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• DAC for the study EGAS00001005773

  Dac EGAC00001002411

• Tytgat Institute for Liver and Intestinal Research Data Access Committee

  Dac EGAC00001002437

• DAC for COPD human sputum 16s rRNA gene sequencing data

  Dac EGAC00001002779

• EGAD00010000819

  Summary statistics from meta-analysis for BP phenotypes

  Dataset EGAD00010000819

• Data access committee for sequencing data at Kyoto University

  Dac EGAC50000000045

• Data access committee for neural retina and retinal organoid data

  Dac EGAC50000000019

• bed_files

  Bed files as whitelist for Tapestri Insights analysis

  Dataset EGAD00010002560

• DAC for access to array genotypes from the PREGO biobank.

  Dac EGAC00001003484

• Centre for Drug Repurposing and Medicines Research Data Access Committee

  Dac EGAC00001002764

• XClone for analyzing somatic copy number alterations Data Access Committee

  Dac EGAC00001003492

• Single cell multi-omics committee for CK-AML

  Dac EGAC00001003353

• Developmental and Stem Cell Biology department - Hospital for Sick Children

  Dac EGAC00001002951

• Anne Eugster, Center for Regenerative Therapies Dresden, TU Dresden

  Dac EGAC50000000315

• WGBS and oxBS-seq for APL

  Study EGAS00001005610

• Data access committee for sequencing data generated by Wyatt Lab

  Dac EGAC50000000538

• University of Melbourne Centre for Cancer Research (UMCCR) Data Access Committee

  Dac EGAC00001003567

• AGLCD sequencing data

  sequencing datasets for AGLCD patient

  Dac EGAC50000000852

• MK Clinical Trial DAC

  DAC for MK Clinical Trial Data

  Dac EGAC50000000677

• NHLBI's Collection of Datasets for General Research Use (Public Posting of Genomic Summary Results: Allowed)

  This collection contains all of NHLBI's authorized individual-level genomic datasets currently in dbGaP that are approved for General Research Use (GRU) and have no further limitations beyond those outlined in the model Data Use Certification Agreement. Genomic Summary Results (GSR) sharing is allowed (unrestricted) or not applicable. To request access to this study collection, select phs003132 in the dbGaP Authorized Access System.

  Study phs003132

• MicroRNA Biomarkers for Prediction of Preeclampsia

  Maternal serum was collected between 17-28 weeks gestation. Samples were obtained from the high-risk Placental Study at the University of California, San Diego (n=48) and from the average-risk Proteomic Assessment of Preterm Risk (PAPR) Study at Sera Prognostics (n=164). RNA was extracted from 500 microliter of maternal serum using the miRNeasy Micro kit (Qiagen). NEBNext Small RNAseq Library Preparation Kit was used for library construction.

  Study phs002016

• Large Scale Meta-analysis Characterizes Genetic Architecture for Common Psoriasis-associated Variants

  The genotypes obtained from the Affymetrix Biobank Plus array constituted one of eight Caucasian datasets used in a large genome-wide meta-analysis of the association of psoriasis cases versus unaffected controls. With a combined effective sample size of > 39,000 individuals, this study identified 16 new psoriasis loci achieving genome-wide significance, increasing the total number of identified psoriasis loci for European-ancestry populations to 63.

  Study phs001306

• T-bet+ CXCR3+ B cells drive hyperreactive B-T cell interactions in multiple sclerosis

  Data Access Committee means the following members of the Institute of Experimental Immunology, UZH, and investigators of the publication, to which the Data Access refers and who are responsible for making the data access decisions for the genotype and gene expression generated by UZH or respectively the epigenetic data, which had been generated at Karolinska Institute. Christian Münz, PhD; Co-Director of Institute of Experimental Immunology, University of Zurich Roland Martin, MD; Affiliated Group Leader (to Institute Münz) of Institute of Experimental Immunology Institute of Experimental Immunology Ivan Jelcic, PhD, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland Maja Jagodic, PhD, Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden

  Dac EGAC50000000464

• Long-read sequencing for cell-free DNA analysis (human)

  In this study, we compared the two long-read sequencing platforms, namely the single-molecule real-time sequencing by Pacific Biosciences and nanopore sequencing by Oxford Nanopore Technologies, for the analysis of cell-free DNA from plasma. Cell-free DNA from plasma samples of 31 pregnant women at different trimesters, 6 hepatitis B carriers, and 8 patients with hepatocellular carcinoma were sequenced with the two platforms.

