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• WGS and RNA-Seq data from a GBM patient PT-FB6711

  WGS and RNA-Seq data from a GBM patient PT-FB6711

  Dataset EGAD00001004235

• RNA-seq of cultured human hematopoietic stem and progenitor cells from umblical cord blood in cytokine-rich ex vivo culture conditions following sphingolipid modulation

  In order to elucidate the biological pathways altered by sphingolipid modulation with N-(4-hydroxyphenyl) retinamide (4HPR) treatment in human HSPC that may contribute to the restraint in proliferation while promoting persistence of HSC self-renewal, as well as determine the mechanism of synergy in enhancement of HSC self-renewal with CB CD34+ agonists UM171 and StemRegenin 1 (SR1), we performed RNA-sequencing (RNA-Seq) of 3 pools of lin-CB cells following 2 or 4 days with DMSO, 4HPR, UM171+SR1 or 3-Factor (4HPR+UM171+SR1). We identified modulation of sphingolipid metabolism regulates self-renewal through activating coordinated stress pathways that coalesce on endoplasmic reticulum stress and autophagy programs.

  Dataset EGAD00001005140

• Disease specific alterations in the olfactory mucosa of patients with Alzheimer’s disease

  The sample AD_Library_1, AD_Library_2 and Control_Library were run on a Chromium Chip B with the Chromium Single Cell 3′ Library & Gel Bead Kit v3 kit (10x Genomics, CA, USA) . The 3’ gene expression libraries were sequenced at an approximate depth of 50,000 reads per cell using the NovaSeq 6000 S1 (Illumina, San Diego, CA, USA) flow cells. Cell Ranger v.3.0.2 was used to analyze the raw base call files. FASTQ files and raw gene-barcode matrices were generated and aligned human genome GRCh37 (hg19). The samples were integrated in R v.4.0.3 and generated Seurat objects, two related to AD samples and one to control samples, were analyzed using the Seurat package v.4.0.3 to perform downstream analysis, clustering of the cells and differential expression.

  Dataset EGAD00001008560

• WGS of cell-free DNA derived from plasma of CRC patients and healthy controls, and WGS of matched tumor tissue and saliva

  This dataset contains: 1.) Whole-genome sequencing (WGS) data (~6x) of 259 cfDNA samples obtained from 50 colorectal cancer (CRC) patients and 61 healthy controls. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38). 2.) WGS data (~1x) of 50 tumor biopsy and 45 saliva samples from CRC patients. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38).

  Dataset EGAD00001009309

• Whole genome sequencing data of paediatric ETV6-RUNX1 acute lymphoblastic leukemia

  Whole genome sequencing data from paediatric (<18-y) ETV6-RUNX1 fusion positive acute lymphoblastic leukemias. Dataset includes fastq and BAM files from diagnostic and remission (control) samples of 33 patients. Dataset consists of two experiments depending on sequencing instrument; "Experiment 1" sequenced by using Illumina Hiseq X Ten instrument, and "Experiment 2" Illumina Novaseq 6000, respectively.

  Dataset EGAD00001010128

• Transcriptomic analyses of a large cohort of adult B cell acute lyphoblastic leukemia

  Total mononuclear cells (MNC) were isolated from peripheral blood (PB) or bone marrow (BM) samples of 105 Ph-/-/- (B-cell acute lymphoblastic leukemia [B-ALL] triple negative) and 31 Ph+ B-ALL adult patients using Lymphosep (Biowest, Nuaillé, France). A total of 15 samples from healthy subjects were processed including hematopoietic stem-progenitor cells (CD34+) from bone marrow specimens (n = 3), and bone marrow mononuclear cell samples (n = 3) from STEMCELL Technologies (Vancouver, Canada). PB MNC samples (n=5) and cord blood samples (n = 4). CD34+ cells were enriched from cord blood samples by immunomagnetic separation (CD34 MicroBead Kit, Miltenyi Biotec, Bergisch Gladbach, Germany).Libraries were prepared using the TruSight RNA Pan-Cancer Panel Kit (Illumina, San Diego, California, USA), following the manufacturer’s protocol. Sequencing was performed using the Illumina MiSeq instrument.

