This dataset contains VCF files generated from long-read sequencing analyses of 40 individuals with rare genetic diseases.

Variants were derived from pangenome graphs constructed using the minigraph-cactus pipeline (v2.9.2). Graph-based variants were obtained through deconstruction and subsequent vcfbub processing to produce standardized multisample VCF representations. Genomic coordinates are reported relative to the GENCODE release 46 reference (GRCh38.primary_assembly.genome.fa).

Two multisample VCFs are provided in this dataset.

The first VCF (SNUH.vcf.gz) includes 77 samples (1 CHM13 reference, 47 HPRC samples, and 29 rare disease samples) based on high-depth (30×) long-read assemblies from PacBio and Oxford Nanopore sequencing.

The second VCF (SNUH_lowDepth.vcf.gz) includes 12 samples (1 CHM13 reference and 11 rare disease samples) based on low-depth (10×) PacBio long-read assemblies.

This table contains information on genotypes, variants, and annotations in the genome. It can be used for research and comparison of different populations to gain insights into genetic diversity and disease susceptibility. VCF data for Drosophila melanogaster assembly from EBI (as it is on January…

This table contains information on genotypes, variants, and annotations in the genome. It can be used for research and comparison of different populations to gain insights into genetic diversity and disease susceptibility. VCF data for Drosophila melanogaster assembly from EBI (as it is on January…

The genotype dataset with 17,717,568 SNPs was used for a resampling-based genome-wide association study on 162 phenotypes from two partially overlapping maize association panels viz, SAM and WiDiv panels. SNPs were filtered to remove markers with a minor allele frequency of less than 0.01 or proportion of heterozygous SNP calls greater than 0.1 to produce the final SNP set

Variant Calling Format (VCF) files associated with the publication entitled: "Selective whole-genome amplification reveals population genetics of Leishmania braziliensis directly from patient skin biopsies"

This repository contains two VCF files that were generated from the called genotyped obtained from ANGSD. One VCF file contains variants obtained from whole-genome sequencing data from one individual sample of Aedes bromeliae (aedesBromeliae_1sample_Brom.mdp8.vcf.gz ) The other VCF file contains variants obtained from whole-exome sequencing data from 71 individual samples belonging to different populations in Africa and outside of Africa (aedesAegypti_71samples_AEGY.allpop.J18.AllSites.REFpol.CALLED.vcf_.gz). The data is accompanied by the raw sequencing data available in SRA with the accession number SRP092518 and with the BioProject number PRJNA349448 More information about the project is available in the manuscript that is under preparation.

No description is available. Visit https://dataone.org/datasets/sha256%3A3a940d9e06047c502344f63cabf7e31463585ad77dab18f2b7ec4fac375a1226 for complete metadata about this dataset.

Genotyping of known SNPs from ClinVar using the VCF and gVCF file formats and the number of homozygous reference sites and no-calls based on WGS data.

