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• FACS sorting of ploidy populations in an undifferentiated soft tissue sarcoma for RRBS

  Groups of cells belonging to different ploidy populations (based on PI staining) were collected from an undifferentiated soft tissue sarcoma. The different ploidy populations underwent RRBS, after which copy number signatures for the ploidy-sorted populations and the bulk population were extracted.

  Study EGAS00001006143

• IBD Whole Genome Sequencing (2019-04-01)

  We will sequence at 15X coverage the genomes of 1536 IBD patients. These samples are currently onsite at Sanger and made available for sequencing via our collaboration with the UK IBD Genetics consortium. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004880

• Colon Cancer Organoid Cultures and Tumors Whole Exome Sequencing Data

  Five hundred fifty nanograms of genomic DNA were input for library preparation after fragmentation by Covaris S2, following the KAPA Hyper Prep Kit (KR0961-V1.14) protocols, with selection for a library size range of 250-450 bp. Three hundred nanograms per library DNA each from 12 samples were normalized and combined into a single pool for exome capture using the xGen Lockdown Probes and Reagents based on their standard protocols

  Dataset EGAD00001005754

• BLUEPRINT September 2016, ChIPmentation for naive B cell from tonsil, on Genome GRCh38

  ChIPmentation data for 1 sample(s) for naive B cell from tonsil, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002937

• BLUEPRINT September 2016, ChIPmentation Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38

  ChIPmentation data for 7 sample(s) Acute Lymphocytic Leukemia for precursor B cell from bone marrow, on Genome GRCh38. 13 run(s), 13 experiment(s), 13 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002928

• BLUEPRINT September 2016, ChIPmentation Acute Myeloid Leukemia for blast cell from bone marrow, on Genome GRCh38

  ChIPmentation data for 2 sample(s) Acute Myeloid Leukemia for blast cell from bone marrow, on Genome GRCh38. 12 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002935

• Sequencing data for oesophageal and related samples - ICGC DCC release 28 earmarked (WGS)

  WGS sequencing for 43 cases (86 samples) from the ICGC ESAD-UK project Tumours 50x Normals 30x HiSeq X BAM files These samples are earmarked for inclusion in ICGC release 28

  Dataset EGAD00001004029

• BLUEPRINT September 2016, ChIPmentation for memory B cell from venous blood, on Genome GRCh38

  ChIPmentation data for 2 sample(s) for memory B cell from venous blood, on Genome GRCh38. 6 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002923

• miRNA data for aSAH patients with or without subsequent vasospasm

  These files contain the normalized and raw count abundances miRNA for aSAH patients. These abundances were obtained using Next-Generation Sequencing after selection of the miRNA in the RNA biobank. In total, there are 28 VSP- and 28 VSP+ patients for two-time points. Normalized data were obtained by applying size factor and VSN normalizations as described in Pulcrano-Nicolas et al. Stroke 2018. Raw count data corresponding to the raw abundances of miRNA in aSAH patients.

  Dataset EGAD00001004185

• Genome Diversity in Africa Project - GemCode libraries (2017-07-05)

  High depth whole genome sequencing from GemCode (10x Genomics) DNA libraries containing long range linkage information for one Baganda trio and one Baganda child (parent already sequenced at high depth). This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-07-05.

  Dataset EGAD00001003426

• BLUEPRINT September 2016, ChIPmentation Acute Lymphocytic Leukemia for precursor B cell from venous blood, on Genome GRCh38

  ChIPmentation data for 5 sample(s) Acute Lymphocytic Leukemia for precursor B cell from venous blood, on Genome GRCh38. 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002936

• BLUEPRINT September 2016, ChIPmentation for germinal center B cell from tonsil, on Genome GRCh38

  ChIPmentation data for 1 sample(s) for germinal center B cell from tonsil, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002932

• BLUEPRINT September 2016, ChIPmentation for regulatory T cell from venous blood, on Genome GRCh38

  ChIPmentation data for 2 sample(s) for regulatory T cell from venous blood, on Genome GRCh38. 14 run(s), 9 experiment(s), 9 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002942

