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• Normal sample for patient SA672

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA672

  Dataset EGAD00001009257

• Tumour sample for patient SA1035

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1035

  Dataset EGAD00001009258

• Tumour sample for patient SA994

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009259

• Tumour sample for patient SA604

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009260

• Tumour sample for patient SA605

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009261

• Tumour sample for patient SA609

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009262

• ATAC-Seq of GM adipose tissue samples

  This dataset contains 28 fastq files (11 files for subcutaneous tissue and 17 files for visceral tissue) from nine individuals. All samples were initially sequenced by SEx50 mode (16 files) and some of them were also sequenced by PEx100 mode (12 files). Sequencing was done on the Illumina HiSeq2000 platform.

  Dataset EGAD00001010254

• RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

  Transcriptome profiling of 121 high-risk paediatric cancer samples for identifying T-cell infiltration signatures using poly-A capture by Truseq and sequenced on NextSeq 500

  Dataset EGAD00001010920

• Dataset for transcriptome and whole genome bisulfite sequencing of glioblastoma(GBM) tumor cells

  This dataset contains transcriptome and whole genome bisulfite sequencing for 40 samples with glioblastoma(GBM). The sequencing was performed on Illumina NextSeq 2000 and Illumina NextSeq 550, . The sequencing was always paired.

  Dataset EGAD00001015614

• Associate Vice President for Research Administration & Director

  Dac EGAC00001003114

• DAC for Duke-NUS genomic sequencing projects

  Dac EGAC00001000256

• DAC for Whole Genome Sequencing of ASD quartet families

  Dac EGAC00001000288

• The Shlien Lab, The Hospital for Sick Children

  Dac EGAC00001000289

• DAC for IGMM / Usher Institute, University of Edinburgh

  Dac EGAC00001000365

• DAC for esophageal squamous cell carcinoma genomic data

  Dac EGAC00001000444

• MacFeeters Hamilton Centre for Neuro-Oncology Research (MHC-NOR)

  Dac EGAC00001000510

• DAC for Dave Lab, Duke University

  Dac EGAC00001000538

• DAC for Single Cell Transcriptomics of Colorectal Cancer

  Dac EGAC00001000550

• Data access committee for BAMS exome sequencing data.

  Dac EGAC00001000583

• DAC for DCM-cases (149 human DCM samples)

  Dac EGAC00001000673

• DAC for DCM-controls (113 human non-DCM samples)

  Dac EGAC00001000674

• DAC for AA HCC patient from National Taiwan University

  Dac EGAC00001000708

• Institut Jules Bordet Data Access Committee for EGAS00001002685 study

  Dac EGAC00001000748

• Data Access Committee for Tumor Progression Study at Karolinska Institute

  Dac EGAC00001000778

• DAC for Institute of Human Genetics, Bonn, AGA Project

  Dac EGAC00001000831

• DAC for Genomic landscpae of Chordoid Glioma study

  Dac EGAC00001000906

• Data Access Committee for the Medulloblastoma Host Genome Study

  Dac EGAC00001000910

• Data Access Committee for Niigata-NIG collaborative work

  Dac EGAC00001000955

• DAC for Peritoneal Mesothelioma sequencing study (LAGA-VPC)

  Dac EGAC00001001093

• ASAN Center for Cancer Genome Discovery Data Access Commitee

  Dac EGAC00001001285

• ASAN Center for Cancer Genome Discovery Data Access Commitee

  Dac EGAC00001001391

• DAC for RNA-Seq Transcriptomic CD4+ T data from EGAS00001004152

  Dac EGAC00001001462

• The data usage policy for epigenomic profile of diverse cancer

  Dac EGAC00001001540

• Englander Institute for Precision Medicine - WCM - Yale - Duke DAC

  Dac EGAC00001001777

• Single cell multi-omics (scNOVA) group for CLL)

  Dac EGAC00001001880

• DAC for Smart-seq2 kidney glomerular single cell project

  Dac EGAC00001001895

• DAC for de-methylation of the FOXP3-TSDR study

  Dac EGAC00001001902

• Data Access Committee for Niigata-NIG collaborative work

  Dac EGAC00001002429

• Data Access Committee for Niigata-NIG collaborative work

  Dac EGAC00001002469

• DAC for tFL with PMBL GE signature Exome samples

  Dac EGAC00001002474

• Center for International Blood and Marrow Transplant Research (CIBMTR)

