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• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0197_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0197_003A for Follicular lymphoma patient sample TFRI_Cont_12

  Dataset EGAD00001010213

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_002 for Triple negative breast cancer sample SA1035X7XB03502

  Dataset EGAD00001011246

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_001 for Triple negative breast cancer sample SA609X5XB03230

  Dataset EGAD00001011212

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_003 for Follicular lymphoma patient sample TFRI_Cont_4

  Dataset EGAD00001010206

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_003A for Follicular lymphoma patient sample TFRI_Cont_11

  Dataset EGAD00001010212

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_003A for Follicular lymphoma patient sample TFRI_Cont_6

  Dataset EGAD00001010208

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_003A for Follicular lymphoma patient sample TFRI_Cont_8

  Dataset EGAD00001010209

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0183_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0183_003A for Follicular lymphoma patient sample TFRI_Cont_9

  Dataset EGAD00001010210

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0187_003A for Follicular lymphoma patient sample TFRI_Cont_10

  Dataset EGAD00001010211

• Tumor from individual with germline POLD1 L474P

  This dataset includes WXS sequencing for 1 tumor FFPE sample and adjacent normal tissue from the individual from one family member.

  Dataset EGAD00001009281

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_003A for Follicular lymphoma patient sample TFRI_Cont_13

  Dataset EGAD00001010214

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_001 for Follicular lymphoma patient sample TFRIPAIR2_FL

  Dataset EGAD00001010215

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0168_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0168_001 for Follicular lymphoma patient sample TFRIPAIR3_FL

  Dataset EGAD00001010216

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_001A for Follicular lymphoma patient sample TFRI_Pair_6_FL

  Dataset EGAD00001010219

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_001A for Follicular lymphoma patient sample TFRIPAIR11_FL

  Dataset EGAD00001010223

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_001A for Follicular lymphoma patient sample TFRI_Pair_13_FL

  Dataset EGAD00001010225

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_003 for Follicular lymphoma patient sample TFRI_Cont_2

  Dataset EGAD00001010204

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0170_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0170_001A for Follicular lymphoma patient sample TFRIPAIR5_FL

  Dataset EGAD00001010218

• Identifying new diagnostic and treatment pathways for patients with unclassifiable sarcomas

  Whole genome sequencing of tumour normal pairs of human undifferentiated sarcomas.

  Dataset EGAD00001003216

• Colon cancer ctDNA

  Samples prepared using Safe-SeqS technology. All samples ran on an Illumina MiSeq instrument. Fastq files for read 1 and the index read present (R and I respectively).

  Dataset EGAD00001003579

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases [MOSAIC] (2017-05-04)

  This dataset contains all the data available for this study on 2017-05-04.

  Dataset EGAD00001003321

• RNA-Seq Illumina GAII dataset

  RNA-Seq Illumina GAII dataset for the TraIT cell-line use case (added reverse and forward reads).

  Dataset EGAD00001001626

• HIV exome pilot, exome data part 1 GRCh37_53

  exome sequence data for 49 HIV elite long term non-progressors and rapid progressors. Partial dataset (overlap with EGAD00001000087) of raw BAMs mapped to GRCh37_53.

  Dataset EGAD00001000047

• MiSeq-High Coverage

  Seven files of patients 3, 21, 29, 30, 31, 32 and 33 with WGS done on Illumina MiSeq with high coverage. For research purpose and authorised user only.

  Dataset EGAD00001003436

• Exome sequencing of tumor samples

  We utilized exome sequencing for DNA obtained from saliva (germline DNA) and the four spatially separated tumor foci and 3 corresponding lymph node metastases

  Dataset EGAD00001000762

• DNA sequencing for gastric cancer ascites

  Whole-exome sequencing (WES) was performed on a total of 34 PC specimens, with 15 cases having matched gDNAs extracted from blood.

  Dataset EGAD00001004364

• Genome - MBD4-deficient AML

  Genome sequencing at diagnosis and post induction for WEHI-AML-1 and WEHI-AML-2. Whole genome sequencing was performed on an Illumina HiSeq X Ten.