• BLUEPRINT September 2016, ChIPmentation for conventional dendritic cell from cord blood, on Genome GRCh38

  ChIPmentation data for 1 sample(s) for conventional dendritic cell from cord blood, on Genome GRCh38. 4 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002940

• BLUEPRINT September 2016, ChIP-Seq for thymocyte from thymus, on Genome GRCh38

  ChIP-Seq data for 5 sample(s) for thymocyte from thymus, on Genome GRCh38. 17 run(s), 17 experiment(s), 17 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002947

• Genetic screening of GPI-anchor protein synthesis (2018-08-13)

  This study will analyse the guide sequence which were used for making mutations in the Cas9-expressing cells. We used GeCKO v2 library which were released by Feng Zhang, 2014. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2018-08-13.

  Dataset EGAD00001004294

• Contribution of Retrotransposition to Developmental Disorders

  Variants and WGS data for Gardner et al. 2018 (biorxiv 471375). One VCF each for Alu, L1, and SVA. Flat text file and WGS for processed pseudogenes.

  Dataset EGAD00001004586

• Oceanian and American population sequencing and phasing (2019-04-11)

  Whole-genome sequencing of human individuals from Polynesian and Native American populations, as well as 10x Genomics Chromium data from Polynesian, Native American and Aboriginal Australian populations, allowing for experimental phasing of haplotypes. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004951

• RNA Sequencing datasets - Project "Multi-omics analysis of Parkinson’s disease midbrains"

  The dataset contains FASTQ files referring to the study "Multi-omics analysis of Parkinson’s disease midbrains". For this project, RNA was isolated from human postmortem midbrain tissue (PD and Control samples). Libraries were prepared with the TruSeq Small RNA library prep (Small RNA Seq) and the TruSeq Stranded Total RNA Kit (for transcriptomics), both from Illumina. Sequencing for both experimental setups was conducted in the Illumina HiSeq4000.

  Dataset EGAD00001006883

• NHLBI TOPMed: Pediatric Cardiac Genomics Consortium (PCGC)'s Congenital Heart Disease Biobank

  The Pediatric Cardiovascular Genetics Consortium (PCGC) proposes to define genetic causes for congenital heart defects (CHD) as part of the TOPMed Program are the most common form of heart disease in childhood and are also the most prevalent form of birth defects, occurring in 2-3% of live births. The PCGC has recruited and clinically characterized = 10,000 CHD probands and parents (CHD trios). From whole exome sequencing (WES) of >2800 CHD trios, we identified a substantial enrichment of damaging de novo mutations in genes important for cardiogenesis, particularly implicating histone modifier enzyme gene defects. Analysis of whole genome sequencing (WGS) of 350 probands with CHD unexplained by WES and their parents preliminarily implicated de novo mutations in enhancers of genes previously shown to cause CHD in mouse knock out models. Sequencing of RNA (RNAseq) from discarded cardiac tissues from CHD probands has revealed likely causal allele-specific expression (ASE) as well as biallelic loss of expression (LOE). We have also discovered de novo epimutations, differentially methylated regions (DMRs), some with underlying de novo DNA variation, that are detectable in peripheral blood leukocytes and appear to underlie 10% of CHD. Of note, these assorted 'omic' approaches have enabled one another, both for attributing causality and assessing functional impact. Based on these extensive preliminary data, we hypothesize that PCGC probands with uninformative exomic analyses (WES-negative) harbor de novo genetic and/or epigenetic mutations in critical regulatory elements that participate in developmental expression of cardiac genes. To identify these etiologies, we propose analyses of WGS in 1000 WES-negative CHD trios, prioritizing those with probands with banked CHD tissues (n=78), one damaging variant in a recessive CHD gene, and older fathers (age>45). We also request WGS for 230 probands, for whom we have cardiac tissues but not parental DNAs. We request RNAseq for 308 cardiac tissues. For DNA methylation, which TOPMed will offer through the Illumina 850k array platform, we are requesting analysis of DNAs from peripheral blood leukocytes for all probands for whom WGS will be performed (1000 from trios, 230 singletons) as well as DNAs from cardiac tissues (n=308) to pair with the WGS, RNAseq and blood DNA methylation data. We will use existing resources and capabilities of the PCGC to confirm relevant mutations and those of its companion consortium in the Bench to Bassinet Program, the Cardiovascular Development Consortium, to inform analyses of non-coding mutations as well as to perform confirmatory functional genomics studies using cell and animal models. We expect that the studies resulting from data generated through TOPMed will provide novel insights into the molecular basis for CHD and fundamental knowledge about genes and pathways involved in cardiac development. Aside from being relevant to CHD, we anticipate that our findings will inform the understanding of later-onset cardiovascular diseases, including some arising in adulthood.

