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• Understanding Rare Variant Contributions to autism: Lessons from Dystrofin-Deficient Model

  This project generated a whole-exome sequencing (WES) dataset of 83 boys with a pathogenic variant in the DMD gene, along with WES data from the parents of 38 of them, totaling 159 samples. Pathogenic DMD variants, besides causing dystrophinopathies, are also known risk factors for neurodevelopmental disorders (NDDs). However, the low penetrance of NDDs in individuals with dystrophinopathies suggests that additional factors contribute to their manifestation. This study aimed to: (1) characterize ASD in males with dystrophinopathies; (2) determine the relevance of additional de novo and inherited rare risk variants for their neurodevelopmental phenotype; and (3) clinically and functionally characterize the identified risk variants.

  Study EGAS50000000754

• Genomic analysis of head and neck squamous cell carcinoma upon acquired resistance to cetuximab

  Anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (cetuximab) remain the only FDA-approved targeted therapy for patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). Unfortunately, most patients develop acquired resistance to cetuximab and there is currently no further therapeutic option for these patients (unmet clinical need), thereby highlighting the need to better understand resistance-causing mechanisms. Here, we carried out whole-exome sequencing on 2 cetuximab-sensitive and -resistant patient-derived xenograft models of HNSCC. Our data reveal several genetic alterations (mutations and copy number variations) associated with acquired resistance to anti-EGFR therapy.

  Study EGAS50000000800

• Targeted DNA sequencing and mRNA sequencing data from patients with peritoneal metastasis from colorectal cancer

  Patients with peritoneal metastasis from colorectal cancer (PM-CRC) have inferior prognosis and respond particularly poorly to chemotherapy. This study aims to identify the molecular explanation for the observed clinical behavior and suggest novel treatment strategies in PM-CRC. Tumor samples (230) from a Norwegian national cohort undergoing surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) with mitomycin C (MMC) for PM-CRC were subjected to targeted DNA sequencing, and associations with clinical data were analyzed. mRNA sequencing was conducted on a subset of 30 samples to compare gene expression in tumors harboring BRAF or KRAS mutations and wild-type tumors.

  Study EGAS50000000404

• Circulating cell-free DNA methylation profiles as noninvasive multiple sclerosis biomarkers: A proof-of-concept study

  In chronic neurodegenerative diseases such as multiple sclerosis (MS), there is a critical unmet need for cost-effective non-invasive blood biomarkers to concurrently classify disease subtypes, monitor disability severity, and predict long-term progression. In this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples collected from a well-characterized real-world prospective clinic cohort with longitudinal disability outcome measurements. We assessed the clinical utility of cfDNA methylation profile for differentiating MS patients from non-MS controls, classifying MS subtypes, estimating disability severity, and predicting disease trajectories.

  Study EGAS50000001277

• CLUSTER RNAseq Study of Juvenile Idiopathic Arthritis patients in methotrexate cohort

  CLUSTER aims to discover novel disease mechanisms and biomarkers in children suffering from Juvenile idiopathic arthritis (JIA). Currently, little is known about the pathological disease mechanisms of JIA and there are no validated tools capable of predicting response or non-response to treatment. The study aims to identify biomarkers for treatment response using RNAseq technology. The samples used in this study comes from blood samples that were collected prior to patients receiving methotrexate treatment (naive). These samples were then processed for PBMC isolation followed by sorting cells into 4 different immune cell types. All of these samples were then sequenced by NovaSeq6000.

  Study EGAS50000000995

• CLUSTER Read-counts matrix of RNAseq Datasets of JIA in methotrexate cohort

  CLUSTER aims to discover novel disease mechanisms and biomarkers in children suffering from Juvenile idiopathic arthritis (JIA). Currently, little is known about the pathological disease mechanisms of JIA and there are no validated tools capable of predicting response or non-response to treatment. The study aims to identify biomarkers for treatment response using RNAseq technology. The samples used in this study comes from blood samples that were collected prior to patients receiving methotrexate treatment (naive). These samples were then processed for PBMC isolation followed by sorting cells into 4 different immune cell types. All of these samples were then sequenced by NovaSeq6000.

  Study EGAS50000001501

• RNA seq of intestinal biopsies and blood cells from patients with celiac disease and controls

  This study presents RNA sequenced from several intestinal phenotypes such as from patients with damaged intestinal mucosa as well as from those without a damaged intestine but still an autoantibody response to tissue transglutaminase (“potential celiac disease”) or those with treated celiac disease. By combining phenotypes in a much larger number of tissue samples from both peripheral blood and mucosal biopsies, we found highly differentially expressed genes, but also identify genes involved in controlling and triggering celiac disease associated changes. This study identifies molecular mechanisms involved in celiac disease and new possible targets for treatment and for identification of individuals at risk.