  Dataset EGAD00001010837

• Genome and transcriptome sequence data from a metastatic breast ductal carcinoma patient

  Genome and transcriptome sequence data from a metastatic breast ductal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011021

• Genome and transcriptome sequence data from a melanoma patient

  Genome and transcriptome sequence data from a melanoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011022

• Genome and transcriptome sequence data from a follicular lymphoma patient

  Genome and transcriptome sequence data from a follicular lymphoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011023

• Genome and transcriptome sequence data from a neuroendocrine tumor patient

  Genome and transcriptome sequence data from a neuroendocrine tumor patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011024

• Genome and transcriptome sequence data from a thymic cancer patient

  Genome and transcriptome sequence data from a thymic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011025

• Genome and transcriptome sequence data from a small cell lung carcinoma patient

  Genome and transcriptome sequence data from a small cell lung carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011026

• Genome and transcriptome sequence data from a pancreatic cancer patient

  Genome and transcriptome sequence data from a pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011027

• Genome and transcriptome sequence data from a nasopharyngeal carcinoma patient

  Genome and transcriptome sequence data from a nasopharyngeal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011028

• Genome and transcriptome sequence data from a colorectal carcinoma patient

  Genome and transcriptome sequence data from a colorectal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011029

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011030

• Genome and transcriptome sequence data from a urachal adenocarcinoma patient

  Genome and transcriptome sequence data from a urachal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011031

• Genome and transcriptome sequence data from a liposarcoma patient

  Genome and transcriptome sequence data from a liposarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011032

• Genome and transcriptome sequence data from a gastrointestinal stromal tumor patient

  Genome and transcriptome sequence data from a gastrointestinal stromal tumor patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011033

• Genome and transcriptome sequence data from a granulosa cell ovarian tumor patient

  Genome and transcriptome sequence data from a granulosa cell ovarian tumor patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011034

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011035

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011036

• Genome and transcriptome sequence data from a papillary thyroid carcinoma patient

  Genome and transcriptome sequence data from a papillary thyroid carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011038

• Genome and transcriptome sequence data from a thymoma patient

  Genome and transcriptome sequence data from a thymoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011039

• Genome and transcriptome sequence data from a rectal adenocarcinoma patient

  Genome and transcriptome sequence data from a rectal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001011040

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - structural variation vcf files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - structural variation vcf files (Mutographs)

  Dataset EGAD00001015389

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - copy number variants (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - copy number variants (Mutographs)

  Dataset EGAD00001015390

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - sequence data (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - sequence data (Mutographs)

  Dataset EGAD00001015386

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - patient metadata files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - patient metadata files (Mutographs)

  Dataset EGAD00001015387

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - filtered vcf files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - filtered vcf files (Mutographs)

  Dataset EGAD00001015388

• Bulk RNA-seq dataset of Single cell RNA sequencing reveals induced bifurcated differentiation after RUNX1::RUNX1T1 depletion in patient-derived leukemic blasts

  In this study, we demonstrate that primary AML cells harboring the chromosomal translocation t(8;21) are critically dependent on the corresponding fusion gene, RUNX1::RUNX1T1, to suppress differentiation and maintain stemness. Silencing RUNX1::RUNX1T1 expression using siRNA-loaded lipid nanoparticles induces significant changes in chromatin accessibility, redirecting the leukemia-associated transcriptional network towards a myeloid differentiation program. Single-cell analyses reveal that this transcriptional reprogramming is associated with the depletion of immature stem and progenitor-like cell populations, accompanied by an expansion of granulocytic and eosinophilic/mast cell-like populations with impaired self-renewal capacity.