  Study phs001735

• Understanding the genetic risk underlying racial disparities in uterine fibroids

  Fibroids affect 77% of women by onset of menopause in the U.S. and account for $2.1 billion in healthcare costs each year. Fibroids negatively impact reproductive health causing heavy and painful menses, pelvic pain and pressure, pregnancy complications, and interventions including myomectomy and hysterectomy. Until recently, tumor tissue and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic analysis can provide a powerful and cost effective tool to identify etiological and causal factors, especially since a genetic predisposition to fibroids has already been documented from twin studies. As much as 69% of risk is explained by genetic factors. Racial disparities also support a role for genetics with fibroid risk. African American women have earlier age of onset, more numerous and larger fibroids with a greater lifetime incidence compared to Caucasians. We propose to identify genetic markers for risk of fibroids through a genome-wide association study (GWAS) of African American and Caucasian participants, leveraging ancestral differences to narrow down genomic regions for targeted follow- up analyses. To accomplish this we will take advantage of a unique Vanderbilt resource, the BioVU DNA databank. BioVU currently has over 122,470 adults linked to electronic medical records. From BioVU we have already identified 2,902 African American and Caucasian subjects who meet our stringent inclusion criteria to conduct a GWAS of fibroids, including pelvic imaging. Available imaging is critical, because many women with fibroids are asymptomatic and without imaging, studies may misclassify as many as 51% of women. We have also defined definitive controls who reached menopause without fibroids. We have a strong group of nationally known fibroid researchers who will provide over 10,000 samples for replication. Our first Specific Aim is to conduct a GWAS for association between common single nucleotide polymorphisms (SNPs) and fibroid risk. Using a case-control design we will perform a GWAS in 2,902 (1,451 fibroid cases and 1,451 controls) women from BioVU stratified by African American and Caucasian race. Secondary admixture mapping (AM) analyses will also be performed to identify chromosomal regions of interest to prioritize for replication. Our second Aim is to resequence chromosome regions identified from GWAS and AM to discover rare variants. Finally, in Aim 3 we will replicate SNPs selected from Aim 1 and 2 in independent samples of at least 3,230 fibroid cases and 7,097 controls. We propose an efficient and cost-effective approach to identify genetic risk factors for fibroids, by taking advantage of imaging information and DNA available through BioVU. This study represents the largest GWAS of uterine fibroids and the first among African Americans leveraging emerging technologies and new statistical approaches to conduct this study. Our proposed study will fundamentally change knowledge about fibroids and lay the ground work for breakthroughs in understanding mechanisms of fibroid formation and in identifying novel therapeutic approaches.