  Dataset EGAD00001003568

• BGI study for primary and metastatic Chinese lung adenocarcinomas

  We sequenced 292 patients who were suffering NSCLC with Whole genome sequencing or Exome sequencing method.

  Dataset EGAD00001001397

• ICGC Benchmark 2 (Medulloblastoma)

  Raw fastq files for sequence data generated at 5 sequencing centers from a Medulloblastoma sample and matching blood normal control.

  Dataset EGAD00001001859

• SCNA-Seq of tumor DNA samples

  Low coverage whole genome sequencing for the identification of somatic copy number alterations (SCNA) and focal amplification mapping of corresponding tumor material

  Dataset EGAD00001002150

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_006

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_006 for Triple negative breast cancer sample SA535X6XB03099

  Dataset EGAD00001011223

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_004 for Triple negative breast cancer sample SA1035X6XB03209

  Dataset EGAD00001011243

• Processed RNAseq data

  This dataset contains log2(TPM + 1) transformed counts for the 823 tumor samples profiled by RNAseq.

  Dataset EGAD00001006618

• Raw FASTq files

  This dataset contains FASTq files for tumors from the 823 patients profiled by RNAseq.

  Dataset EGAD00001006619

• PacBio data of de novo assembly individual EGYPT

  This is the PacBio long read data used for performing de novo assembly of the EGYPT individual (mapped against GRCh38).

  Dataset EGAD00001006034

• RNAseq for brainstem glioma

  RNAseq on Illumina HiSeq X Ten of brainstem glioma primary tumor sample

  Dataset EGAD00001006094

• Melanoma exome profiling

  This dataset includes whole exome profiling of 65 early-passage metastatic melanoma cell lines. The data are provided as BAM files for tumor and normal samples.

  Dataset EGAD00001006271

• 1_fibroblasts_HD_vs_Ctrl_DMSO_Branaplam

  n=4 Ctrl and n=4 HD fibroblasts lines were treated with DMSO or 10nM Branaplam for 72h and RNA-seq was performed.

  Dataset EGAD00001008807

• Genomic Landscape of Human Skin at a Single-Cell Resolution

  Every cell in the human body has a unique set of somatic mutations, yet it remains difficult to comprehensively genotype an individual cell. Here, we developed solutions to overcome this obstacle in the context of normal human skin, thus offering the first glimpse into the genomic landscapes of melanocytes at single-cell resolution. We comprehensively genotyped 133 melanocytes from 19 sites across 6 donors. As expected, sun-shielded melanocytes had fewer mutations than sun-exposed melanocytes. However, within sun-exposed sites, melanocytes on chronically sun-exposed skin (e.g. the face) displayed a lower mutation burden than melanocytes on intermittently sun-exposed skin (e.g. the back). Melanocytes from donors with a history of skin cancer had higher mutation burdens than melanocytes from donors without skin cancer, implying that the mutation burden of normal skin can be harnessed to measure cumulative sun damage and skin cancer risk. Moreover, melanocytes from healthy skin commonly harbor pathogenic mutations, likely explaining the origins of the melanomas that arise in the absence of a pre-existing nevus. Phylogenetic analyses identified groups of related melanocytes, suggesting that melanocytes spread throughout skin as fields of clonally related cells, invisible to the naked eye. Overall, our study offers an unprecedented view into the genomic landscapes of individual melanocytes, revealing key insights into the causes and origins of melanoma.

  Study phs001979

• Uveal Melanoma Immunogenomics Predict Immunotherapy Resistance and Susceptibility

  Immunotherapy using immune checkpoint inhibition (ICI) has shown success in treating metastatic cutaneous melanoma but has limited efficacy against metastatic uveal melanoma (UM), a rare variant arising from the immune privileged eye. To better understand this immune resistance, we performed comprehensive immunogenomic profiling of 100 human UM metastases using clinicogenomics, bulk and single cell transcriptomics, T cell receptor (TCR) repertoire analysis, and tumor infiltrating lymphocyte (TIL) potency assessment. We found that over half of these metastases harbored TIL with potent autologous tumor specificity, despite having low tumor mutational burden and resistance to prior immunotherapies, including ICI and the bispecific T cell engager tebentafusp. These T cell infiltrated metastases displayed activated antigen presenting cells, chronic interferon signaling, and diverse TCR repertoires. However, we observed strikingly low intratumoral TCR clonality and transcriptionally non-proliferative TIL within the tumor microenvironment even after ICI and tebentafusp therapy, demonstrating that these immunotherapies were insufficient to induce proliferation of the tumor reactive TIL. To harness the therapeutic potential of these quiescent TIL, we developed rapid tumor transcriptomic profiling to enable their selective in vivo identification and ex vivo liberation to counter their growth suppression. We demonstrate that adoptive transfer of these transcriptomic selected TIL can promote tumor immunity in patients with metastatic UM when other immunotherapies are incapable.Data that will be available through dbGaP includes raw bulk tumor transcriptomic sequencing data.