  Dac EGAC00001002536

• Single cell multi-omics (scNOVA) group for Skin fibroblast

  Dac EGAC00001002833

• AmsterdamUMC Data Access Committee for the EPIC-CD study

  Dac EGAC00001003480

• Data Access Committee for Triple Negative Breast Cancer dataset

  Dac EGAC00001003584

• The EMC-HEMA-SCN DAC for severe congenital neutropenia data

  Dac EGAC00001003590

• Genomic and transcriptome analysis for intrahepatic cholangiocarcinoma

  Study EGAS00001006007

• Test GWAS Data for Training and Computational Benchmarking

  Study EGAS00001007914

• Altered Interactions between Circulating and Tissue-Resident CD8 T Cells with the Colonic Mucosa Define Checkpoint Inhibitor Colitis

  Therapeutic immune checkpoint blockade has revolutionized oncology, but treatments are limited by immune-related adverse events, including checkpoint inhibitor colitis (irColitis). To define molecular drivers of irColitis, we profiled ~300,000 cells from the colonic mucosa and blood of 29 patients and controls. Patients with irColitis showed expanded mucosal Tregs (regulatory T cells), CD8 tissue-resident memory T cells expressing CXCL13 and Th17 gene programs, and recirculating ITGB2 CD8 T cells. Cytotoxic CD4 T cells, recirculating CD8 T cells, and endothelial cells expressing hypoxia gene programs were further expanded in colitis associated with anti-PD-1/CTLA-4 compared to in the presence of anti-PD-1 therapy. Luminal epithelial cells in irColitis patients expressed PCSK9, PD-L1, and interferon-induced signatures associated with apoptosis, increased cell turnover, and malabsorption.Raw data files from both colon and PBMCs from irColitis patients and controls have been provided to dbGaP. Count matrices derived from raw RNA-Seq data will be available through GEO accession GSE206301.

  Study phs003418

• Longitudinal Immune Dynamics of Mild COVID-19 Define Signatures of Recovery and Persistence

  SARS-CoV-2 has infected over 160 million and caused more than 3 million deaths to date. Most individuals (>80%) have mild symptoms and recover in the outpatient setting, but detailed studies of immune responses have focused primarily on moderate to severe COVID-19. We deeply profiled the longitudinal immune response in individuals with mild COVID beginning with early time points post-infection (1-15 days) and proceeding through convalescence to >100 days after symptom onset. We correlated data from single cell analyses of peripheral blood cells, serum proteomics, virus-specific cellular and humoral immune responses, and clinical metadata. Acute infection was characterized by vigorous coordinated innate and adaptive activation, including an early cellular and proteomic signature that correlated with the amplitude of virus-specific humoral responses after day 30. We characterized signals associated with recovery and convalescence to define a new signature of inflammatory cytokines, gene expression, and chromatin accessibility that persists in individuals with post-acute sequelae of SARS-CoV-2 infection (PASC).

  Study phs002576

• Chromatin accessibility in stem cells unveils progressive transcriptional reprogramming in myelodysplastic syndrome

  Myelodysplastic syndromes (MDS) originate from hematopoietic stem cell (HSC) clones with acquired gene mutations/deletions. However, our knowledge on the epigenetic properties of MDS stem cells are limited. Here, we examined chromatin accessibility of MDS stem cells. Chromatin accessibility profiles of MDS stem cells represented the disease status better than progenitor cells and unveiled the process of stem cell reprograming during disease progression. Characterization of differentially accessible regions (DARs) revealed that MDS stem cells acquire progenitor-like chromatin accessibility with disease progression, resulting in imprecise stem-progenitor hierarchy. Profiling of transcriptional factor binding motifs at DARs revealed precocious inactivation and activation of HSC and myeloid transcriptional networks, respectively, in MDS stem cells. Increased chromatin accessibility at CEBP target sites well represented the myeloid reprograming of MDS stem cells and stratified MDS into distinct subgroups. Newly developed "Progenitor scores" based on chromatin accessibility well stratified the disease status and correlated with prognosis. These results highlight that the chromatin properties of MDS stem cells define the cell-autonomous behavior of MDS and disease progression.

  Study JGAS000718