15130 results for "cancer studies using rna-seq"

in 24.84 milliseconds.

• EGAD00010000488

  Chondroblastoma case sample genotype using Affymetrix SNP6.0

  Dataset EGAD00010000488

• iOmics_genomic_data

  iOmics genomic data using 2.5M and Exome array

  Dataset EGAD00010001309

• A3164_ClariomD

  ALL SAMPLES USING ClariomD microarray (Affymetrix)

  Dataset EGAD00010001705

• BIOCLOCK Study Methylation Data

  Whole blood methylation data measured using Illumina MethylationEPIC v2.0 array

  Dataset EGAD00010002801

• INVADE cohort

  Cohort of bulk RNAseq data from DCIS, invasive and microinvasive breast cancer at diagnosis. This dataset covers 18 DCIS cases, 17 microinvasive and 20 primary invasive breast cancer.

  Study EGAS50000000219

• DAC for "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis"

  DAC for the data of "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis" with: • Holger Sültmann • h.sueltmann@dkfz.de • German Cancer Research Center (DKFZ) • Ann-Kathrin Daum • a.daum@dkfz.de • German Cancer Research Center (DKFZ)

  Dac EGAC50000000076

• Organoid_Derivation_Project___GRCh38___RNAseq

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

  Study EGAS00001004677

• Organoid_Derivation_Project___GRCh38___WGS

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines

  Study EGAS00001004712

• Organoid_Derivation_Project___GRCh38___TGS

  Generation of normal and cancer organoids for sequencing as part of the WTSI-CRUK international collaboration with NCI (in HCMI) to generate the next generation of cancer cell lines.

  Study EGAS00001007349

• Using de novo assembly to identify structural variation of complex immune system gene regions

  Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity. Several factors contribute to this including inheritable structural polymorphism of the underlying genes. To investigate this we generated data for a single healthy European individual (identified as HV31) using multiple long-read (PacBio Sequel II and Oxford Nanopore), short-read (Illumina HiSeq and MGI G400) and linked-read (10X Chromium and stLFR) sequencing platforms, and optical mapping using the Bionano Saphyr platform. DNA was extracted from monocytes or from peripheral blood mononuclear cells. We used these data to assemble a set of regions encoding components of the immune system and to investigate structural variation. The raw data and analysis results are deposited here for generate research use. A complete description of these data and results can be found in the accompanying article at https://doi.org/10.1101/2021.02.03.429586.

  Study EGAS00001005046

• Cancer Genomics, ICR, cell line data

  DAC for cell line (non patient) data from the Cancer Genomics group, Division of Genetics and Epidemiology at the Institute of Cancer Research, Sutton, UK.

  Dac EGAC50000000023

• Validation_for_human_early_embryonic_substitutions_

  PCR and MiSeq validation for early embryonic substitution candidates from 400 Breast cancer patients

  Study EGAS00001001218

• Whole Exome Sequencing analysis of three tumor biopsies from a LUAD patient

  Whole Exome Sequencing (WES) analysis was performed on three distinct tumor biopsies collected at different time points, along with their matched germline non-tumor sample, from the same LUAD patient. DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD) according to the manufacturer’s instructions and quantified using the Qubit Fluorometer assay (Life Technologies, Carlsbad, CA). To minimize FFPE-related sequencing artifacts (e.g., C:G > T:A transitions), the extracted DNA was treated with the DNA repair enzyme Uracil-DNA-Glycosylase (UDG) following the manufacturer’s protocol (New England Biolabs, Ipswich, MA). Whole-exome capture was performed with the Twist Human Core Exome + RefSeq + Mito-Panel kit (Twist Bioscience), in accordance with the manufacturer’s guidelines. Sequencing generated paired-end 100-bp reads on the Illumina NovaSeq 6000 platform. The resulting reads were aligned to the reference human genome (GRCh38) using the Burrows-Wheeler Aligner (BWA, v0.7.12).

  Dataset EGAD50000001198

• Reconstruction of human phylogenetic trees using single-cell genome sequencing

  The trillions of cells that constitute the human body are developed from a fertilized egg through embryogenesis. However, cellular dynamics and developmental outcomes of embryonic cells remain to be largely unknown due to the technical and ethical challenges. Here we sequenced whole-genomes single-cell-derived clones and various bulk tissues dissected from entire human bodies. Using the discovered somatic mutations as an intrinsic barcode, we reconstructed cellular phylogenetic trees that provide novel insights into early human embryogenesis.