  Study EGAS00001006328

• Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS).

  The datasets includes 21 samples from 7 families with Bosma arhinia microphthalmia (BAMS). For details of the study please refer to the manuscript "De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development", Nature Genetics 2017. Each sample was exome sequenced and the family ID in sample description refers to the Individual ID in Supplementary figure 2 of the manuscript.

  Study EGAS00001002193

• We evaluate the PGD/PGS including 129 couples with NGS test and 266 couples with SNP-array test for the detection of embryonic chromosomal abnormalities.

  A total of 395 couples were subjected to IVF-PGD treatment, including 129 couples with NGS test and 266 couples with SNP-array test for the detection of embryonic chromosomal abnormalities. The NGS test was performed using low coverage whole genome sequencing with HiSeq 2000 platform. And the SNP array test was using Affymetrix Gene Chip Mapping Nsp I 262K.

  Study EGAS00001000981

• DNA sequencing for human normal endometrial glands

  To elucidate the timing and mechanism of the clonal expansion of somatic mutations in cancer-associated genes in the normal endometrium, we conducted whole-exome and whole-genome sequencing for 56 endometrial glands and matched blood samples from 4 women. By collecting endometrial glands from different parts of the endometrium, we showed that multiple glands with the same somatic mutations occupied substantial areas of the endometrium.

  Study EGAS00001005822

• Berlin Neuroblastoma Patient Genomic Data from Targeted Sequencing for Detection of TERT rearrangement breakpoints to monitor neuroblastoma

  Our objective was to establish a liquid biopsy-based monitoring strategy for pediatric high-risk neuroblastomas that are harboring genomic TERT rearrangements at diagnosis. TERT rearrangement breakpoints are detected by a hybrid capture-based neuroblastoma DNA panel sequencing (published in PMID: 34442335) in tumor material and are reflected in cell-free tumor DNA and can serve as robust biomarkers for disease activity. Within the dataset, 5 tumors of 4 pediatric patients with a neuroblastoma were DNA sequenced. Provided are FASTQ data files, bam and bambai files, as well as breakpoint spanning and encompassing read (enspan) bam and bambai files. Sequencing bam data files are aligned to GRCh37.p13 reference genome (processed).

  Dataset EGAD00001011088

• PCA Atlas Chromium scRNA-seq and demultiplexing support (FASTQs, BAMs, capture-level VCFs and mapping tables)

  This dataset contains Chromium single-cell RNA-seq data and demultiplexing support for the PCA Atlas study EGAS00001008332. Included data objects: - 16 Chromium scRNA-seq FASTQ runs (EGAR accessions) for captures PCa1–PCa16. - 16 aligned BAM analyses (one Cell Ranger possorted_genome_bam.bam per capture). - 16 capture-level genotype VCF analyses derived from the Axiom UK Biobank array, used as demultiplexing panels. - demux_map.csv (EGAZ00001945520): donor–capture–sample mapping for genotype-based demultiplexing. - chromium.csv (EGAZ00001945537): run-level mapping linking captures, EGA samples, runs (EGAR), experiments (EGAX) and BAM analyses. Together these objects provide a complete view of the Chromium scRNA-seq data and the genotype-based demultiplexing support needed to reproduce donor assignment and downstream analyses.See also https://zenodo.org/records/17372603

  Dataset EGAD00001015795

• PPGL WES dataset

  Dataset includes fastq files for WES experiments for tumor samples of PPGL patients. Paired end reads fastq files are available for 87 different samples

  Dataset EGAD00001008579

• Paired RNA-Seq of fCAB treated and bisulfite treated VDH01, partly depleted for NSUN3

  Paired RNA-seq of bisulfite treated VDH01 samples control or depleted for NSUN3 (4 replicates each). The samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of fCAB treated vdh01 samples control or depleted for NSUN3(4 replicates each). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of fCAB treated VDH01 samples (3). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of bisulfite treated VDH01 samples (3). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq.