This synthetic dataset release has been generated in the context of the Better Project, funded by the European Union’s Horizon program and the UK Research and Innovation Program. The synthetic datasets mimic healthcare multimodal data (clinical and genomic) from different hospitals. It is intended to be reused for algorithm and pipeline testing for healthcare data management and analysis. The release includes healthcare data combining tabular data (in csv, tsv and xlsx format) and genomic resources in VCF format (Version 4.2 Specification). Three use cases are represented, with subfolders simulating data from distinct hospitals. Content: Use Case 1 condition: Hypotonia synthetic datasets: VCF files and tabular data (xlsx, tsv) description: For each of two hospitals, it is provided tabular data and VCF resources. Tabular data provides information on genomic, phenotypic, biological and clinical data (baseline and dynamic) according to domain rules. Regarding VCF resources, each dataset contains 300 patients, 50% of whom are affected by hypotonia and have a homozygote mutation overlapping the JUN gene region in the hg19 genome (chr1:59246463-59249719), and other random variants distributed throughout the genome. Use Case 2 condition: Inherited Retinal Diseases synthetic datasets: VCF files and tabular data (xlsx) description: For each of two hospitals, it is provided tabular data and VCF resources. Tabular data provides information on electrophysiological and visual field data according to standard clinical eye examination protocols. The dynamic clinical data has been generated to mimic patients showing different clinical progression depending on the affected gene. Measures are generated for both eyes, which may present differences in progression. Regarding VCF resources, each dataset contains 200 patients, of which 50% have mutations overlapping gene ABCA4 and 50% with mutations overlapping gene RPGR. Details on included variants for each gene extracted from ClinVar can be found in the ANNEX below. Use Case 3 condition: Autism Spectrum Disorder synthetic datasets: tabular data (csv and tsv) description: The generated dataset simulates patients with ASD, and provides information on demographic variables, neurodevelopmental and psychiatric comorbidities, family psychiatric history, bullying-related variables, abuse-related variables, sexual and gender-related descriptors, passive ideas of death, self-harm indicators and suicidal-intention-related features. Datasets have been generated with synthetic biases such as correlations and clusters to provide richness to the generated data. No VCF resources are included for this use case. ANNEX (UC2) The variants included in the VCF files for the ABCA4 gene are: ##fileformat=VCFv4.2 ##FORMAT=

The study that produced this dataset aimed to discover and analyze polymorphisms in abaca (Musa textilis) vital for varietal authentication and cross-species genotyping for advanatageous traits such as disease resistance, climate change resilience and enhanced agronomic traits. This dataset contains the resulting genotype calls in abaca (within M. textilis and between M. textilis and other Musa spp.) stored in variant call format (VCF) files. The genotypes are in the form of SNPs or InDels.

The VCf files starting with 'Mtextilis' and 'Musa' pertain to genotypes mined within M. textilis and between Musa spp., respectively. The VCF files containing SNPs or InDels are denoted by 'SNPsonly' and 'Indelsonly' file labeles, respectively. The 'AP' label indicate that the reference genome used for mapping and variant calling is a polished version of Galvez et al. (2020)'s reference genome. The 'minQ40' label indicates that the VCF files contains only SNPs and InDels with mapping quality of at least 40. The 'geno0.1' and 'pruned' labeles indicate that genotypes having at most 10% missing genotypes and those that are pruned-in (based on linkage disequilibrium thresholds) were selected.

Verticillium dahliae is an important soil-borne pathogen causing Verticillium wilt. It is also the primary causal agent of the Potato Early Dying, a disease complex involving the root-lesion nematode. Here, we report the whole-genome sequencing of 192 isolates of V. dahliae originating from the major potato production areas across Canada. Our results yielded a resource of 277,010 genetic variations that will be useful for genetic analyses and revealed the presence of two major lineages, both present in all provinces but exhibiting differences in regional prevalence.

The American chestnut (Castanea dentata) is a functionally extinct tree species that was decimated by an invasive fungal pathogen in the early 20th century. An understanding of the genomic architecture of local adaptation in wild American chestnut was necessary in order to deploy locally adapted, disease-resistant American chestnut populations. Here, we characterize the genomic basis of climate adaptation in remnant wild American chestnut, develop new computational methods, and evaluate the adaptive genomic content captured within backcross breeding populations. Whole genome re-sequencing data of 356 trees from Sandercock et al. (2022) coupled with genotype-environment association methods identified 18483 climate associated loci.Methods: VCF file: The ~21 million SNP dataset from Sandercock et al. (2022) was first imputed using BEAGLE and filtered to remove SNPs with MAF < 0.05. Climate associated loci were then identified using RDA and LFMM2 genotype-environment association methods. Seed zone shape files: Three seed zones were identified using the ~18k climate associated loci. These regions partition the chestnut range into geographic seed zones that reflect relatively homogeneous areas with respect to multivariate adaptive genomic variation. These regions can be used to conserve germplasm ex situ and guide subsequent breeding crosses that lead to climate-matched restoration populations. gmbigxhorn.jtl.map.2022.csv is a genetic map generated from American chestnut backcross genotyping-by-sequencing data. R code for estimating the average migration distance for each seed zone under future climate change conditions.