  Study EGAS00001004824

• Annotated VCF Files for WGS of ASD Cohort with 68 Individuals from 22 families, enriched for recent shared ancestry

  We performed whole genome sequencing (WGS) in an ASD cohort of 68 individuals from 22 families enriched for recent shared ancestry. Samples were sequenced using Illumina HiSeq X platform, and Variants (single nucleotide variants (SNVs) and insertions or deletions (indels)) were detected using GATK with HaplotypeCaller. Quality control checks for (i) duplicate samples, (ii) samples per platform, (iii) genome call rate, (iv) missingness rate, (v) singleton rate, (vi) heterozygosity rate, (vii) homozygosity rate, (viii) Ti/Tv ratio, (ix) inbreeding coefficient, and (x) sex inference were performed as previously described. Variant call format (VCF) files for SNVs and indels were annotated with ANNOVAR using allele frequencies from the 1000 Genomes project (2015; 1000G), the Genome Aggregation Database (gnomAD), and the Greater Middle East Variome Project (GME).

  Dataset EGAD00001008634

• Targeted DNA panel sequencing analysis of PanNEN tumors of varying grades using custom and commercial panels

  Targeted panel sequencing data from PanNEN samples. Sample ID is annotated in the following manner: each patient is given a number and "P" is appended to the patient number if it is a primary tumor, "M” if it is metastasis and "N" if it is normal (healthy tissue) sample. All NETG1 and NETG2 samples underwent panel sequencing using a custom panel (in-house PanNEN panel). All NEC and NETG3 samples (except PNET2, PNET77P and PNET77M) underwent panel sequencing using a commercial CCP panel.

  Dataset EGAD00001008432

• eMERGE Network Phase III: HRC SNV and 1000 Genomes SV Imputed Array Data of 105,000 Participants