  Dataset EGAD00001008742

• Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts

  Whole Exome Sequencing was performed in a dilution series containing known amounts of human and mouse DNA, 3x 100% human 0% mouse, 2x 90/10, 3x 50/50, 2x 25/75 and 3x 0/100. A set of breast cancer clinical samples, matched normal tissue and matched PDTXs (total number = 14) were also analysed. Paired-end 75bp sequences for the dilution series and paired-end 125bp for the clinical samples were obtained on Illumina HiSeq2500; fastq files are provided. A triplicate analysis of the transcriptome using RNA-seq was also performed for the Universal Human RNA Reference and the Universal Mouse RNA Reference samples. Paired-end 150bp fastq files obtained on Illumina HiSeq4000 are provided.

  Dataset EGAD00001003800

• Targeted Validation Samples

  The dataset consists of targeted sequencing data obtained from an independent cohort of 11 patients to validate discriminative DMRs (differentially methylated regions) for Acute coronary syndrome (ACS) subtypes. The cohort includes 2 healthy subjects, 4 STEMI (ST-segment elevation myocardial infarction), 3 NSTEMI (non-ST-segment elevation myocardial infarction), and 2 UA (unstable angina) patients. The sequencing panel targeted 18,831 CpGs for analysis, reaching at least 5 reads coverage for 75% of the targeted CpGs in each sample. The sequencing was performed using the NEBNext Enzymatic Methyl-seq Module and the Nonacus Cell3TMTarget: Library Preparation kit, followed by probe hybridization, capture enrichment, and sequencing on the Novaseq 6000 platform, generating 400 million reads. The dataset is in raw fastq format.

  Dataset EGAD00001010934

• Machine Learning Guided Signal Enrichment for Plasma Tumor-burden Monitoring Dataset

  Includes 4 Datasets: 1. 35 Control Plasma WGS samples sequenced on NovaSeq V1.0 Chemistry at the New York Genome Center. Denoted by CTRL-2XX naming scheme. 2. Plasma WGS from 17 patients with Small Cell Lung Cancer. Samples extracted at either Pretreatment or Postoperative at weeks 2 or 3. Denoted by SCLC-XX naming scheme. 3. Plasma WGS from a synthetic mixing study of a high-burden melanoma plasma sample with plasma bag, at estimated tumor concentrations of 10e-3, 10e-4, 10e-5, and 10e-6 for 2 replicates. Denoted by SM-repX naming scheme. 4. Assorted Plasma, Tumor, and Normal WGS from patients with NSCLC expressing high-burden for use in model training. Denoted by NSCLC-2XX naming scheme.

  Dataset EGAD00001011352

• Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

  Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III “borrowed” GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

  Study phs000206

• Center for Common Disease Genomics [CCDG] - Inflammatory Bowel Disease (IBD) - Global Microbiome Conservancy Host Exomes