  Dataset EGAD00001015508

• Center for Common Disease Genomics [CCDG] - Cardiovascular: TexGen

  Cardiovascular disease (CVD), especially coronary heart disease, heart failure and cerebrovascular disease remain the leading causes of death in men and women across all race groups in the United States. Substantial evidence exists for genetic factors underlying CVD risk, and their discovery offers an opportunity to enhance understanding of disease mechanisms, to provide specific diagnostic and prognostic indicators, and to identify novel therapeutic targets. The Centers for Common Disease Genomics (CCDG) are a large-scale genome sequencing effort undertaken as a collaboration to identify rare risk and protective alleles for common chronic diseases, with a current focus on early onset heart disease, hemorrhagic stroke, and autism. In this study, whole genome sequencing (WGS) was performed in 6,146 individuals with any personal or family history of CVD from the TexGen study who were recruited from several institutions in the Texas Medical Center located in Houston, Texas. The participants included patients who were admitted with a variety of diagnoses including acute coronary syndromes, strokes, and transient ischemic attacks, or who underwent percutaneous coronary interventions or coronary artery bypass grafting (CABG) (Virani SS et al. Am J Cardiology 107:1504-1509, 2011). Patients in the TexGen cohort with a confirmed diagnosis of acute coronary syndrome (ACS) or CABG when discharged from the hospital between 2001-2008 were eligible for a follow-up study of recurrent myocardial infarction (Virani SS et al. Circulation J 76:950-956, 2012), while those presenting with ACS beginning in 2007 were recruited for an investigation of the role of depressive symptoms in the risk of major adverse coronary events (Sanner J et al. Biol Res Nurs 20:168-176, 2018). In addition, the initial criterion for enrollment in a third TexGen sub-study undertaken between 2007 and 2013 was the occurrence of premature acute myocardial infarction in men ≤ 50 years and women ≤ 55 years in order to establish a blood sample collection resource with the intention of localizing genes that contribute to disease risk. This definition was later expanded to include individuals with a clinical history of premature coronary heart disease such as CABG or percutaneous coronary interventions.

  Study phs003010

• Automated machine-learning approach for next generation profiling of sequence alterations, mutation burden, microsatellite instability, and structural variants in human cancers

  Sequence and structural alterations together with tumor mutation burden (TMB) and microsatellite instability (MSI) have been identified as biomarkers for the determination of response to targeted and immune checkpoint inhibitor therapies. However, widespread clinical adoption of these biomarkers has historically been limited due to barriers such as evidence of clinical utility and reimbursement. We have developed 2.2 Mb targeted NGS system and an automated machine-learning analysis approach (PGDx elio™ tissue complete, ETC) that has been FDA cleared for examination of 500+ cancer-related genes and 68 mononucleotide repeats for identification of sequence and structural alterations, TMB, and MSI in solid cancers in a clinical setting. We designed and trained this approach using sequence data from 4,174 cancers and >124,000 in silico alterations and evaluated the methodology in >2,550 tumor or non-cancerous normal samples. Independent analyses of ETC sequence changes in 440 formalin fixed paraffin embedded (FFPE) tumor or cell line samples using MSK-IMPACT™, FoundationOne®, and ddPCR revealed a positive percent agreement (PPA) >97% with high sensitivity as low as 3% mutant allele fraction. We observed high concordance between panel-wide and whole-exome TMB for 307 pan-cancer FFPE tumors (Pearson r=0.95, p < 0.0001) using samples with ≥20% tumor cellularity. Comparison of the mutation context and repeat-based MSI approach in ETC with a multiplex MSI PCR assay in 223 samples revealed a PPA of 99% and negative predictive agreement (NPA) >99%. We confirmed the accuracy and precision of TMB and MSI measurements across three independent laboratories (CV of <5% and average PPA >99%, respectively). Finally, evaluation of amplifications and translocations against DNA and RNA-based approaches exhibited >98% NPA and PPA of 86% and 82% respectively. These results demonstrate high analytical performance for determination of sequence and structural changes, TMB, and MSI using a targeted NGS panel and provide a scalable and facile approach for evaluating these biomarkers in a clinical laboratory.

  Study EGAS00001005556

• Professors Colin Palmer and Ewan Pearson, from University of Dundee, are both members of the Access Group panel for the GoDARTS bioresource, which provided the data for the current study. The current Study used genotypes (EGAD00010000282) generated by the WTCCC2 (EGAS00000000121), on which both Colin and Ewan are also DAC panel members.

  Dac EGAC00001000504

• DAC for "Establishment and characterization of circulating tumor cells-derived organoids from metastatic breast cancer patients."