  The Electronic Medical Records and Genomics (eMERGE) Network is a National Institutes of Health (NIH)-organized and funded consortium of U.S. medical research institutions. The primary goal of the eMERGE Network is to develop, disseminate, and apply approaches to research that combine biorepositories with electronic medical record (EMR) systems for genomic discovery and genomic medicine implementation research. eMERGE was announced in June 2007 and began its third phase in September 2015. eMERGE Phase III (June 2015 - June 2020) consists of 10 study sites, two central sequencing and genotyping facilities, and a coordinating center. Included in this study are: Human Reference Consortium (HRC) single nucleotide variants and 1000 Genomes structural variants imputed array data of 105,108 eMERGE participants from nine Phase III study sites and three Phase II study site collaborators. Corresponding demographics, body mass index measurements. Case/control status for the following phenotypes: Abdominal aortic aneurysm; Ace-Inhibitor/Cough; Attention Deficit Hyperactivity Disorder; Age-related macular disease; Appendicitis; Asthma; Atopic Dermatitis; Autism; Benign Prostatic Hyperplasia; Carotid artery disease as a Quantitative Measure; caMRSA; Cataract; Clostridium difficile colitis; Extreme Obesity; Chronic Kidney Disease; Chronic Kidney Disease and Type 2 Diabetes; Chronic Kidney Disease, Type 2 Diabetes and Hypertension; Colon Polyps; Cardiorespiratory Fitness; Dementia; Diverticulosis; Diabetic retinopathy; Gastroesophageal Reflux Disease; Glaucoma; Height; Heart failure; Hypothyroidism; Lipids; Ocular hypertension; Peripheral Arterial Disease; QRS duration; Red blood cell indices; Remission of Diabetes after ROUX-EN-Y gastric bypass surgery; Resistant hypertension; MACE while on Statins; Type 2 Diabetes; Venous Thromboembolism; White blood cell indices; and Zoster virus infection. Study sites and participants include: Boston Children's Hospital: The Gene Partnership (TGP) is a prospective longitudinal registry at Boston Children's Hospital (BCH) to study the genetic and environmental contributions to childhood health and disease, collect genetic information on a large number of children who have been phenotyped, and implement the Informed Cohort and the Informed Cohort Oversight Board (ICOB). The term "The Gene Partnership" reflects a partnership between researchers and participants. Children seen at BCH are offered enrollment, as are their parents and siblings. DNA is collected on all enrollees. BCH has a comprehensive EMR system, and virtually all inpatient and outpatient data are captured electronically. Clinical data in the BCH EMR is loaded in the i2b2 data warehouse which is available to investigators. Cases, phenotypes, and covariates are ascertained using the i2b2 database. Participants at BCH in TGP have consented to receive any research result and/or incidental finding that arises from studies using TGP that is approved by the Informed Cohort Oversight Board (ICOB) and is in accordance with the participants'preferences;results are returned through the Personally Controlled Health Record (PCHR). BCH and Cincinnati Children's Hospital Medical Center (CCHMC) have partnered as the Pediatric Alliance for Genomic and Electronic Medical Record (EMR) Research (PAGER) site for the eMERGE Phase II network for pediatric institutions, and the cohort for eMERGE at BCH is TGP. Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. A large majority of participants consenting to prospective genomic analyses also consent to analysis of their de-identified electronic health records (EHRs). EHRs are longitudinal, with a mean duration of 6.5 years. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): Cincinnati Children's Hospital Medical Center (CCHMC) is a pediatric institution dedicated to improving health and welfare of children and to the discovery and practical application of new genomic information to the ordinary care of children. CCHMC brings an extraordinary faculty to eMERGE III who are committed to gain a better understanding of the genesis of disease and to elucidate the mechanisms of diseases that afflict children, specifically pediatric disease phenotypes that will leverage the available eMERGE adult genomic data and electronic medical records (EMRs) to discover meaningful use results. Generation of EMR phenotype algorithms, informed by natural language processing, using heuristic and machine learning methods is ongoing. CCHMC has developed tools to evaluate adolescent return of results preferences, examined the ethical and legal obligations and potential to reanalyze results, and developed clinical decision support for phenotyping, test ordering, and returning sequencing results. The success of these eMERGE III studies is enhanced by the ongoing institutional investment made in the CCHMC Biobank, the comprehensive EMR (EPIC), the i2b2 de-identified medical record data warehouse, and hundreds of faculty and senior staff who make genomics or informatics an active focus of their research. Columbia University: Columbia University Medical Center/New York Presbyterian (CUMC/NYP) Hospital system is one of the nation's largest and most comprehensive hospital systems with over 2 million inpatient and outpatient yearly visits that serves a racially and ethnically diverse urban patient population. The Columbia University GENomic Integration with EHR (GENIE) research study contributed and shared phenotype and genotype data for individuals who were recruited as part of a diverse array of initiatives within the hospital, including Northern Manhattan Study (NOMAS), Pediatric Cardiac Genomic Consortium (PCGC), Caribbean Hispanics with Familial and Sporadic Late Onset Alzheimer's disease (AD), Alzheimer's Disease Sequencing Project (ADSP), and Genetics of Chronic Kidney Disease study. Some of these individuals had kidney or neurological problems, some were healthy adult volunteers with self-reported health status information from the medically underserved Northern Manhattan community, and others were pediatric patients with cardiac conditions. For the kidney disease cohort, patients with the diagnosis of Chronic Kidney Disease (CKD) and healthy controls were recruited to the Columbia University CKD biobank. For the NOMAS cohort, eligible participants were stroke-free, were 40 years old, and resided for at least 3 months in a Northern Manhattan household with a telephone. The PCGC study recruited parent-offspring trios with pediatric probands diagnosed with congenital heart defects (CHD). For the Caribbean Hispanics with Alzheimer's disease project, individuals from families affected by AD and with sporadic AD were recruited, along with unrelated controls. Samples for the ADSP study have been selected from well-characterized cohorts of individuals with AD diagnosis. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All participants provided written informed consent and HIPAA authorization. Consenting patients agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. Partners Health Care (Harvard): The Partners HealthCare Biobank is a large research program designed to help researchers understand how people's health is affected by their genes, lifestyle, and environment. This large research data and sample repository provides access to high-quality, consented blood samples to help foster research, advance our understanding of the causes of common diseases, and advance the practice of medicine. For the Partners research community (Massachusetts General Hospital and Brigham and Women's Hospital), the Biobank provides: Banked samples (plasma, serum, and DNA) collected from consented patients Blood samples that were discarded after clinical testing in the Crimson Cores maintained in the Brigham and Women's Hospital and Massachusetts General Hospital Pathology Departments Sample handling and preparation services Link to the biobank data to the Partners Research Patient Data Registry (RPDR) a research instance of our electronic clinical chart Data access through our research portal. To date, over 60,000 Partners patients have given their consent to enroll, give a blood sample, receive research results and agreed to be re-contacted for additional research studies. The Biobank has enabled Partners investigators to compete for nationally recognized grants in personalized medicine such as a clinical electronic Medical Records and Genomics network (eMERGE) site and the national All of US program. The Biobank currently supports over 120 Partners investigators and over 100 million dollars in NIH research. Kaiser Permanente Washington with the University of Washington and the Fred Hutchinson Cancer Research Center: KPWA participants were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~8,073.) NWIGM is based at the University of Washington and co-managed by the University of Washington and KPWA. The purpose of the NWIGM biorepository is to build infrastructure and resources to carry out a broad range of future genetic research. KPWA members enrolled in the biorepository are asked to provide informed consent to providing a DNA sample for storage in the NWIGM biorepository. The consent is purposefully broad to serve the dual purpose of reducing the burden on researchers who wish to use this biorepository and the IRB committees who will be responsible for reviewing these requests in the future. Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in KPWA's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at KPWA. 1) Demographics - participants with self-reported race as Asian ancestry were prioritized and selected to enrich for non-European ancestry. The KPWA eMERGE cohort includes n=1,245 members of Asian ancestry. 2) Participants were also selected for a history of colorectal cancer (n=1,002), in order to allow us to enrich germline pathogenic variants. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Mayo Clinic: The Mayo Vascular Disease Biorepository is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval;this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG , was <1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR. Case control study of venous thromboembolism (PI John Heit) Controls from a case control study of pancreatic cancer (PI Gloria Petersen) Mayo Clinic Biobank. Icahn School of Medicine at Mount Sinai School (Mt. Sinai): The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants'DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University Medical Center: BioVU, Vanderbilt's DNA databank, was designed as an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations. BioVU is currently the largest single site DNA collection world-wide, at >235,000 samples as of spring 2017.