  The human body is a complex ecosystem, hosting tens of billions of bacteria, primarily in the gut. This community, known as the 'gut microbiome', is vital to human health, affecting functions ranging from metabolism, immunity, and development, to behavior. Conversely, reduced microbiome diversity and other imbalances of the gut community (dysbiosis) are associated with many diseases of the industrialized world, such as metabolic syndrome, asthma, and inflammatory bowel diseases among others. Microbiome-based interventions thus hold the potential to revolutionize our understanding of, and therapeutic approaches to, many aspects of human health and disease. Despite many advances, current understanding of the human microbiome is largely limited to majority ethnic groups in industrialized nations, and does not reflect the full diversity of human commensal microbiota. In addition to the scientific ramifications of minority underrepresentation, there are significant translational and ethical implications as well: First, failing to capture the full diversity of healthy human microbiota may limit our ability to understand and generate effective therapeutics for many microbiome-associated diseases. Second, underrepresented groups are less likely to benefit from microbiome-based medical advances tailored to well-studied populations, propagating healthcare inequities. The window of opportunity to characterize human microbiome diversity is closing rapidly. Human gut microbial diversity is diminishing around the globe, due to the spread of Westernized diets and lifestyles, which are associated with reduced microbiome biodiversity. The Global Microbiome Conservancy (GMbC) is a non-profit collaboration between scientists and communities around the world that aims to 1) preserve the full biodiversity of the human gut microbiome, 2) characterize its evolution across populations and host lifestyles and 3) investigate its interactions with the human host. We are collecting gut microbiome and human DNA samples from diverse populations around the world, from isolated groups to urbanized populations, to culture and study this essential biodiversity and to preserve it in a living "seed bank" as a long-term safeguard against future extinctions. The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of: Undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases. Understanding how best to design rare variant studies for common disease. Developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. Both exome and genotyping information of GMbC participants will help understanding how recruited human populations are structured and genetically related. This human genetic structure will be analyzed in light of gut microbiome compositional and genomic differences to illuminate associations between specific microbiome traits and human groups and the evolutionary processes involved in the co-evolution of human hosts and their gut microbiome. It will also provide variant information for critical genes shaping the composition of microbiomes, such as genes involved in innate and adaptive immunity (e.g. antimicrobial peptides) or in the regulation of the immune response (e.g. Nod2). Association studies between specific variants in these genes and microbiome features will be performed, which will then be validated with in vitro and in vivo experiments. Human data from GMbC cohorts will also increase the genetic diversity of human populations, providing additional variant information for many susceptible loci that were found to be associated with Crohn's disease and ulcerative colitis. The whole genome sequencing data generated is comprised of samples from diverse and indigenous populations. Raw sequencing data, metadata, vcf and phenotype data at the individual level for a subset of the cohort's samples are available at https://anvilproject.org/data. For questions about availability, contact help@lists.anvilproject.org. Microbiome data for the samples sequenced in this study can be found in phs002235 Global Microbiome Conservancy Sequence Data.

  Study phs002205

• EGAD00010000624

  A new beta-globin mutation responsible of a beta-thalassemia (HbVar database ID 2928) was observed in 8 unrelated French families. The mutation carriers originated from Nord-Pas-de-Calais, a Northern French region where the chief town is Lille. 5 unrelated mutation carriers were genotyped for a set of 12 microsatellites from chromosome 11, around the beta-globin gene. Among the 5 mutation carriers, 4 were genotyped for 97 European Ancestry Informative SNPs (EAIMs).

  Dataset EGAD00010000624

• EGAD00010000626

  A new beta-globin mutation responsible of a beta-thalassemia (HbVar database ID 2928) was observed in 8 unrelated French families. The mutation carriers originated from Nord-Pas-de-Calais, a Northern French region where the chief town is Lille. 5 unrelated mutation carriers were genotyped for a set of 12 microsatellites from chromosome 11, around the beta-globin gene. Among the 5 mutation carriers, 4 were genotyped for 97 European Ancestry Informative SNPs (EAIMs).

  Dataset EGAD00010000626

• InterPregGen-GWAS-UZB-3

  GWAS genotype data for maternal and fetal (baby) cases of preeclampsia and controls from Uzbekistan. This dataset is a component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium members in Tashkent, Uzbekistan at the Institute of Immunology, Uzbek Academy of Sciences and at the Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology

  Dataset EGAD00010001919

• InterPregGen-GWAS-UZB-2

  GWAS genotype data for maternal and fetal (baby) cases of preeclampsia and controls from Uzbekistan. This dataset is a component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium members in Tashkent, Uzbekistan at the Institute of Immunology, Uzbek Academy of Sciences and at the Republic Specialized Scientific Practical Medical Centre of Obstetrics and Gynecology

  Dataset EGAD00010001917

• A Collaborative Search for New Genes for Non-Syndromic Deafness

  We seek to identify the DNA variants underlying sensorineural hearing loss. We obtain the DNA sequence of affected individuals and their family members. DNA variants are filtered to determine their segregation in affected families.