  Study EGAS50000001104

• The EGA joins EUCAIM, the European project powering AI and imaging in cancer research

  EUCAIM (EUropean Federation for CAncer IMages) is the cornerstone of the European Cancer Imaging Initiative, one of the flagships of the Europe’s Beating Cancer Plan (EBCP). The European Genome-Phenome Archive (EGA), as member of the EUCAIM consortium, attended the kick-off meeting organized this week in Brussels for the launch of the Initiative. The EGA will contribute to the project with the experience gained in the deployment of our federated network, the Federated EGA (FEGA), and the experience gained as partners of EuCanImage (AI4HI initiative) contributing to the definition of common data models, ontologies and data standards. The ultimate goal of the project is to unlock the power of imaging and Artificial Intelligence for the benefit of cancer patients, clinicians and researchers. The consortium will deploy a pan-European digital federated infrastructure hosting cancer-related images, and associated pathology, molecular and laboratory data from Real-World. This endeavour will provide a comprehensive dashboard for imaging data discovery, federated search, metadata harvesting, annotation and distributed data processing. EUCAIM joins 76 partners, gathering the experience and previous effort made by the different repositories of the AI4HI initiative, European Research Infrastructures and national/regional repositories. Find out more information about the EUCAIM launch here

  Blog the-ega-joins-eucaim

• GCAT | Genomes for life

  The GCAT project is a prospective cohort study that was designed to recruit the general population of Catalonia, the north-east region of Spain. This study is a long-term genomic, environmental and lifestyle cohort project that aims to evaluate and track multiple pathologies as well as biologically related traits. By the end of 2017, the GCAT Study had recruited a total of 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants are followed biannually for/during, at least, 20 years after recruitment. Therefore, the GCAT Study offers a unique opportunity to integrate diverse data to allow the identification of novel relations among different biomarkers and conditions. Results may lead to the development of new genetic, genomic, epigenomic and proteomic diagnoses and screening tests, as well as new public health recommendations. The only currently available population-based prospective cohort study in Spain to the date, including more than 4899 genotyped participants and 800 whole genome sequences is now stored in the EGA archive under accession id EGAS00001003018.

  Blog gcat-genomes-for-life

• Identification of point mutations, expression perturbations, and gene fusions in T-cell acute lymphoblastic leukemia by RNA-seq

  RNA-seq is a promising technology to re-sequence protein-coding genes for the identification of single nucleotide variants, while simultaneously obtaining information on structural variations and gene expression perturbations. T-cell acute lymphoblastic leukemia (T-ALL) is caused by a combination of gene fusions leading to the over-expression of transcription factors reinforced by point mutations in oncogenes and tumor suppressor genes. We asked whether RNA-seq is suitable for the detection of driver mutations in these leukemias. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq.

  Study EGAS00001000536

• Population whole-genome bisulfite sequencing across two tissues highlights environment as principal source of human methylome variation

  The study includes whole-genome bisulfite sequencing (WGBS) on 34 adipose(7 MZ pairs, 6 DZ pairs and 8 singletons) and 27 blood (7 MZ pairs, 6 DZpairs and 1 singleton) DNA samples derived from a total of 43 female twinsbelonging to the MuTHER/TwinsUK cohort. We generated 11.5 billion 100bppaired-end reads covering 2.3 Tera-basepairs (Tbp) ofsequence using the Illumina HiSeq2000 or 2500 systems. Applying standardalignment methods and filters we obtain a mean genome coverage of 6.3-fold(range: 1.0- to 12.9-fold) for adipose and 8.7-fold (range: 0.7- to29.0-fold) for blood.

  Study EGAS00001001569

• Integrative genome profiling in AML

  AML emerges as a consequence of accumulating independent genetic aberrations that direct regulation and/or dysfunction of genes resulting in aberrant activation of signalling pathways, resistance to apoptosis and uncontrolled proliferation. Given the significant heterogeneity of AML genomes, AML patients demonstrate a highly variable response rate and poor median survival in response to current chemotherapy regimens. For the past 4 years we have conducted gene expression profiling on purified bone marrow populations equating to normal haematopoietic stem and progenitor cells from healthy subjects and patients with de novo AML in order to identify AML signatures of aberrantly expressed genes in cancer versus normal. We are now applying a series of bioinformatic methodologies combined with clinical and conventional diagnostic data to establish novel genomics strategies for improved prognostication of AML. Additionally, we use our AML signatures to unravel oncogenic signalling pathway activities in AML patients and test inhibitory drugs for these pathways inn preclinical therapeutic programmes. We consider that superimposing GEP and clinical data for our AML patient cohort with additional data on their mutational status will significantly improve the prognostic power of the study as well as unravel yet unknown mutations associated with aberrant signalling activities of oncogenic pathways.

  Dataset EGAD00001001873

• Whole-genome sequencing of rare disease patients in a national healthcare system

  Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and mediating genes for more than half such disorders remain to be discovered. We implemented whole-genome sequencing (WGS) in a national healthcare system to streamline diagnosis and to discover unknown aetiological variants, in the coding and non-coding regions of the genome. In a pilot study for the 100,000 Genomes Project, we generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 patients with detailed phenotypic data. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed aetiological. Using WGS of UK Biobank1, we showed that rare alleles can explain the presence of some individuals in the tails of a quantitative red blood cell (RBC) trait. Finally, we reported 4 novel non-coding variants which cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.

  Dataset EGAD00001006065

• RNA sequencing of a glioblastoma PDX cohort

  Patient-derived xenograft (PDX) models represent an essential tool for the preclinical evaluation of novel targeted therapies and their combinations. In this project, we molecularly characterized a comprehensive panel of glioma PDX models with well conserved molecular and pathological features over multiple passages. PDX samples were subjected to next generation sequencing (NGS). Characterization of cancer-relevant features including driver mutations or cellular processes was performed using mutational and gene expression data in order to identify potential biomarker or treatment targets in glioblastoma. In summary, we reported a newly established and molecularly characterized panel of glioma PDX models with high relevance for translational preclinical research.