VCF file of sites called across all pipelines

Exome sequencing data (VCF files) of the nine adult patients with liver diseases.For exome sequencing, the library was prepared using Illumina DNA Prep with Exome 2.5 Enrichment product and sequenced on a NovaSeq 6000 instrument (Illumina, San Diego, CA). Reads were aligned to the GRCh38 Human Reference Genome using the Illumina DRAGEN Bio-IT Platform v3.9.

These datasets are important to genomics researchers because they characterize several aspects of what the scientific community has learned to date about human sequence variants. Making this human annotation data freely available in GCP will enable researchers to focus less on data movement and management tasks associated with procuring this data and instead make immediate use of the data to better understand the clinical relevance of particular variant such as disease causing or protective variants (ClinVar), search a catalog of SNPs that have been identified in the human genome (dbSNP), and discover how frequently a particular variant occurs across the human population (1000Genomes, ESP, ExAC, gnomAD) This human annotation dataset contains both a mirror of the original Variant Call Files (VCF) files from NCBI, NHLBI Exome Sequencing Project (ESP) and ensembl as Google Cloud Storage (GCS) objects. In addition, these human sequence variants have also been translated into a particular variant table format and made available in Google BigQuery giving researchers the ability to use cloud technology and code repositories such as the Verily Life Sciences Annotation Toolkit to perform analyses in parallel. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

T-box transcription factor 1 Enables protein homodimerization activity and sequence-specific double-stranded DNA binding activity. Involved in several processes, including chordate embryonic development; gland development; and soft palate development. Predicted to be active in chromatin and nucleus. Implicated in several diseases, including DiGeorge syndrome; congenital heart disease (multiple); hypoparathyroidism; sensorineural hearing loss; and velocardiofacial syndrome. Biomarker of congenital heart disease. This gene is a member of a phylogenetically conserved family of genes that share a common DNA-binding domain, the T-box. T-box genes encode transcription factors involved in the regulation of developmental processes. This gene product shares 98% amino acid sequence identity with the mouse ortholog. DiGeorge syndrome (DGS)/velocardiofacial syndrome (VCFS), a common congenital disorder characterized by neural-crest-related developmental defects, has been associated with deletions of chromosome 22q11.2, where this gene has been mapped. Studies using mouse models of DiGeorge syndrome suggest a major role for this gene in the molecular etiology of DGS/VCFS. Several alternatively spliced transcript variants encoding different isoforms have been described for this gene. [provided by RefSeq, Jul 2008]

Genotype files for 34 bread wheat CAIGE accessions genotyped with the Illumina Infinium Wheat Barley 40K v1.2 BeadChip. Genotype data is available in four formats: Tab-delimited text file - unanchored to any genome (260918_CAIGE-BreadWheat-DiseaseCore_Raw_unphased_unanchored_genotypes.tsv.gz) Raw comprehensive Variant Call Format (VCF) file - contains all markers, including those anchored to the specified genome, and those that could not be anchored being placed in 'ChrUnk' (eg: 260918_CAIGE-BreadWheat-DiseaseCore_IWGSC-v2.1_Raw_phased_anchored_comprehensive.vcf.gz) Raw unique VCF file - contains only markers that could be anchored to the specified genome, with redundant markers (mapping to duplicated positions) removed (eg: 260918_CAIGE-BreadWheat-DiseaseCore_IWGSC-v2.1_Raw_phased_anchored_unique.vcf.gz) Filled-in unique VCF file - same as the raw unique file, but missing genotype calls have been imputed (eg: 260918_CAIGE-BreadWheat-DiseaseCore_IWGSC-v2.1_Filled_in_phased_anchored_unique.vcf.gz) Users are advised to refer to the README file for more information. Generally, files 3) or 4) are more appropriate for immediate use in downstream bioinformatics tools, while 1) and 2) should be used when a user is interested in analysing the maximum number of markers, regardless of being genome-anchored. Sample information is contained in 260918_CAIGE-BreadWheat-DiseaseCore_Biosample_sheet.tsv. Detailed descriptions of each file are included in the README. Funded by the Australian Grains Genebank Strategic Partnership, a $30M joint investment between the Victorian Government and the Grains Research and Development Corporation that aims to unlock the genetic potential of plant genetic resources for the benefit of Australian grain growers.