  Study phs003701

• Single-sell RNA sequencing counts from 7 acute myeloid leukemia patients and 3 healthy donors

  This dataset contains count data (from 10X CellRanger) and metadata from 7 acute myeloid leukemia subjects treated with chemotherapy, as well as data from 3 healthy donors. Additionally, the dataset includes VDJ data for all subjects except for 31_base and the 3 healthy donors (TCRseq not performed).

  Dataset EGAD50000000525

• Whole Exome Sequences from Eivissan and Menorcan Individuals

  30 and 21 whole exome sequences for Eivissan and Menorcan healthy unrelated volunteers, respectively. The exomes were captured with the Agilent SureSelect Human All Exon V6 capture kit and pair-end sequenced in Illumina platforms. For each individual, a pair of ".fastq" files can be found containing the raw reads.

  Dataset EGAD50000000620

• Sensitive urothelial cancer detection via high volume urine DNA analysis

  We developed a high volume (100 mL) urine DNA collection kit and laboratory platform (UroScout) analyzing 25 commonly mutated UC genes and 8 copy number-altered loci for urine tumor DNA (utDNA) alterations. To assess accuracy of UC detection, we analyzed 498 urine samples from diagnostic and surveillance timepoints from 193 UC patients and 88 cancer-negative patients evaluated for UC.

  Dataset EGAD50000000891

• EDi019-A / SAMEA4774918 WGS data

  The iPSC line EDi019-A / SAMEA4774918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi019-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001029

• RCi009-A / SAMEA4339688 WGS data

  The iPSC line RCi009-A / SAMEA4339688 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi009-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001030

• EDi018-A / SAMEA4771918 WGS data

  The iPSC line EDi018-A / SAMEA4771918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi018-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001027

• EDi016-A / SAMEA4562366 WGS data

  The iPSC line EDi016-A / SAMEA4562366 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi016-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001032

• EDi017-A / SAMEA4768918 WGS data

  The iPSC line EDi017-A / SAMEA4768918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi017-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001033

• EDi019-C / SAMEA4777168 WGS data

  The iPSC line EDi019-C / SAMEA4777168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi019-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001036

• EDi010-A / SAMEA4459354 WGS data

  The iPSC line EDi010-A / SAMEA4459354 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi010-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001038

• BIONi010-C / SAMEA3158050 WGS data

  The iPSC line BIONi010-C / SAMEA3158050 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001039

• UKKi019-C / SAMEA17626918 WGS data

  The iPSC line UKKi019-C / SAMEA17626918 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi019-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001042

• EDi011-B / SAMEA4459359 WGS data

  The iPSC line EDi011-B / SAMEA4459359 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi011-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001044

• EDi011-C / SAMEA4459360 WGS data

  The iPSC line EDi011-C / SAMEA4459360 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi011-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001045

• EDi012-A / SAMEA4459361 WGS data

  The iPSC line EDi012-A / SAMEA4459361 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi012-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001046

• EDi015-A / SAMEA4459373 WGS data

  The iPSC line EDi015-A / SAMEA4459373 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi015-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001047

• EDi015-C / SAMEA4459376 WGS data

  The iPSC line EDi015-C / SAMEA4459376 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi015-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001048

• EDi017-B / SAMEA4770418 WGS data

  The iPSC line EDi017-B / SAMEA4770418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi017-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001049

• EDi011-A / SAMEA4459357 WGS data

  The iPSC line EDi011-A / SAMEA4459357 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi011-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001050

• EDi013-B / SAMEA4459367 WGS data

  The iPSC line EDi013-B / SAMEA4459367 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi013-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001052

• BIONi010-A / SAMEA3105765 WGS data

  The iPSC line BIONi010-A / SAMEA3105765 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001054

• BIONi010-B / SAMEA3158000 WGS data

  The iPSC line BIONi010-B / SAMEA3158000 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line BIONi010-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001055