  Study EGAS00001007119

• Deciphering Intratumoral Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma with a Radiogenomics Platform

  Intratumoral heterogeneity (ITH) challenges the molecular characterization of clear cell renal cell Carcinoma (ccRCC) with percutaneous biopsies and is a confounding factor in selection of molecular-targeted versus immune-based therapy. Magnetic Resonance (MR) Imaging can noninvasively assess the spatial landscape of the entire tumor. To validate MRI for ITH assessment, we implemented a radiogenomic platform through a systematic imaging based co-localization approach for multi region tissue acquisition from single tumors. We investigated if the spatial changes in imaging can predict the molecular changes using transcriptome and histopathological correlatives. Our study confirmed imaging heterogeneity as a predictor of molecular heterogeneity in ccRCC.

  Study EGAS00001003846

• Chracterising_cellur_pathways_underlying_CD3_CD28_activation_of_human_CD4__cells

  We devised an approach to disentangle the TCR and CD28 pathways upon stimulation in naive and memory primary human CD4+ T cells (Tcons) in response to defined stimulatory signals. Sorted Tcons were activated using a titration of anti-CD3 and anti-CD28 in combination as well as individually. As a control we cultured cells in the same conditions but without the stimuli. In total, we defined seven conditions from four individuals for sequencing. 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/

  Study EGAS00001004147

• Neutrophils infected with Leishmania donovani

  In the context of studying visceral leishmaniasis, neutrophils infected with Leishmania donovani have been compared to uninfected neutrophils. Compared time points are 0, 6 and 24 hours post infection. Neutrophils of three human donors have been used. Overall 6 samples for infected neutrophils at time point 6 hours and 6 samples for infected neutrophils at time point 24 hours exist, including three biological samples and two technical samples. Uninfected neutrophils represent 3 samples at time point 0 hours, 3 samples at time point 6 hours and 3 samples at time point 24 hours. Transcriptome of Leishmania donovani culture has been assessed in two replicates.

  Study EGAS00001004912

• 2014_Lung_sq_WES

  Purpose Lung squamous cell carcinoma (SqCC) is the second most prevalent type of lung cancer. Currently, no targeted-therapeutics are approved for treatment of this cancer, largely due to lack of systematic understanding of the molecular parthenogenesis of the disease. To identify therapeutic targets and comparatively analyze them in lung SqCC, we probed somatic alterations of Korean lung SqCC. Patients and Methods We performed massively parallel whole-exome sequencing of 104 Korean lung SqCC patients’ samples with matched normal samples. In addition, copy number analysis and transcriptome analysis were conducted for a subset of these samples. Clinical association with cancer-specific somatic alterations was investigated.

  Study EGAS00001002844

• Detection of Cancer Mutations by Urine Liquid Biopsy in Bladder Cancer Patients

  The management of bladder cancer faces multiple challenges concerning the diagnostics and fol-low-up approaches. The standard diagnostic test is invasive cystoscopy. Urine cytology and re-cently proposed urine-based biomarkers still could not replace cystoscopy, thus prompting calls for improvements. Here, we explore urine liquid biopsy to detect cancer mutations for therapeutic and prognostic assessment and subsequently evaluate the utility of urine as a suitable specimen in diagnosing bladder cancer. Our findings show that the analysis of pre-and post-operative urine enables the characterization of specific tumor mutations, which can be used to diagnose and evaluate a patient’s prognosis and therapy response.

  Study EGAS00001005758

• Chracterising_cellur_pathways_underlying_CD3_CD28_activation_of_human_CD4__cells

  We devised an approach to disentangle the TCR and CD28 pathways upon stimulation in naive and memory primary human CD4+ T cells (Tcons) in response to defined stimulatory signals. Sorted Tcons were activated using a titration of anti-CD3 and anti-CD28 in combination as well as individually. As a control we cultured cells in the same conditions but without the stimuli. In total, we defined seven conditions from four individuals for sequencing. 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/

  Study EGAS00001002438

• 'KOREAN' never-smoker female adenocarcinoma RNA-seq

  Multi-layered genomic studies such as the TCGA project have greatly advanced our understanding on molecular pathogenesis of lung cancer. For Asian nonsmoker patients, however, majority of studies have been limited to mutational analyses, emphasizing ethnic differences in driver mutations (e.g. more frequent EGFR mutations and ALK fusions). In essence, a comprehensive multi-layered characterization that could lead to molecular etiology and patient stratification scheme to be translated into clinical applications for this patient group is still missing. Here we report molecular profiling of tumor and matched normal tissues from 114 non-small-cell lung adenocarcinoma patients using whole exome sequencing, transcriptome sequencing, and array comparative genomic hybridization (CGH).

  Study EGAS00001003789

• GWAS Results from Danjou et al, Nature Genetics 2015

  We report genome-wide association study results for the levels of A1, A2 and fetal hemoglobins, analyzed for the first time concurrently. Integrating high-density array genotyping and whole-genome sequencing in a large general population cohort from Sardinia, we detected 23 associations at 10 loci. Five signals are due to variants at previously undetected loci: MPHOSPH9, PLTP-PCIF1, ZFPM1 (FOG1), NFIX and CCND3. Among the signals at known loci, ten are new lead variants and four are new independent signals. Half of all variants also showed pleiotropic associations with different hemoglobins, which further corroborated some of the detected associations and identified features of coordinated hemoglobin species production.

