p> <p>To gain more information about the mutations in a particular bin, an data file has been provided for each of these figures. To identify which mutations occur in each bin, sort the data file in order of the <em>`%Mol_Phenotype`</em> column which was used to designate histogram bins. Once a mutation of interest has been found, download <a href="../records/3522192">Dataset 1 on Zenodo</a> (Filename: ‘ValidSpliceMut_TabDelimited_v4.txt’) and search for the mutation (mutation names will be identical). The mutation will appear in Dataset for however many patients the mutation was identified in (from the TCGA and ICGC Cancer Projects). From Dataset 1, users can acquire the mutation’s associated gene, its position within a gene, the Information-Theory based prediction that the mutation would have on splicing, and the given evidence of splicing using the <a href="https://f1000research.com/articles/3-8">Veridical algorithm</a>. The latter provides how many reads were found in RNAseq data that supported an aberrant splicing event (exon skipping, intron inclusion, etc).</p> <p>Files/archives provided:</p> <p>1 Supplementary figure to Shirley et al. <em>F1000Research</em> 2019, <strong>7</strong>:1908. Dataset 2 contains six histograms and supporting tables (generated with GraphPad Prism v6.0). A variant may occur in multiple individuals, tissue types, or splice sites. Histogram bins show the fraction of splicing molecular phenotype classifications relative to the major consensus phenotype among all cases for variants that are present in at least 2, 3, 5, 10, 15, and 20 different tissue types.</p> <p>2. Aggregated variant data/consensus phenotypes used to generate histograms, including tissue types.</p> <p>3. Software to generate histograms from variants validated in Dataset 1 (DOI <a href="../doi/10.5281/zenodo.1488210">10.5281/zenodo.1488210</a>).</p>
Identification of genetic variants associated with clinical features of sickle cell disease
Open the record for dataset details and reuse information.
Data from: The role of conflict in the formation and maintenance of variant sex chromosome systems in mammals
<p>The XX/XY sex chromosome system is deeply conserved in therian mammals, as is the role of <em>Sry</em> in testis determination, giving the impression of stasis relative to other taxa. However, the long tradition of cytogenetic studies in mammals documents sex chromosome karyotypes that break this norm in myriad ways, ranging from fusions between sex chromosomes and autosomes to Y chromosome loss. Evolutionary conflict, in the form of sexual antagonism or meiotic drive, is the primary predicted driver of sex chromosome transformation and turnover. Yet conflict-based hypotheses are less considered in mammals, perhaps because of the perceived stability of the sex chromosome system. To address this gap, we catalogue and characterize all described sex chromosome variants in mammals, test for family-specific rates of accumulation, and consider the role of conflict between the sexes or within the genome in the evolution of these systems. We identify 152 species with sex chromosomes that differ from the ancestral state and find evidence for different rates of ancestral to derived transitions among families. Sex chromosome-autosome fusions account for 80% of all variants whereas documented sex chromosome fissions are limited to three species. We propose that meiotic drive and drive suppression provide viable explanations for the evolution of many of these variant systems, particularly those involving autosomal fusions. We highlight taxa particularly worthy of further study and provide experimental predictions for testing the role of conflict and its alternatives in generating observed sex chromosome diversity.</p>
The role of structural variants in pest adaptation and genome evolution of the Colorado potato beetle, Leptinotarsa decemlineata (Say)
<p>Structural variation has been associated with genetic diversity and adaptation in diverse taxa. Despite these observations, it is not yet clear what their relative importance is for microevolution, especially with respect to known drivers of diversity, e.g., nucleotide substitutions, in rapidly adapting species. Here we examine the significance of structural variants (SVs) in pesticide resistance evolution of the agricultural super-pest, the Colorado potato beetle,<em> Leptinotarsa decemlineata</em>. By employing a parent offspring trio sequencing procedure, we develop highly contiguous reference genomes to characterize structural variation within this species. These updated assemblies represent >100-fold improvement of contiguity and include derived pest and ancestral non-pest individuals. We identify >200,000 SVs, which appear to be non-randomly distributed across the genome as they co-occur with transposable elements and genes. SVs intersect exons for a large proportion of gene annotations (~20%) and are associated with insecticide resistance, development, and transcription, most notably cytochrome P450 (CYP) genes. To understand the role that SVs might play in adaptation we measure allele frequencies of SVs for an additional 57 individuals, using whole genome resequencing data, representing pest and non-pest populations of North America. Incorporating multiple independent tests of significance using SNP data, we identify 14<strong> </strong>positively selected genes that include SVs and SNPs of elevated frequency within the sampled pest lineages. Among these, four are associated with insecticide resistance. One of these genes, glycosyltransferase-13, is a duplicated gene enclosed within a structural variant that resides inside the <em>CYP4g15</em> genic region. Both gene products have been observed to be co-induced during insecticide exposure. These results demonstrate the significance of structural variations as a genomic feature to describe species history, genetic diversity, and adaptation.</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
Supplemental Data for "Identifying novel variants of small molecules through database search of mass spectra"
<p>Supplemental Dataset 1 contains GNPS dataset information for the large scale search of GNPS vs Pubchem.</p> <p>Supplemental Dataset 2a contains the top scoring exact mode hit for each spectrum against PubChem and COCONUT. Files are split into "*chunk*" files of up to 10 million records each.</p> <p>Supplemental Dataset 2b contains the top scoring variable mode hit for each spectrum against COCONUT.</p> <p>Supplemental Dataset 3 contains mass spectra provided by Waters Corporation to analyze impurities of Imatinib.</p>
GCK variants and their annotations for "Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes"
<p>This file provides annoatations for GCK variants as dsecribed in "Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes"</p>
Identifying genetic variants associated with chromatin looping and genome function
<p><span>Here<span> we present a comprehensive HiChIP dataset on naïve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants. Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the <em>RNASET2</em> locus). Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying novel classes of QTLs linked to cell-specific gene expression and connectivity.</span></span></p> <p> </p> <p><span><span>This repository contains the source code, supplementary datasets for the manuscript (Nature Communications 2024).</span></span></p>
Gene expression QTL lead variants in macrophages stimulated with IFNg and Salmonella
<p>Lead eQTL variants from RASQUAL and FastQTL analyses.</p> <p>Columns in the RASQUAL results:</p> <ul> <li>snp_id - id of the lead eQTL variant</li> <li>gene_id - id of the gene</li> <li>chisq - Chi-square statistics from RASQUAL</li> <li>p_nominal - Nominal p-value from RASQUAL</li> <li>p_eigen - Nominal p-value corrected by EigenMT for the number of indepedent tests in the cis region of the gene.</li> <li>n_tests - Number of independent tests estimated by EigenMT</li> <li>fdr_thresh - Empirical FDR 10% threshold. Those eQTLs for which p_eigen < fdr_threshold are significant at the 10% FDR level.</li> </ul> <p> </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.