• EDi010-B / SAMEA4459356 WGS data

  The iPSC line EDi010-B / SAMEA4459356 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi010-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001056

• EDi013-A / SAMEA4459365 WGS data

  The iPSC line EDi013-A / SAMEA4459365 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi013-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001057

• RBi001-A / SAMEA3368212 WGS data

  The iPSC line RBi001-A / SAMEA3368212 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RBi001-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001058

• RCi006-A / SAMEA3962402 WGS data

  The iPSC line RCi006-A / SAMEA3962402 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi006-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001059

• UKKi017-C / SAMEA17621668 WGS data

  The iPSC line UKKi017-C / SAMEA17621668 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi017-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001061

• EDi012-B / SAMEA4459363 WGS data

  The iPSC line EDi012-B / SAMEA4459363 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi012-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001063

• EDi012-C / SAMEA4459364 WGS data

  The iPSC line EDi012-C / SAMEA4459364 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi012-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001064

• EDi013-C / SAMEA4459368 WGS data

  The iPSC line EDi013-C / SAMEA4459368 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi013-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001065

• EDi015-B / SAMEA4459375 WGS data

  The iPSC line EDi015-B / SAMEA4459375 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi015-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001066

• UKKi018-C / SAMEA103988380 WGS data

  The iPSC line UKKi018-C / SAMEA103988380 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi018-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001067

• RCi004-A / SAMEA3106011 WGS data

  The iPSC line RCi004-A / SAMEA3106011 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi004-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001069

• RCi004-B / SAMEA3106205 WGS data

  The iPSC line RCi004-B / SAMEA3106205 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi004-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001070

• RCi005-A / SAMEA3961534 WGS data

  The iPSC line RCi005-A / SAMEA3961534 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi005-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001071

• RCi007-C / SAMEA4084916 WGS data

  The iPSC line RCi007-C / SAMEA4084916 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line RCi007-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001072

• UOXFi007-A / SAMEA103988274 WGS data

  The iPSC line UOXFi007-A / SAMEA103988274 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UOXFi007-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001079

• EDi018-B / SAMEA4773418 WGS data

  The iPSC line EDi018-B / SAMEA4773418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi018-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001080

• EDi018-C / SAMEA4774168 WGS data

  The iPSC line EDi018-C / SAMEA4774168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi018-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001081

• UKKi020-C / SAMEA103988344 WGS data

  The iPSC line UKKi020-C / SAMEA103988344 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi020-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001082

• UKKi021-B / SAMEA103988346 WGS data

  The iPSC line UKKi021-B / SAMEA103988346 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi021-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001083

• UKKi022-C / SAMEA103988349 WGS data

  The iPSC line UKKi022-C / SAMEA103988349 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi022-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001084

• WTSIi009-A / SAMEA2593858 WGS data

  The iPSC line WTSIi009-A / SAMEA2593858 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line WTSIi009-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001085

• EDi016-B / SAMEA4767418 WGS data

  The iPSC line EDi016-B / SAMEA4767418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi016-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001086

• EDi017-C / SAMEA4771168 WGS data

  The iPSC line EDi017-C / SAMEA4771168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi017-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001087

• UOXFi008-B / SAMEA103887561 WGS data

  The iPSC line UOXFi008-B / SAMEA103887561 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UOXFi008-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001089

• EDi016-C / SAMEA4768168 WGS data

  The iPSC line EDi016-C / SAMEA4768168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi016-C is available for research use at www.EBiSC.org.

  Dataset EGAD50000001090

• EDi019-B / SAMEA4776418 WGS data

  The iPSC line EDi019-B / SAMEA4776418 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line EDi019-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001091

• UKKi019-A / SAMEA17624668 WGS data

  The iPSC line UKKi019-A / SAMEA17624668 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi019-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001094

• UKKi019-B / SAMEA17626168 WGS data

  The iPSC line UKKi019-B / SAMEA17626168 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line UKKi019-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001096

• Single-cell RNA sequencing of 16 NPM1 mutated AML patients

  This dataset contains paired-end fastq files for single-cell RNA-seq of 16 NPM1 mutated AML patients. 19 fastq pairs are included, with two samples split into two lanes and a second (technical) replicate for one patient. Libraries were constructed using 10x Genomics Chromium Next GEM Single cell 3’ v3.1 and sequencing was performed using Illumina NovaSeq6000.