  Study EGAS00001003645

• Single-cell transcriptomics identifies pathogenic T-helper 17.1 cells and pro-inflammatory monocytes in ICI-related pneumonitis

  ICI-pneumonitis is a frequent serious adverse event of cancer immunotherapy hinging on a cell-mediated immune response, though the exact pathophysiology is currently unknown. Using single-cell transcriptomics, an enrichment of pathogenic (TBX21, RORC, IFNG, IL17A, CSF2 expressing) T-helper 17.1 cells and pro-inflammatory (TNF, IL1B, IL6, IL23A expressing) monocytes was identified in ICI-pneumonitis bronchoalveolar lavage fluid, putatively engaging in a feedforward inflammatory loop. This finding yields several novel therapeutic targets for the treatment of ICI-pneumonitis. Most notably repurposing anti-IL-23 merits further research as a potential efficacious and safe treatment for ICI-pneumonitis.

  Study EGAS00001006762

• Methylation-based classification of human mesenchymal chondrosarcoma

  We applied methylome and copy number profiling to a set of 45 well characterized MCS cases to evaluate their potential diagnostic value. Notably, the findings were reproducible also when analysing the round cell and cartilaginous component separately. Furthermore, four outliers were identified by methylome profiling for which the diagnosis had to be revised. Methylome profiling represents a sensitive, specific and reliable tool to support the diagnosis of MCS, particularly if only the round cell component is obtained in a biopsy and the diagnosis is not suspected. It can furthermore aid in confirming the diagnosis in case RNA sequencing for the HEY1::NCOA2 fusion transcript is not available.

  Study EGAS00001007042

• Spatially resolved antigen receptor and gene expression data from breast cancer patients

  The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011061

• August 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  August 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001008101

• Spatially resolved antigen receptor and gene expression data from human tonsil tissue

  The dataset includes spatially-resolved gene expression and antigen receptor data from two Tonsil samples (1 and 2). Tissue sections from the tonsil samples were used for spatial transcriptomics (Visium, 10x genomics). Tonsil 2 tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. Nearby or adjacent tissue sections (from Tonsil2) were also analyzed by a bulk antigen receptor sequencing approach (amplicon sequencing), by a method also newly developed by us in the same publication (Bulk SS3 VDJ). For Visium, the data were anonymized (all SNPs removed) using Bamboozle (Ziegenhain and Sandberg, Nature Communications 2021). The deposited data is in the form of fastq files. All remaining data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011062

• T2D-GENES Consortium: San Antonio Mexican American Family Studies (SAMAFS)

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to type 2 diabetes in multiple ethnic groups using next generation sequencing. To fulfill this objective, T2D-GENES Consortium undertook two large sequencing studies, called T2D-GENES Projects 1 and 2. Project 1 has carried out whole exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 non-diabetic controls, equally divided among five continental ancestry groups: Europeans, East Asians, South Asians, Hispanic Americans, and African Americans. The goal of Project 1 is to identify all genetic variants in the complete coding regions of the genomes (i.e., whole exome) by sequencing, including rare variants. Project 2 (i.e., SAMAFS substudy 2) is a pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence information from approximately 600 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas, augmented with family-based imputation into approximately 440 additional family members. The major objectives of Project 2 are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. Both T2D-GENES Projects 1 and 2 involve the San Antonio Mexican American Family Studies (SAMAFS), which are composed of four San Antonio, Texas-based family studies: the San Antonio Family Heart Study (SAFHS), San Antonio Family Diabetes/Gallbladder Study (SAFDGS), Veterans Administration Genetic Epidemiology Study (VAGES), and Family Investigation of Nephropathy and Diabetes - San Antonio (FIND-SA) Component and its extension called the Extended FIND [E-FIND]. The SAFHS and SAFDGS began in 1991 and have followed participants with extensive clinical phenotyping related to T2D for over 20 years. The VAGES was initiated in 1994 and a large battery of T2D-related phenotypic data has been obtained from its participants. The FIND-SA began in 2000, a part of the multicenter FIND study which was designed to identify genetic determinants of diabetic kidney disease; data from its participants related to T2D were used for this project. Non-overlapping subsets of SAMAFS participants are part of the T2D-GENES Projects 1 and 2, henceforth referred to as SAMAFS substudies 1 and 2, respectively. The SAMAFS substudies 1 and 2 are part of one of the five awards funded by NIDDK under a cooperative agreement award mechanism, which is governed by the Steering Committee of the T2D-GENES Consortium. Since Project 1 relies on population based subsets of cases and controls, 491 unrelated participants are drawn from the four SAMAFS as part of the T2D-GENES Project 1 Mexican American sample (i.e., SAMAFS substudy 1). The whole exome sequencing was performed at the Broad Institute. For Project 2, 1048 individuals are drawn from two SAMAFS (SAFHS and SAFDGS), representing 20 large families for substudy 2. The substudy 2 strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The available sample of 1,048 includes 5 sequenced individuals who do not belong to any of the 20 large pedigrees. The final family data of 1,043 individuals includes whole genome sequence data for 607 individuals. After quality control, 590 sequenced individuals provide data for family based imputation using Merlin linkage analysis software into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. The complete SAMAFS data including phenotype, genotype, sequence, other T2D-related trait data utilized for Projects 1 and 2 are available. These data can readily be viewed by clicking on the substudy title shown below or in the box: "Substudies", located on the right hand side of this parent or top study page phs000847.v1.p1, titled T2D-GENES Consortium: San Antonio Mexican American Family Studies (SAMAFS). phs000849 T2D-GENES Project 1: San Antonio Mexican American Family Studies (SAMAFS), Substudy 1: Whole Exome Sequencing phs000462 T2D-GENES Project 2: San Antonio Mexican American Family Studies (SAMAFS), Substudy 2: Whole Genome Sequencing in Pedigrees