  This is the DAC for the study "Establishment and characterization of circulating tumor cells-derived organoids from metastatic breast cancer patients." of Andreas Trumpp (a.trumpp@dkfz.de), Roberto Würth (r.wuerth@dkfz.de) and Martin Sprick (Martin.Sprick@hi-stem.de).

  Dac EGAC50000000371

• NGS-based targeted exome sequencing of osteosarcoma

  The present study aimed to identify potential genomic markers for the prediction of chemosensitivity in patients with OS using a genomic approach.

  Study JGAS000282

• Clinical and neuroimaging study on preclinical Alzheimer's disease.

  This clinical study aims to discriminate MCI individuals at risk for development of Alzheimer dementia, as well as preclinical AD without clinical manifestations.

  Study JGAS000272

• Zimbabwe Mendelian Disorders Genomics DAC

  Data Access Committee responsible for reviewing and approving access requests to human genomic data generated from individuals with suspected Mendelian disorders. The DAC ensures ethical use of genomic data in accordance with institutional, national, and international guidelines, protecting participant privacy while enabling responsible data sharing for research purposes.

  Dac EGAC50000000963

• WGS of ecDNA neuroblastoma cell lines

  ecDNA reconstruction for MYCN-amplified neuroblastoma cell lines using shallow long-read whole-genome sequencing (MinION platform, WGS mean-coverage 5-10X).

  Study EGAS50000000349

• scRNA-seq for 4 reactive lymph node and 12 high grade B cell lymphoma samples

  Dataset includes count matrices, barcode tsvs, and features tsv for each sample, generated using Cell Ranger.

  Dataset EGAD50000001386

• miRNA

  miRNA Sequencing of olfactory mucosa (OM) cells derived from cognitively healthy and individuals with AD exposed to traffic-related ultrafine particles (UFPs) for 72h in submerged cultures. The UFPs used for exposures were: A0 and A20. Exposures were compared to the corresponding blank samples.

  Dataset EGAD50000001532

• Dataset for "HPV integration induces gene fusions" (pacBio)

  pacBio whole-genome sequencing data for "HPV integration induces gene fusions" We performed long read whole-genome sequencing on four HPV+ head and neck cancer samples using pacBio HiFi. The sequence reads were submitted in fastq format.

  Dataset EGAD50000001304

• WGS dataset for two NUP98-Rearranged Acute Myeloid Leukemia PDX samples

  This dataset includes whole genome sequencing of two patient-derived xenograft (PDX) samples with NUP98-Rearranged Acute Myeloid Leukemia. Bam files are provided for each sample.

  Dataset EGAD50000001563

• Dataset for WGS and RNA melanoma samples

  This dataset contains WGS and RNA sequencing data for 4 melanoma samples. Sequencing was performed on Illumina Novaseq 6000 and Illumina HiSeq X. The sequencing was always paired.

  Dataset EGAD50000000093

• Exome_sequencing_of_Non_syndromic_Congenital_Heart_Defects

  Exome sequencing of ~1,800 patients with non-syndromic Congenical Heart Defects (CHD) to identify genes enriched for damaging rare variants that increase risk of CHD

  Study EGAS00001002522

• WTCCC2 Bacteraemia Susceptibility (BS) samples

  A WTCCC2 project genome-wide association study for bacteraemia susceptibility in 4924 individuals from Kenya, genotyped on the Affymetrix 6.0 array.

  Study EGAS00001001756

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

  Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS). The dataset includes 21 samples from 7 families with BAMS; see Gordon et al, Nature Genetics, 2017.

  Dataset EGAD00001003130

• Transcriptomic profiles of neuroblastoma PDXs and primary tumors

  This dataset contains bam files for RNA-seq experiments for 6 neuroblastoma PDXs (Patient Derived Xenograft) and 3 pairs of neuroblastoma tumors at diagnosis and at relapse.

  Dataset EGAD00001003393

• Whole Genome Sequencing for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Whole Genome Sequencing for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Dataset EGAD00001003435

• Exome and Transcriptome for pan-cancer gene-drug analysis

  Total of 584 tumor specimens and/or patient-derived cells across 14 cancer types were subjected for whole-exome/targeted-exome and/or whole-transcriptome sequencing.

  Dataset EGAD00001003441