  Study phs001584

• Genomic profiling identified ERCC2 helicase domain mutations respond to platinum-based neoadjuvant therapy in urothelial bladder cancer

  Genomic profiling of tumours enables therapeutic decisions, and identifying drug-matched mutations will prolong survival and prognosis. Here, we generated a custom panel for detecting genetic alterations in 19 patients with urothelial bladder cancer. This panel targeted 71 genes associated with urological cancer. Targeted sequencing was performed on formalin-fixed paraffin-embedded tumour tissues. In this study, we revealed genome profiles and actionable mutations in patients with urothelial bladder cancer.

  Study JGAS000241

• Broad utility of ultrasensitive analysis of ctDNA dynamics across solid tumors treated with immunotherapy

  Plasma ctDNA sequencing data from samples collected from a pan-cancer cohort of patients with advanced-stage disease treated with IO therapies. This study highlights the broader applicability of ultrasensitive ctDNA as a biomarker across cancer types and immunotherapy modalities. Here, we applied a whole-genome, tumor-informed approach to study ctDNA in 202 advanced-stage cancer patients treated at VHIO. This study captures the plasma sequencing data from the project.

  Study EGAS50000001271

• Comparative genomics research for Chinese colorectal cancer

  In order to clarify the mechanisms of genesis and development of colorectal carcinoma, in this study, we performed comparative genomics research in Chinese colorectal cancer at various clinical stage via whole exon sequencing or whole genome sequencing. Comparative bioinformatical analysis was perform to find the difference on somatic single-nucleotide variants, short insert/deletion variants, genomic structural variants, and copy number variants between cancer tissue and normal tissue, or among cancer tissues at various clinical stages.

  Study EGAS00001002576

• Comparative genomics research for Chinese colorectal cancer

  In order to clarify the mechanisms of genesis and development of colorectal carcinoma, in this study, we performed comparative genomics research in Chinese colorectal cancer at various clinical stage via whole exon sequencing or whole genome sequencing. Comparative bioinformatical analysis was perform to find the difference on somatic single-nucleotide variants, short insert/deletion variants, genomic structural variants, and copy number variants between cancer tissue and normal tissue, or among cancer tissues at various clinical stages.