  Study phs000847

• The Genetic Architecture of Smoking and Smoking Cessation

  This study includes samples from two projects: Collaborative Genetic Study of Nicotine Dependence (COGEND; PI: Laura Bierut) and University of Wisconsin Transdisciplinary Tobacco Use Research Center (UW-TTURC; PI: Timothy Baker). Data are available for an additional 1420 COGEND subjects through the Study of Addiction: Genetics and Environment (SAGE), dbGaP study accession phs000092. The majority of these subjects are independent from the current study, but there is a small amount of overlap between the two samples (n=29 subjects) for quality control purposes. It should be noted that the case definition in the SAGE study is DSM-IV alcohol dependence. The case definition in the current study is nicotine dependence by a current score of 4 or greater on the Fagerström Test for Nicotine Dependence (FTND). The overall goal of this project is to identify and characterize genetic variants that contribute to the development of nicotine dependence, related smoking behaviors, and smoking cessation. The COGEND sample includes unrelated cases and controls for a genetic association study of nicotine dependence. Cases are defined by a commonly used definition of nicotine dependence, a current score of 4 or more (maximum score of 10) on the Fagerström Test for Nicotine Dependence (FTND). Control status is defined as an individual who smoked at least 100 cigarettes during their lifetime, yet never became dependent (lifetime FTND=0). By selecting controls who smoked, those genetic effects that are specific to nicotine dependence can be examined. The UW-TTURC sample includes nicotine dependent smokers from three smoking cessation studies. Subjects had to smoke at least 10 cigarettes per day (confirmed smoking by an alveolar carbon monoxide (CO) level greater than 9) and report being motivated to quit smoking. Participants were excluded based on evidence of psychosis history, clinically significant depression symptoms, other severe mental illness, or contraindications to smoking cessation medications. COGEND: COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design is a community based case-control family study. Nicotine dependent cases and non-dependent, smoking controls were identified and recruited from Detroit and St. Louis. In addition, one sibling for each case and control subject was recruited in a subset of the sample. More than 54,000 subjects aged 25-44 years were screened by telephone; more than 3,100 subjects were personally interviewed; and more than 2,900 subjects donated blood samples for genetic studies. UW-TTURC: The UW-TTURC was initiated in 2001 as a study of nicotine dependence and smoking cessation treatment. The second round of UW-TTURC was initiated in 2005 as a study of efficacy of smoking cessation and long term outcomes. Nicotine dependent smokers seeking cessation treatment were identified and recruited from Madison and Milwaukee, WI. Over 9,000 adult smokers were screened by telephone; 2,575 individuals were enrolled and randomized to treatment conditions that involved use of different smoking cessation medications. Participants from the UW-TTURC smoking cessation clinical trials had the option of participating in a genetic substudy, and approximately 2,000 donated blood samples for genetic studies. The goal of the genetic studies of smokers seeking cessation treatment is to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes including cessation, withdrawal, and relapse. Both studies (COGEND and UW-TTURC) include measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, and educational attainment. Information on nicotine dependence, as assessed by the Fagerström Test for Nicotine Dependence (FTND) is available for all subjects. In addition, participants also completed the Nicotine Dependence Syndrome Scale (NDSS; Shiffman et al., 2004) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68; Piper et al, 2004). Coding for both individual variables and indices has been standardized across studies. All subjects were assessed in person by trained research assistants. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to the genetic architecture of smoking through large-scale genome-wide association studies of two well-characterized cohorts. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000404

• Genomewide Association Study of Inflammatory Bowel Disease - Combined Controls

  WTCCC genome-wide case-control association study for Inflammatory Bowel Disease (IBD) using six disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000007

• BLUEPRINT release August 2014, Bisulfite-Seq for hematopoietic multipotent progenitor cell

  Bisulfite-Seq data for 1 hematopoietic multipotent progenitor cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000917

• Comprehensive NGS Profiling to Enable Detection of ALK Gene Rearrangements and MET Amplifications in Non-Small Cell Lung Cancer

  We evaluate the analytical performance of the PGDx™ elio™ tissue complete assay, a 505 gene next-generation sequencing (NGS) tissue-based assay, that has now been FDA-cleared for use by physicians to help guide treatment decisions for cancer patients, using a NSCLC cohort of 38 patients.

  Dataset EGAD50000000016

• NeuroCHARGE Consortium GWAS of White Matter Hyperintensities on MRI

  Meta-analysis results of white matter hyperintensities (WMH) volume genome wide association study (GWAS) in older population-based studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the UK biobank.

  Study phs002227

• Myelofibrosis Etiology and Transplant Outcomes

  The Myelofibrosis Etiology and Transplant Outcomes study is a collaboration between the National Cancer Institute and the Center for International Blood and Marrow Transplant Research (CIBMTR). The study aims to identify genomic factors affecting myelofibrosis pathogenesis and prognosis.