  Study phs001409

• FHS-Net Social Networks

  Note: Data Use Restrictions: The data must only be used for genetic research projects solely in the area of health-related social-network research. This study compiles detailed information on family and social ties linked to participants in the offspring cohort of the Framingham Heart Study. The investigators took computerized information from self-report data used by the FHS for over 30 years to facilitate health exam scheduling. These tracking sheets asked participants to identify people close to them, which were assumed by the investigators to indicate a social tie. Individuals are listed as "egos" (if the ties are from their perspective) and/or "alters" (if they are linked as a friend or family members to one of the other participants). All egos were FHS offspring participants while alters could be FHS participants in any cohort or non-participants. This information was combined with internal FHS pedigree data on family ties to list family member alters (as well as the nature of their relationship). The resultant dataset therefore includes each ego-alter tie, the nature of their relationship and the start and end dates for their ties for each of seven waves for the offspring study. Due to privacy concerns, all exam dates for individuals are listed relative to the initial FHS exam date. The study investigators chose a random, confidential date, to which all initial exam dates are linked (by number of months from random date to initial exam date). With this information, researchers can have access to the temporal relationships among participants' exam dates while remaining unaware of the actual exam dates for individual participants. During each clinic exam cycle, the participants undergo a detailed examination including physical examination, medical history, laboratory testing, and electrocardiogram. Over the years, other tests (that may not be performed at every exam cycle) have included pulmonary function, lifestyle, physical function, cognitive function questionnaires, and various noninvasive cardiovascular tests including echocardiograms. This study involves phenotypic data from these exams including (when available) basic body measurements (height, weight, blood pressure), laboratory values (blood sugar levels, LDL levels), information on smoking and alcohol use, and tests of depression and cognitive functioning. Important links to apply for individual-level data Data Use Certification Requirements (DUC) Instructions to Request Authorized Access Apply here for controlled access to individual level data Participant Protection Policy FAQ In 1948, researchers recruited men and women from the town of Framingham, Massachusetts, beginning the first round of extensive physical examinations and lifestyle interviews that would later be analyzed for common patterns related to CVD development. Initially, the Framingham Heart Study enrolled 5,209 men and women from the Framingham area who were between the ages of 28 and 62 years. Beginning in 1971, the Framingham Heart Study enrolled 5,124 men and women, who were either offspring of the original cohort or spouses of those offspring. In 2002, 4,095 third generation participants (men and women) were enrolled.

  Study phs000153

• WTCCC2 Pharmacogenomic Response to Statins study

  A WTCCC2 project genome-wide study for pharmacogenomic response to statins (PR) in individuals from Scotland.

  Study EGAS00000000121

• vaccgene_1000G_MKK_hla

  VaccGene HLA imputation panel variants and HLA allele calls for all individuals across all tested sequence platforms and countries, and the high quality direct genotypes for these individuals (with genotype data available) across the MHC in PLINK format

  Dataset EGAD00010002577

• Peru.mg.maf.subsetEGA

  Genotypes generated for study investigating signals of selection in Peruvians from three ecological regions. 96 genotypes in plink format after QC filtering (missingness per individual, per variant and minor allele freq). See publication for more details on QC filtering.

  Dataset EGAD00010002261

• small RNA sequencing for 6 patients

  small RNA sequencing data for 3 patients with type 2D diabetes, and 3 patients with non diabetes. Single end sequencing was performed with read length of 100.

  Dataset EGAD50000001259

• Metagenome shotgun sequencing of the Inflammatory Bowel Disease

  We performed a comprehensive metagenome-wide association study for inflammatory bowel disease by utilizing whole-genome shotgun sequencing.

  Study JGAS000530

• PCA Atlas donor genotyping arrays (Axiom UK Biobank v2.0)

  Raw Axiom v2.0 UK Biobank Array CEL files for PCA Atlas donor buffy-coat DNA, used to generate SNP genotypes for demultiplexing single-cell and spatial transcriptomics libraries.

  Dataset EGAD00010002818

• WTCCC2 BO (Barretts oesophagus) samples

  A WTCCC2 project genome-wide case-control association study for Barretts oesophagus in individuals from the UK.

  Study EGAS00001000628

• Multi-regional tumour biopsies of a RET fusion patient

  Identifying modes of resistance to a new experimental drug for a patient resistant to other therapies

  Study EGAS00001004023

• Melanoma Germlines subset for BAP1

  These samples comprise both melanoma cases and controls sequenced for a selection of loci linked to disease susceptibility. These bams are a subset of the sequencing restricted specifically to the GRCh37 coding areas of the BAP1 gene.

  Dataset EGAD00001002743

• ChIP-seq in colorectal cancer and paired adjacent normal mucosa

  Chip-seq samples for 20 colorectal patients with paired adjacent normal mucosa to characterize H3K27ac and H3K4me1.