  Study EGAS00001002607

• Sequencing data for oesophageal and related samples - OACs release 5 (RNA)

  Dataset EGAD00001005383

• Sequencing data for oesophageal and related samples - Normals release 6 (RNA)

  Dataset EGAD00001005376

• Sequencing data for oesophageal and related samples - BOs release 6 (RNA)

  Dataset EGAD00001005377

• Sequencing data for oesophageal and related samples - OACs release 4 (RNA)

  Dataset EGAD00001004021

• RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases

  This study is to understand of the molecular mechanisms driving metastasis.

  Study EGAS00001004078

• Unraveling the heterogeneity of multiple myeloma identifies therapy resistant subpopulation with vulnerability to the splicing pathway intervention

  Intra-tumor heterogeneity underlies the therapy resistance of multiple myeloma (MM). However, its details remain unknown. Here, we performed single-cell RNA sequencing coupled with VDJ targeted sequencing on highly purified bone marrow lineage marker-negative CD38+ MM cells from patients at diagnosis and in remission. This approach successfully subdivided MM cells into distinct clusters, unraveling their heterogeneity and plasticity on the axis of cell surface CD138 expression. Notably, unique clusters were identified in the minor CD138-negative population that resisted therapies. CRISPR/Cas9 screening of upregulated genes in the resistant population identified their vulnerability to the splicing pathway inhibition, among which RNA-binding protein 39 (RBM39) appeared to be overexpressed in the resistant population and involved in aberrant splicing. Genetic and pharmacological inhibition of RBM39 exerted a profound synthetic lethal effect on them. Our results unveil an uncharacterized subpopulation associated with therapy resistance and provide a novel therapeutic strategy targeting the splicing pathway in MM.

  Study JGAS000723

• Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas

  RNA polymerase II mediates the transcription of all protein-coding genes in eukaryotic cells, a process that is fundamental to life. Genomic mutations in this enzyme have not been previously linked to any pathology in humans, a testament to its indispensable role in cell biology. Based on a combination of next-generation genomic analyses of 775 meningiomas, we report that recurrent somatic p.Gln403Lys or p.Leu438_His439del mutations in POLR2A, which encodes the catalytic subunit of RNA polymerase II1, are sufficient to hijack this essential enzyme and drive neoplasia. POLR2A mutant tumors reveal dysregulation of key meningeal identity genes, including WNT6 and ZIC1/ZIC42,3. In addition to POLR2A, NF2, SMARCB1, TRAF7, KLF4, AKT1, PIK3CA and SMO4-8, we also report somatic mutations in AKT3, PIK3R1, PRKAR1A, and SUFU in meningiomas. Our results identify a role for essential transcriptional machinery in driving tumorigenesis and define mutually exclusive meningioma subgroups with distinct clinical and pathological features.

  Study EGAS00001001916

• Single-cell ATAC-seq analysis for COVID19 patients

  While SARS-CoV-2 infection causes mild respiratory disease in most individuals, a small group of patients develops severe COVID-19. Dysfunctional innate immune responses have been identified to contribute to differences in COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell epigenetics, transcriptomic, and genetics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified 41.3% of significantly up-regulated genes in hospitalized COVID-19 patients potentially induced by differential chromatin accessibility. Sub-clustering and motif-enrichment analyses of monocytes reveal disease condition-specific regulation by transcription factors, such as C/EBPs and SPI1, and their targets, including a long-noncoding RNA LUCAT1, which further regulates interferon responses and is associated with the need for oxygen supply of COVID-19 patients. The interaction between C/EBPs and LUCAT1 was validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in promoters/enhancers of COVID-19 patients. Integrating our data with publicly available expression quantitative trait loci and chromosomal interactions indicates that ASoC SNP rs6800484-C is associated with lower expression of CCR2, which may contributeto higher viral loads in lungs and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to the innate immune responses of different COVID-19 patients.

  Study EGAS00001006559

• Colorectal_organoids_and_tumour_tissue___Whole_Genome_X10

  Whole genome sequencing of single cell derived organoids from normal colon tissue and colorectal cancer.