Dataset Overview

This dataset contains genetic variants and their clinical significance (pathogenicity) extracted from the ClinVar database. Structured according to VCF (Variant Call Format) standards, this dataset can be used to identify genetic variants and analyze their associations with human diseases.

CHROM: The chromosome number where the variant is located (e.g., 1-22).

POS: The 1-based starting position of the variant on the respective chromosome.

ID: The unique identifier for the variant (typically the dbSNP rsID number).

REF: The standard (original) reference allele found at that location in the reference genome (A, C, G, T, or longer sequences).

ALT: The alternative allele(s) observed in the sample that deviates from the reference sequence.

QUAL: The quality score of the variant call. (In this dataset, it mostly appears as "." indicating that no quality filter was applied or the information is missing).

FILTER: Indicates whether the variant passed specific quality filters. (Mostly appears as "." in this dataset).

INFO: A comprehensive field containing detailed additional information about the variant. It includes multiple sub-attributes in a Key=Value format (e.g., disease association CLNDN, ClinVar allele ID ALLELEID, etc.).

CLNSIG: (Clinical Significance / Target Variable) Represents the clinical impact of the variant. This is typically used as the target variable for predictive modeling in genetic disease analysis (e.g., Likely_benign, Benign, Pathogenic, etc.).

Database for phenotype genotype associations for humans. Used by clinical researchers to store standardized phenotypic information, diagnosis, and pedigree data and then run analyses on VCF files from individuals, families or cohorts with suspected Mendelian disease.

Novel pathogens have become a major challenge faced by wildlife in the Anthropocene. White-nose syndrome (WNS), a fungal pathogen, has decimated bat populations across North America over the last two decades. Demographic and physiological evidence of resistance in one heavily affected species, Myotis lucifugus, has prompted multiple attempts to delineate the genomic underpinnings, but they show little congruence in their findings. This may be due, in part, to the limitations of the genomic resources utilized and/or analytical approaches employed. Here, we performed high-coverage whole-genome resequencing of M. lucifugus sampled prior to (n = 29) and 10 years after the arrival of WNS (n = 30), aligned to a new reference genome to identify signatures of selection associated with pathogen resistance. Using 41.9 million SNPs, we implemented a combination of hard and soft sweep detection analyses, leading to discovery of 405 genes with robust evidence of selection. Of these, 241 (59.5 %) wer..., , , # README: A multifaceted approach reveals complex genomic mediation of white-nose syndrome resistance in the little brown bat (Myotis lucifugus)

Dataset DOI: 10.5061/dryad.ncjsxkt66

Description of the data and file structure

This dataset contains all analytical code for this manuscript, along with associated data files, two versions of the final VCF, and selection statistic output files. The genome annotation used with this data is available through https://github.com/docmanny/myotis-gene-annotations. Raw data for this study are available through the NCBI Short Read Archive (BioProject PRJNA1353610, accession nos. SAMN52933055–SAMN52933113).

Data Files

VCFs:

• gatk.snp.qual_hard_filtered_autosomes.vcf.gz -- full set of filtered SNPs
• gatk.snp.qual_hard_filtered_autosomes_thin.vcf.gz -- filtered SNPs thinned by 10 Kbp distance

SNP Calling Pipeline Input File..., ,