  Study phs003330

• NIH Division of Intramural Research Multiomic Monogenic Disease Study

  This cross-sectional, observational study utilized high-dimensional approaches, including transcriptomic analysis, serum protein profiling, and peripheral blood frequency assessments, to compare 228 adult and pediatric patients diagnosed with 22 monogenic immune-mediated conditions impacting various key immunological pathways, with 42 age- and sex-matched healthy controls. Participants were enrolled in clinical protocols at the National Institutes of Health in Bethesda, Maryland, USA. The primary objectives of the study were twofold: first, to identify disease-specific and shared "pan-disease" signatures using the collected data, and second, to develop a Random Forest classifier integrating these datasets to differentiate between age- and sex-matched healthy individuals and monogenic patients. This classifier produced a metric known as the "Immune Health Metric" (IHM). Subsequent analysis demonstrated that the IHM effectively distinguished healthy subjects from individuals with multiple polygenic autoimmune and inflammatory diseases in independent datasets. Moreover, it exhibited correlations with healthy aging and tracked disease activities and treatment responses across both immunological and non-immunological conditions. Additionally, the IHM accurately predicted age-dependent antibody responses to various vaccines, including influenza, Hepatitis A/B, Yellow Fever, Varicella Zoster, and Meningococcal vaccines.The microarray-based transcriptomic data, aptamer-based serum protein concentrations (Somalogic platform), peripheral blood frequency data, and clinical information will be made accessible through the database of Genotypes and Phenotypes (dbGaP).

  Study phs002732

• Papuan Y chromosome Diversity Panel

  Island Southeast Asia and Oceania host one of the world’s richest assemblages of human phenotypic, linguistic and cultural diversity. Despite this, the region’s male genetic lineages are globally among the last to remain unresolved. We compiled ~9.7 Mb of Y chromosome sequence from a diverse sample of over 380 men from this region, including 152 first reported here. The granularity of this dataset allows us to fully resolve and date the regional Y chromosome phylogeny. This new high-resolution tree confirms two main population bursts: multiple rapid diversifications following the region’s initial settlement ~50 kya, and extensive expansions <6 kya. Notably, ~40-25 kya the deep rooting local lineages of C-M130, M-P256, and S-B254 show almost no further branching events in Island Southeast Asia, New Guinea and Australia, matching a similar pause in diversification seen in maternal mitochondrial DNA lineages. The main local lineages start diversifying ~25 kya, at the time of the Last Glacial Maximum. This improved Y chromosome topology highlights localized events with important historical implications, including pre-Holocene contact between Mainland and Island Southeast Asia, potential interactions between Australia and the Papuan world, and a sustained period of diversification following the flooding of the ancient Sunda and Sahul continents as the insular landscape observed today formed. The high-resolution phylogeny of the Y chromosome presented here thus enables a detailed exploration of

  Study EGAS00001006025

• Identifying genetic consequences of Epstein-Barr Virus transformation by comparing an individual’s genomic DNA with that of its lymphoblastoid cell line.