  Study EGAS00001001100

• Whole Genome Sequence and RNASeq Samples for Lung Cancer

  Short Read Whole Genome Sequence and RNASeq Samples for Lung Cancer

  Study EGAS00001007832

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564

• Whole genome sequencing of Sinonasal hemangiopericytoma and patient derived cell line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1 using whole genome sequencing.

  Study EGAS50000000025

• Whole-Genome and Exome Sequencing in clear-cell Renal Cell Carcinoma (ccRCC)

  Study to evaluate the molecular genetics of ccRCC through integrative analyses of tumors and tumorgrafts using multiple platforms in clinically annotated samples. Genome and exome data for patients giving consent to have the information available on a public, secure, database is being submitted.

  Study phs000491

• Cardiometabolic effects of Anaerobutyricum soehngenii

  This study investigated the effects of the probiotic strain Anaerobutyricum soehngenii on plasma metabolites and cardiometabolic parameters. Strain engraftment was determined through strain tracking of fecal metagenomics. Plasma metabolites levels were determined using untargeted metabolomics. Additionally the interaction with metformin dose was studied.

  Study EGAS50000000415

• Sex Differences in MAGEL2 Gene Promoter Methylation in High Functioning Autism - Trends from a Pilot Study Using Nanopore Cas9 Targeted Long Read Sequencing

  Using DNA extracted from peripheral blood, Cas9-targeted nanopore DNA sequencing was used to analyze MAGEL2 gene, including its entire regulatory construct (chr15:23639316-23651466), for sequence variation and 5-methyl-cytosine (5mC) modification in a cohort of adults with HFA compared to sex- and age-matched NC.

  Dataset EGAD50000000726

• Moroccan Genome Project: Sequencing Data from 109 Whole Genomes of Moroccan Individuals

  This study is the first phase of the Moroccan Genome Project, which included the complete sequencing of 109 genomes from the Kingdom of Morocco. The sequencing was performing using the Illumina NovaSeq6000 platform, with a mean coverage of 30X.

  Study EGAS50000000529

• Hi-C analysis of patient-derived pancreas neoplasm organoids

  Pancreatic cancers arise from two different precursors; intraductal papillary mucinous neoplasms (IPMN) and pancreatic intraepithelial neoplasm (PanIN), while biological differences in cancers originated from thme remain obscure. Here, we analyzed their three-dimensional genome structure by Hi-C using patient-derived organoids.

  Study JGAS000262

• Comprehensive analysis of the abnormality of the genes in juvenile myelomonocytic leukemia

  To develop a simple and robust methylation test for risk stratification of patients with juvenile myelomonocytic leukemia. We used the DREAM method, a method for whole genome methylation analysis using NGS, to analyze methylation profiling of patients with juvenile myelomonocytic leukemia.

  Study JGAS000292

• WTCCC case-control study for Bipolar Disorder - Combined Controls

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

  Study EGAS00000000002

• TenK10K Phase 1: Whole Genome Sequencing SNP + indels multi-sample VCFs

  SNP and indel variants were called for 1,925 samples from phase 1 of the TenK10K project. Variant calling from WGS alignments to the GRCh38 reference assembly was performed using GATK4 HaplotypeCaller in DRAGEN mode. These VCFs consist of common (>=1% minor allele frequency [MAF]) and rare (<1% MAF) variants . This dataset is comprised of autosomal variants provided as multisample compressed VCF format files. Principal component scores were derived by using gnomAD’s run_pca_with_relateds method.

  Dataset EGAD50000002377

• Precancerous lesions in Lynch Syndrome

  This dataset includes whole-exome sequencing data from precancerous colorectal lesions (adenomas and advanced adenomas) obtained from 19 patients with Lynch syndrome. Forty-four samples are analyzed, including 33 mismatch repair–deficient (dMMR) and 11 mismatch repair–proficient (pMMR) lesions as assessed by immunohistochemistry. Library preparation was performed using the Twist Human Core Exome + RefSeq + Mitochondrial Panel, followed by sequencing on the Illumina NovaSeq 6000 platform. Aligned BAM files were generated with the DRAGEN pipeline using the human reference genome hg38

  Dataset EGAD50000002222

• WGS data of paediatric hyperdiploid B cell acute lymphoblastic leukemia (set3)