  Study phs002635

• COVID-19 ACTIV-4 ACUTE: A Multicenter, Adaptive, Randomized Controlled Platform Trial of the Safety and Efficacy of Antithrombotic Strategies in Hospitalized Adults with COVID-19 (ACTIV4A)

  This is a randomized, open label, adaptive platform trial to compare the effectiveness of antithrombotic strategies for prevention of adverse outcomes in COVID-19 positive inpatients.

  Study phs002694

• Adipose tissue macrophage dysfunction is associated with a breach of vascular integrity in NASH

  Our study describes for the first time the myeloid cell landscape in human visceral adipose tissue at single-cell level within a cohort of well-characterized patients with non-alcoholic fatty liver disease.

  Study EGAS50000000424

• Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma

  Investigating the spatial architecture and microenvironment of EMD lesions in Multiple Myeloma patients using spatial whole transcriptome sequencing (10X Visium), tomography for spatially resolved transcriptomics (TOMO-seq), and single-cell RNA sequencing (scRNAseq)

  Study EGAS50000000227

• SIGi001-A-13 / SAMEA104386250 WGS data

  The iPSC line SIGi001-A-13 / SAMEA104386250 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 SIGi001-A-13 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001031

• BIONi010-C-8 / SAMEA4454011 WGS data

  The iPSC line BIONi010-C-8 / SAMEA4454011 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-8 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001028

• BIONi010-C-3 / SAMEA4342740 WGS data

  The iPSC line BIONi010-C-3 / SAMEA4342740 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-3 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001034

• BIONi010-C-7 / SAMEA4454010 WGS data

  The iPSC line BIONi010-C-7 / SAMEA4454010 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-7 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001035

• BIONi010-C-6 / SAMEA4454009 WGS data

  The iPSC line BIONi010-C-6 / SAMEA4454009 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-6 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001037

• BIONi010-C-9 / SAMEA4454012 WGS data

  The iPSC line BIONi010-C-9 / SAMEA4454012 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-9 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001040

• SIGi001-A-3 / SAMEA4448571 WGS data

  The iPSC line SIGi001-A-3 / SAMEA4448571 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 SIGi001-A-3 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001041

• BIONi010-C-4 / SAMEA4452060 WGS data

  The iPSC line BIONi010-C-4 / SAMEA4452060 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-4 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001043

• EDi014-A / SAMEA4459369 WGS data

  The iPSC line EDi014-A / SAMEA4459369 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 EDi014-A is available for research use at www.EBiSC.org.

  Dataset EGAD50000001051

• EDi014-B / SAMEA4459371 WGS data

  The iPSC line EDi014-B / SAMEA4459371 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 EDi014-B is available for research use at www.EBiSC.org.

  Dataset EGAD50000001053

• SIGi001-A-7 / SAMEA4448730 WGS data

  The iPSC line SIGi001-A-7 / SAMEA4448730 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 SIGi001-A-7 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001060

• BIONi010-C-5 / SAMEA4452061 WGS data

  The iPSC line BIONi010-C-5 / SAMEA4452061 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-5 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001062

• BIONi010-C-2 / SAMEA4342705 WGS data

  The iPSC line BIONi010-C-2 / SAMEA4342705 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-2 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001068

• SIGi001-A-1 / SAMEA4451096 WGS data

  The iPSC line SIGi001-A-1 / SAMEA4451096 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 SIGi001-A-1 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001073

• SIGi001-A-10 / SAMEA4451117 WGS data

  The iPSC line SIGi001-A-10 / SAMEA4451117 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 SIGi001-A-10 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001074

• SIGi001-A-11 / SAMEA4451118 WGS data

  The iPSC line SIGi001-A-11 / SAMEA4451118 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 SIGi001-A-11 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001075

• SIGi001-A-12 / SAMEA104237570 WGS data

  The iPSC line SIGi001-A-12 / SAMEA104237570 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 SIGi001-A-12 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001076

• SIGi001-A-2 / SAMEA4451116 WGS data

  The iPSC line SIGi001-A-2 / SAMEA4451116 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 SIGi001-A-2 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001077

• SIGi001-A-6 / SAMEA4447426 WGS data

  The iPSC line SIGi001-A-6 / SAMEA4447426 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 SIGi001-A-6 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001078

• SIGi001-A-4 / SAMEA4448632 WGS data

  The iPSC line SIGi001-A-4 / SAMEA4448632 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 SIGi001-A-4 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001088

• SIGi001-A-5 / SAMEA4448708 WGS data

  The iPSC line SIGi001-A-5 / SAMEA4448708 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 SIGi001-A-5 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001092

• SIGi001-A-8 / SAMEA4448777 WGS data

  The iPSC line SIGi001-A-8 / SAMEA4448777 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 SIGi001-A-8 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001093

• SIGi001-A-9 / SAMEA4447499 WGS data

  The iPSC line SIGi001-A-9 / SAMEA4447499 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 SIGi001-A-9 is available for research use at www.EBiSC.org.

  Dataset EGAD50000001095

• Comprehensive genomic characterization of early stage bladder cancer - whole exome sequencing data

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed WES data from 438 patients. The dataset is composed of 876 BAM files: 438 tumor samples and 438 paired blood samples.