  A detailed analysis of whole genomes can be now achieved with next generation sequencing. Epstein Barr Virus (EBV) transformation is a widely used strategy in clinical research to obtain an unlimited source of a subject’s DNA. Although the mechanism of transformation and immortalization by EBV is relatively well known at the transcriptional and proteomic level, the genetic consequences of EBV transformation are less well understood. A detailed analysis of the genetic alterations introduced by EBV transformation is highly relevant, as it will inform on the usefulness and limitations of this approach. We used whole genome sequencing to assess the genomic signature of a low-passage lymphoblastoid cell line (LCL). Specifically, we sequenced the full genome (40X) of an individual using DNA purified from fresh whole blood as well as DNA from his LCL. A total of 217.33 Gb of sequence were generated from the cell line and 238.95 Gb from the normal genomic DNA. We determined with high confidence that 99.2% of the genomes were identical, with no reproducible changes in structural variation (chromosomal rearrangements and copy number variations) or insertion/deletion polymorphisms (indels). Our results suggest that, at this level of resolution, the LCL is genetically indistinguishable from its genomic counterpart and therefore their use in clinical research is not likely to introduce a significant bias.

  Study EGAS00001000323

• Targeted Sequencing of GWAS Loci in Cleft Lip and Palate

  Nonsyndromic cleft lip and palate (NSCL/P) is a complex disorder caused by both genetic and environmental factors and has been the focus of an extensive effort to identify genetic risk factors. A number of candidate gene studies have been performed but have not been widely replicated. To date, four independent genome wide association studies have been performed as well as a meta-analysis. Together these studies have identified many loci associated with NSCL/P. The goal of this project is to use targeted sequencing to further characterize these regions and to progress from the association signals identified by GWAS to the identification of causative genes and/or variants. This study is part of the GWASeq project, a collaboration of five disease studies, which will sequence genomic regions from GWAS to characterize the genetic variation underlying these diseases and to compare study design and methods for the follow-up of GWAS studies by sequencing. The goal of this study is to sequence 1000+ NSCL/P case-parent trios from China and the Philippines and 400 trios of European ancestry. Targeted sequencing was performed on intervals ranging between 60kb to 1Mb surrounding 13 genes/loci previously associated with NSCL/P including: IRF6, MAFB, ARHGAP29, 8q24, PAX7, VAX1, NTN1, NOG, FOXE1, MSX1, BMP4, FGFR2, PTCH1.

  Study phs000625

• Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade

  Following the pivotal Phase III IMpower133 study, atezolizumab (anti–PD-L1), combined with carboplatin and etoposide (CE), was the first immune checkpoint inhibitor approved for first-line treatment of extensive-stage small cell lung cancer (ES-SCLC) and is now a standard of care. A clearer understanding of therapeutically relevant SCLC subsets is needed to improve outcomes and to identify rational combination strategies. Transcriptomic analyses and nonnegative matrix factorization were conducted on 271 pre-treatment patient tumor samples from IMpower133 and four subsets with general concordance to previously reported SCLC subtypes were identified. Deeper investigation into the immune heterogeneity relevant to clinical outcomes uncover two subsets with differing neuroendocrine (NE) versus non-neuroendocrine (non-NE) phenotypes that demonstrated hallmarks of immune cell infiltration. The balance of tumor-associated macrophage (TAM) to T-effector signals distinguished these inflamed subsets. Tumors with low TAM but high T-effector signals had a NE phenotype and demonstrated longer overall survival with PD-L1 blockade and CE versus CE alone than did tumors with high TAM and high T-effector signal, which had a non-NE phenotype. The delineation of SCLC immune heterogeneity offers a clinically relevant approach to discriminate SCLC patients likely to benefit most from immunotherapeutic approaches and highlights the complex mechanisms underlying response and resistance to immune checkpoint blockade. 

  Study EGAS50000000138

• EGAD00000000045

  Genomic sequencing and transcriptome shotgun sequencing of a metastatic tumour and its recurrence after drug therapy in a single patient

  Dataset EGAD00000000045

• oacoch-M-1

  Methylation changes in OA patients with chronic exposure to cobalt and chromium

  Dataset EGAD00010001040

• Proteom study primary CRC and LM

  Proteom characterization in primary colorectal cancer and corresponding liver metastasis

  Dataset EGAD00010002237

• CIRdb-CEU1

  Single Nucleotide Polymorphisms in autosomes of Canary Islanders

  Dataset EGAD00010002294

• Knoll et al Identification of drug candidates targeting monocyte reprogramming in people living with HIV

  Bulk RNAseq PBMC

  Dataset EGAD50000000068