  The dataset includes FASTQ and BAM/CRAM files from diagnostic and matched remission (control) samples of eight hyperdiploid ALL patients (ALLT-364, ALLT-367, ALLT-368, ALLT-371, ALLT-377, ALLK-111, ALLT-382, ALLT-395). Sequencing libraries were made using PCR free-based WGS library preparation (PCR-), NEBNext® Ultra™ II DNA Library Prep Kit (Cat No. E7645). Both diagnostic (90x) and remission (30x) samples were sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000002427

• Human_Evolution_3B

  The mtDNA and Y chromosome of up to 15 Australian Aborigines, concentrating on individuals with indigenous lineages will be sequenced using the standard whole-genome sequencing followed by filtering out autosomal and X sequences, so that only mtDNA and the Y chromosome would be analysed and released.

  Study EGAS00001000718

• Mutations in PNPLA6 are linked to photoreceptor degeneration and various forms of childhood blindness

  In this work, using exome sequencing, we identified biallelic PNLPA6 mutations in patients with childhood blindness due to severe photoreceptor death and clinical features of Leber congenital amaurosis (LCA) and, interestingly, also of the rare Oliver McFarlane Syndrome

  Study EGAS00001001013

• RA-Map Early Rheumatoid Arthritis patient genotyping (InfiniumCoreExome-24-v1)

  Genotyping of 244 early RA patients and 44 vaccine recipient controls was performed using the Illumina InfiniumCoreExome-24-v1-1 according to the manufacturer’s SOP. Raw idats from the Illumina iScan instrument were imported into GenomeStudio (v2011.1). Samples &lt; 90 % call rate were excluded. Data was exported to PLINK PED/MAP format on the forward strand. Data was converted from PED/MAP to BED/BIM/FAM using PLINK v1.07.

  Dataset EGAD00001006736

• Fine-mapping clustered GWAS hits enhances the identification of disease risk and protective genetic variants

  This dataset contains: 1) Raw FASTQ and BAM files for short reads. Here, DNA libraries were prepared using Nextera Rapid Capture Custom Enrichment kit (Illumina) and paired-end sequenced on a HiSeq2500 (Illumina). 2) Raw FASTQ and BAM files for long reads. Here, DNA libraries were prepared using 1D DNA ligation Sequencing Kit (SQK-LSK109, Oxford Nanopore) and single-end sequenced on a MinION device (Oxford Nanopore).

  Dataset EGAD00001006916

• Low Coverage Whole Genome Sequencing from high grade osteosarcoma

  This dataset contains the WGS of 35 samples (high grade osteosarcoma). All cases were reviewed by an expert bone pathologist and have a tumour content of 50% minimum. Paired-end libraries from fresh frozen tumour samples were prepared using the Agilent SureSelectXT HumanV5 kit for whole-genome sequencing (WGS). These were sequenced together with a tumour complementary DNA on an Illumina HiSeq2500 (paired-end 100 bp). Sequencing reads were mapped to the GRCh37 human reference genome using HISAT2

  Dataset EGAD00001006540

• Whole genome sequence of monozygotic twins

  We generated whole genome sequence data from a family of monozygotic twins. DNA was obtained from blood, buccal epithelial cells, placenta, and umbilical cords of monozygotic twins. DNA from the parents were also obtained. Libraries were generated using Truseq-PCR free, Truseq nano, and NEBnext Ultra II depending on the availability of DNA. Data was generated on Illumina NovaSeq platform. Raw sequence data was aligned to the human reference genome GRCh38 using bwa mem aligner.

  Dataset EGAD00001008677

• shallow WGS of cell free DNA

  12.5 ng of cfDNA was used as input for shallow whole-genome sequencing (sWGS), aiming for a coverage of x0.2-0.4-fold. Library preparation was performed using the TruSeq Nano DNA High Throughput Library Prep Kit (Illumina, San Diego, CA, USA) on an automated Hamilton STAR liquid handling system (Hamilton, Germany GmbH, Robotics, Gräfeling, Germany) with dual indexing, and sequencing was performed on the NextSeq500/550 platform (Illumina). The fraction of tumor-derived DNA in cell-free DNA was estimated using the R package ichorCNA.

  Dataset EGAD00001009796