  Study EGAS50000000511

• CANCAP03 single-nuclear RNA sequencing

  CANCAP03 single-nuclear RNA sequencing. Tissue punch cores for six patients, 3 from each of olaparib and olaparib and degarelix cohorts, were processed for nuclear isolation and extraction, prior to single-nuclear RNA sequencing. 10x libraries were constructed. Sequencing on NovaSeq 6000. FASTQ files were processed using CellRanger pipeline. Outputs from the CellRanger pipeline were analysed using the Seurat package in R.

  Dataset EGAD50000001280

• 10x Genomics BCR Sequencing

  BCR sequencing done with 10x Genomics V(D)J kit + platform for the paper: Integrated single cell analysis reveals co-evolution of malignant B cells and tumor micro-environment in transformed follicular lymphoma. Dataset contains paired FASTQ files for 35 samples, each was processed with the 10x Genomics V(D)J kit to obtain the sequence of the B-cell receptor sequence.

  Dataset EGAD50000001373

• PCR-Free Shallow Whole Genome Sequencing for Chromosomal Copy Number Detection

  Archival peripheral blood or bone marrow plasma samples from non small-cell lung cancer, B-cell lymphoma and acute myeloid leukemia patients, and healthy donors. Shallow whole genome sequencing of 100 Samples by two distinct library preparation methods (PCR = 45, PCR-Free = 55). sWGS done for Chromosomal CN detection.

  Dataset EGAD50000001404

• The dataset for Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types

  The dataset for “Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types” includes 288 bam files from whole genome, whole exome and targeted next-generation sequencing on the Illumina HiSeq2500 and MiSeq. The samples analyzed include tumor and normal tissue samples from patients with cancer.

  Dataset EGAD50000000744

• Head and Neck Organoid Biobank cohort, Issing et. al., 2025, RNA + WES data

  This dataset contains the raw fastq files of RNA and whole exome sequencing of the head and neck organoid biobank. For RNA sequencing, 41 organoid samples collected at different timepoints as well as before and after genetic modification are included. For WES, 25 samples of early organoid cultures or matching tumor tissues are included.

  Dataset EGAD50000001733

• Transcriptome profiling of slice cultures of human embryonic forebrain

  7 samples from slice cultures kept for 4-29 days from forebrain of the developing human embryo at ages between post conceptional week 7.5 and 10.5 were dissociated and single cells were collected and processed without bias for mRNA-seq using the 10Xgenomics chromium 3' protocol version 3. Libraries were sequenced on Illumina NovaSeq 6000 and reads aligned against the human GRCh38 genome.

  Dataset EGAD50000001690

• Single nucleus and spatial transcriptomic characterization of prostate cancer versus normal controls

  10x Genomics Visium and Chromium platforms were used for spatial and single-nuclei RNA-sequencing, respectively. Cohort consisted of 8 prostate cancer patients (8 prostate cancer + 8 paired non-malignant control samples, fresh-frozen)

  Study EGAS50000001143

• Dataset of 10 WES from bladders tumors and PBMC of 4 non-muscle invasive bladder cancer patients

  WES (FASTQ files) of paired tumor and PBMC samples of non-muscle-invasive bladder cancer patients. All CTRL samples originate from PBMC. For patient UC2, DNA from 3 different synchronous tumors were extracted. DNA from tumors was extracted from fresh frozen tumors,except for patient UC4 (FFPE). NB : patients aliases are the following :ROXY : UC1, OMAD : UC2, MAKI : UC3, HAPE : UC4

  Dataset EGAD50000002008

• Neuroblastoma cell-lines and healthy cfDNA used for the benchmarking study

  This dataset comprises nine neuroblastoma cell lines used to construct the reference matrix for the RRBS-CL dataset in the corresponding benchmarking study. In addition, it includes one extra neuroblastoma cell line and a pooled cfDNA sample from multiple donors, which were used to generate the in silico mixtures. All the samples were profiled using DNA methylation sequencing.

  Dataset EGAD50000002199

• Altered Blood and Keratinocyte microRNA/transfer RNA Fragment Profiles Related to Fibromyalgia Syndrome and its Severity

  We provide small RNA transcriptome signatures of whole blood and cultured keratinocytes, which separate FMS patients from healthy controls. This opens new perspectives for FMS diagnostics, symptom monitoring, and clinical management.

  Study EGAS50000000638

• Multimodal epigenetic sequencing analysis of cell-free DNA identifies biomarkers for ALS diagnosis and progression

  The role of the epigenome in age-related neurodegenerative disorders remains understudied. Here, we analyzed circulating cell-free DNA (cfDNA) from blood to detect methylation changes as a liquid-biopsy for Amyotrophic Lateral Sclerosis (ALS). Our study included 20 patients with sporadic ALS, 10 patients with C9orf72-associated ALS, 10 asymptomatic carriers of the C9orf72 repeat expansion mutation, and 21 non-disease controls.

  Dac EGAC50000000739

• Transcriptional and epigenetic profiling of bone marrow blood progenitors across age

  Bone marrow from donors without known malignancy was isolated and CD34-enriched, FACS-purified to further enrich for CD34+CD38- primitive cells, and then profiled by single-cell 10X genomics chromium Multiome (RNA+ATAC).