  Dataset EGAD50000000478

• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  Single cell mRNAseq of T-ALL samples including presentation samples for 13 with refractory disease and 8 with responsive disease, and Day 28 samples for 8 patients with refractory disease. Also includes bulk mRNA sequencing on 6 of the patients with responsive disease.

  Dataset EGAD50000001128

• M116 CHIP Amplicon Sequencing

  Genomic DNA was obtained from M116 peripheral blood sample and was used for targeted deep sequencing (TDS) studies. Barcoded libraries were prepared according to the manufacturer’s instructions, using a probe-based panel (KAPA HyperCap, Roche®) targeting frequently mutated regions of 50 myeloid-related genes. Samples were run on a MiSeq (Illumina®) sequencer for paired-end 2x75 bp reads with a mean coverage of 1000X.

  Dataset EGAD50000001287

• WES dataset for SJMPAL011911 with different treatments

  This dataset includes whole exome sequencing (WES) data from PDX samples post-drug treatment (N=1 vehicle, N=1 gilteritinib, N=1 venetoclax and N=1 VenGilt). Bam files are provided for each sample. The VenGilt-treated sample underwent whole genome amplification using the Qiagen REPLI-g kit (#150345) prior to sequencing due to the very low cell number.

  Dataset EGAD50000001425

• RNA-sequencing of platelets and immortalized megakaryocyte cell lines for inherited thrombocytopenia

  Total RNA-sequencing of platelets and immortalized megakaryocyte cell lines for inherited thrombocytopenia. Libraries were prepared using Illumina TruSeq Stranded Total RNA with Ribo-Zero Gold kits. The samples were paired-end sequenced using the NovaSeq 6000 platform at a depth of 60 million reads per sample with a read length of 100 base pairs.

  Dataset EGAD50000001818

• MB_COMICS_Methylome

  Medulloblastoma (MB) is the most common malignant brain tumor in children. It is a neuroectodermal tumor located in the cerebellum. International consensus recognizes four distinct subgroups of MBs including Wingless (WNT), Sonic Hedgehog (SHH), Group 3 (G3), and Group 4 (G4) with distinct molecular characteristics, prognoses, and mortality rates in patients. Here, we have compiled and evaluated a large international MB cohort for which we generated methylome data for 369 primary MB patient samples.

  Dataset EGAD00010002669

• Genetic analysis in lifestyle-related disease, arteriosclerotic disease, and aging-related diseases.

  Exome sequence analysis was conducted in patients with hypertension (Stage 3). Rare variants in candidate susceptible genes discovered by previous GWASs were prioritised and further analysed for an additional case-control association study.

  Study JGAS000016

• Mitochondrial DNA Mutations at Low-level Heteroplasmy in Chronic Kidney Disease

  To explore for relationship between somatic or heteroplasmic mutations of mtDNA and kidney function, we here performed ultra-deep sequencing based on long-range PCR from DNAs of 78 CKD kidney tissues.

  Study JGAS000611

• CIAO Clinical Trial

  Whole exome and RNA sequencing for samples from CIAO (Checkpoint Inhibitors Assessment in Oropharynx cancer) (NCT03144778) clinical trial testing neoadjuvant anti-PD-L1 durvalumab with or without the anti-CTLA-4 tremelimumab in oropharynx cancer

  Study EGAS50000001174

• Diverse Cellular Composition in Alveolar Rhabdomyosarcoma Revealed by Single Cell RNA Sequencing

  Using single cell RNAseq data to characterize tumor composition and cellular states for Alveolar Rhabdomyosarcoma. We find that that ARMS tumors harbor phenotypically and transcriptionally distinct malignant subpopulations within an immune-influenced microenvironment.

  Study EGAS50000001564