  Study EGAS00001005303

• A98257B

  Whole genome sequencing for single cells for library A98257B 1286 cells; filetype=bam

  Dataset EGAD00001007126

• Phenotype data for samples with serum metabolomics

  Phenotypic data for 475 human samples, including: Demographics Anthropometrics Diet data Clinical data Time of day Season in which serum sample was taken

  Dataset EGAD00001006354

• A96178B

  Whole genome sequencing for single cells for library A96178B 846 cells; filetype=bam

  Dataset EGAD00001007116

• WXS files for AML data

  WXS files for paper titled "Integrative Analysis of Pediatric Acute Leukemia Identifies Acute Myeloid/T-Lymphoblastic Leukemia Subtype that Spans a T Lineage and Myeloid Continuum with Distinct Prognoses"

  Dataset EGAD00001006443

• RNASeq files for AML data

  RNASeq files for paper titled "Integrative Analysis of Pediatric Acute Leukemia Identifies Acute Myeloid/T-Lymphoblastic Leukemia Subtype that Spans a T Lineage and Myeloid Continuum with Distinct Prognoses"

  Dataset EGAD00001006444

• WGS files for AML data

  WGS files for paper titled "Integrative Analysis of Pediatric Acute Leukemia Identifies Acute Myeloid/T-Lymphoblastic Leukemia Subtype that Spans a T Lineage and Myeloid Continuum with Distinct Prognoses"

  Dataset EGAD00001006442

• ChIPseq data for Glioblastoma (EGAS00001003953, H016)

  ChIPseq data for human glioblastoma patients, EGAS00001003953. Mix of input, H3K27ac, H3K27me1, H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9me3 and BRD, 20 human samples, 2 cell lines (LN229, ZH487).

  Dataset EGAD00001006548

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome - cell line data

  Dataset EGAD00001008963

• A98284B

  Whole genome sequencing for single cells for library A98284B 1582 samples; filetype=bam

  Dataset EGAD00001008263

• A95675A

  Whole genome sequencing for single cells for library A95675A 668 cells; filetype=bam

  Dataset EGAD00001007109

• single cell RNASEQ files for Mullighan BiTE RNASEQ3

  single cell RNASEQ files for Mullighan BiTE RNASEQ3 paper titled "Tumor intrinsic and extrinsic mechanisms of response and resistance to blinatumomab in relapsed/refractory acute lymphoblastic leukemia"

  Dataset EGAD00001005733

• WGS files for paper titled "Genomic Landscape and Clonal Architecture in Pediatric Myeloid Neoplasms with Chromosome 7 Deletions"

  WGS files for Klco paper titled "Genomic Landscape and Clonal Architecture in Pediatric Myeloid Neoplasms with Chromosome 7 Deletions"

  Dataset EGAD00001015490

• RNASeq files for paper titled "Genomic Landscape and Clonal Architecture in Pediatric Myeloid Neoplasms with Chromosome 7 Deletions"

  RNASeq files for Klco paper titled "Genomic Landscape and Clonal Architecture in Pediatric Myeloid Neoplasms with Chromosome 7 Deletions"

  Dataset EGAD00001015492

• DATA FILES FOR PCGP SJCBF RNASEQ

  Additional files for "The Genomic Landscape of Core-Binding Factor Acute Myeloid Leukemias" (EGAS00001000349). This dataset includes the processed RNASeq data referenced in this paper.

  Dataset EGAD00001002530

• Exome and RNA seq data for female patient

  Exome and RNA sequencing data for EGAS00001003776 - one female patient with neurofibroma/schwannoma hybrid nerve sheath tumor (N/S HNST)

  Dataset EGAD00001005249

• 10x Genomics Single Cell Gene Expression for SA1096A

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1096

  Dataset EGAD00001009139

• MissionBio files for paper titled "Genomic Landscape and Clonal Architecture in Pediatric Myeloid Neoplasms with Chromosome 7 Deletions"

  MissionBio files for Klco paper titled "Genomic Landscape and Clonal Architecture in Pediatric Myeloid Neoplasms with Chromosome 7 Deletions"

  Dataset EGAD00001015493

• Single cell sequencing data for chronic myeloid leukemia cell lines

  This dataset contains RNA sequencing information for Chronic Myeloid Leukemia. In total 2 single-end RNA-seq tumor cell line samples are present.

  Dataset EGAD00001009736