  Study EGAS50000001623

• Whole exome and whole genome sequencing of juvenile myelomonocytic leukemia (JMML)

  To obtain a complete registry of gene mutations in JMML, whole-exome sequencing was performed for paired tumor-normal DNA from 13 JMML cases, of which 2 cases were also analyzed by whole genome sequencing.

  Study EGAS00001000521

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Myeloid Leukemia, on genome GRCh38

  Bisulfite-Seq data for 19 Acute Myeloid Leukemia sample(s). 338 run(s), 32 experiment(s), 38 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002419

• BLUEPRINT release August 2016, Bisulfite-Seq for osteoclast, on genome GRCh38

  Bisulfite-Seq data for 2 osteoclast sample(s). 88 run(s), 4 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002427

• BLUEPRINT release August 2016, Bisulfite-Seq for Mantle Cell Lymphoma, on genome GRCh38

  Bisulfite-Seq data for 5 Mantle Cell Lymphoma sample(s). 65 run(s), 5 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002505

• BLUEPRINT release August 2016, Bisulfite-Seq for thymocyte, on genome GRCh38

  Bisulfite-Seq data for 1 thymocyte sample(s). 14 run(s), 1 experiment(s), 2 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002440

• Illumina short-reads and 10X Genomics linked-reads sequencing data for MCF7 and a primary breast tumor sample

  This dataset contains whole genome sequencing data from Illumina short-reads sequencing (2X150bp) and 10X Genomics linked-reads sequencing. Both the sequencing technologies were used to sequence MCF7 cell line and a primary breast triple-negative cancer sample. The fastq of paired-end reads for both the samples sequenced with both the technologies is available.

  Dataset EGAD00001005724

• BLUEPRINT release January 2015, Bisulfite-Seq for alternatively activated macrophage

  Bisulfite-Seq data for 4 alternatively activated macrophage sample(s). 64 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001143

• Genetic screening of GPI-anchor protein synthesis

  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/

  Dataset EGAD00001001928

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - chemo_cohort

  Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - chemo_cohort

  Dataset EGAD00001002241

• Whole Genome Sequencing of INTERVAL (2019-06-12)

  15x Whole Genome Sequencing of 15,000 individuals from the INTERVAL study cohort, phase III. 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 2019-06-12.

  Dataset EGAD00001005086

• BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 Summary Statistics of Most Significant Associations

  BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 summary statistics of most significant association data include five molecular traits (eQTL, hQTL(H3K27ac), hQTL(H3K4me1), mQTL, and psiQTL) for three primary blood cells (Monocytes, Neutrophils, and T-cells). Each summary statistics file contains the most significant association for each phenotype ID.

  Dataset EGAD00001005200

• BLUEPRINT release January 2015, Bisulfite-Seq for mature neutrophil

  Bisulfite-Seq data for 6 mature neutrophil sample(s). 79 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001201

• BLUEPRINT release August 2014, Bisulfite-Seq for mature neutrophil

  Bisulfite-Seq data for 6 mature neutrophil sample(s). 79 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000935

• BLUEPRINT release January 2015, Bisulfite-Seq for germinal center B cell

  Bisulfite-Seq data for 1 germinal center B cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001203

• BLUEPRINT release August 2015, Bisulfite-Seq for Acute Myeloid Leukemia, on genome GRCh38

  Bisulfite-Seq data for 6 Acute Myeloid Leukemia sample(s). 66 run(s), 6 experiment(s), 6 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001482

• BLUEPRINT release August 2014, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000915

• Isotype-resolved sequencing of B-cell receptor in health and disease (2017-09-13)

  Sequencing of B-cell receptor repertoires in healthy individuals and patients with chronic lymphocytic leukemia. 1) 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 2017-09-13.

  Dataset EGAD00001003748

• A108879A

  Whole genome sequencing for single cells for library A108879A 1567 cells; filetype=bam

  Dataset EGAD00001007093

• BLUEPRINT release August 2014, RNA-Seq for common lymphoid progenitor

  RNA-Seq data for 3 common lymphoid progenitor sample(s). 15 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000918

• BLUEPRINT release January 2015, Bisulfite-Seq for memory B cell

  Bisulfite-Seq data for 1 memory B cell sample(s). 20 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001131

• Australia and New Guinea haplotype phasing (2017-06-27)

  Whole-genome sequencing and phasing of admixed Aboriginal Australian genomes and Papua New Guinean genomes using 10x Genomics Chromium technology. 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 2017-06-27.

  Dataset EGAD00001003407

• BLUEPRINT release August 2015, Bisulfite-Seq for Acute promyelocytic leukemia, on genome GRCh38

  Bisulfite-Seq data for 3 Acute promyelocytic leukemia sample(s). 24 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001537

• BLUEPRINT release August 2014, RNA-Seq for granulocyte monocyte progenitor cell

  RNA-Seq data for 3 granulocyte monocyte progenitor cell sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000922

• BLUEPRINT release January 2015, Bisulfite-Seq for hematopoietic multipotent progenitor cell

  Bisulfite-Seq data for 1 hematopoietic multipotent progenitor cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001141

• BLUEPRINT release January 2015, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001140

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001186

• BLUEPRINT release August 2016, DNase-Hypersensitivity for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  DNase-Hypersensitivity data for 28 CD14-positive, CD16-negative classical monocyte sample(s). 28 run(s), 28 experiment(s), 28 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_dnaseseq_analysis_20160816

  Dataset EGAD00001002286