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• BLUEPRINT release August 2016, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  ChIP-Seq data for 3 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 19 run(s), 18 experiment(s), 18 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002362

• BLUEPRINT release August 2016, ChIP-Seq for CD3-negative, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 2 CD3-negative, CD4-positive, CD8-positive, double positive thymocyte sample(s). 7 run(s), 5 experiment(s), 5 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002369

• Blueprint: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cell types, (ChIP-Seq for mature neutrophil, on genome GRCh37)

  ChIP-Seq data for 182 mature neutrophil sample(s). 2847 run(s), 366 experiment(s), 355 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002670

• Blueprint: A human variation panel of genetic influences on epigenomes and transcriptomes in three immune cell types, (RNA-Seq for mature neutrophil, on genome GRCh37)

  RNA-Seq data for 205 mature neutrophil sample(s). 205 run(s), 205 experiment(s), 205 analysis(s) on human genome GRCh37. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/blueprint_Epivar/protocols/README_rnaseq_analysis_sanger_20160816

  Dataset EGAD00001002675

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002503

• BLUEPRINT release August 2016, ChIP-Seq for Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma, on genome GRCh38

  ChIP-Seq data for 3 Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma sample(s). 10 run(s), 10 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002506

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD4-positive, alpha-beta T cell sample(s). 8 run(s), 7 experiment(s), 7 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002514

• BLUEPRINT release August 2016, RNA-Seq for CD3-positive, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  RNA-Seq data for 1 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte sample(s). 1 run(s), 1 experiment(s), 1 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002525

• Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

  We applied this signature to a 567-patient GC cohort to establish genomic-based molecular subtypes and then used a support vector machine to build a molecular subtype-based risk-scoring model. Both source code and supplementary datasets for risk score prediction are available at https://github.com/hwanglab/Yonsei_gastric_cancer_32genes.

  Dataset EGAD00001008091

• Normal sample for patient SA495

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009372

• Normal sample for patient SA425

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009371

• Normal sample for patient SA423

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009370

• Normal sample for patient SA300

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009369

• Normal sample for patient SA291

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009368

• Normal sample for patient SA289

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009367

• Normal sample for patient SA286

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009366

• Normal sample for patient SA280

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA280

  Dataset EGAD00001009365

• Normal sample for patient SA239

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009364

• Normal sample for patient SA679

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009379

• Normal sample for patient SA680

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009380

• Normal sample for patient SA681

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009381

• Normal sample for patient SA682

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009382

• Normal sample for patient SA683

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009383

• Normal sample for patient SA678

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009378

• Normal sample for patient SA677

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009377

• Normal sample for patient SA673

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009373

• Normal sample for patient SA676

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009376

• Normal sample for patient SA674

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009374

• Normal sample for patient SA675

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009375

• Resolution of tumour cell populations enhances specificity of treatment options for precision cancer medicine

  This dataset comprises FASTQ files for single-cell RNA-seq and single-cell ATAC-seq data. These data were generated using 10x Genomics' multiome kit. In this cohort, there are 25 samples from 17 human cancer patients.

  Dataset EGAD00001015782

• Characterizing Disease-Causing Variants Using Personal Genomes with Large Recurrent Deletions

  Individuals with recurrent genomic deletions are known to present variable clinical phenotypes, despite the apparent identical range of the genomic segments being affected in their genomes. We propose to study the phenotypic variability and the molecular polymorphism in individuals with the recurrent 17q12 deletions or the recurrent 1q21.1 deletions, with the objective to establish causal relationship between the genotypes and the incompletely penetrant clinical phenotypes. Genome sequencing and phenotypic data from individuals with the deletions and their relatives are included in this submission.

  Study phs002613

• Inherited Corneal Dystrophies - Institute of Ophthalmology, University College London (IoO, UCL)

  DAC created for access to data published by Dr. Alice E Davidson

  Dac EGAC50000000213

• Mechanism of Decitabine response in MDS/AML patients

  Data access agreement for access to scRNASeq data of serial AML/MDS patients undergoing decitabine treatment.

  Dac EGAC50000000550

• Exome sequencing for identifying point mutations driving M haemophilum susceptibility

  To understand non tuberculous Mycobacterial susceptibility

  Study EGAS50000001076

• SMPaeds cfDNA lcWGS

  Low coverage whole genome sequencing (0.5-4X) for cfDNA collected from blood plasma.

  Dataset EGAD50000000782

• BreastCancer_Miroarrays

  Genotyping array data for breast cancer and matched normal mammary gland samples

  Dataset EGAD00010002251

• Genotypes_Agta

  Genotype data for Agta hunter-gatherers Philippines

  Dataset EGAD00010002140

• BLEMD (arrays set)

  Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma (arrays set)

  Dataset EGAD00010001857

• TwinsUK_EpiTwin_DNA_Methylome

  MeDIP-seq RPM chromsome BED files for Peripheral Blood from EPITWIN Project (Columns 4-4353 represent samples)

  Dataset EGAD00010000983

• Dataset for study EGAS00001004946 (Endothelium-derived stromal cells contribute to bone marrow niche formation)

  Dataset EGAD00001006914

• Cellular Dynamics Upon Immune Checkpoint Inhibition

  DAC for "Cellular Dynamics Upon Immune Checkpoint Inhibition" with Prof. Dr. Dr. Jürgen C. Becker (j.becker@dkfz.de)

  Dac EGAC50000000321

• Non-Hodgkin Lymphoma WES Data Access Committee

  DAC for controlled access to whole exome sequencing (WES) data from NHL patients at University of Tabuk

  Dac EGAC50000000998

• Reference epigenome OB56_N_PreA_WGBS data generated from KEP study

  A OB56_N_PreA_WGBS paired end data for Preadipocytes(fat)

  Dataset EGAD00001003479

• Multisample2 Amplicon

  Amplicon data of tumor samples generated for validation of WES findings and further sub clonal mapping

  Dataset EGAD00001004020

• UROMOL 2020 - RNA-seq data for validation

  Total RNAseq data from 47 patients with NMIBC.

  Study EGAS00001005050

• Familial CEBPA-mutated AML whole exome sequencing dataset

  Whole exome sequencing data for AML and matched normal samples

  Dataset EGAD00001000996

• Seminoma exome sequencing

  Exome sequencing data for 8 pairs of seminomas and matched normal

  Dataset EGAD00001001002

• KRAS Mutations in Multiple Myeloma

  Fastq files for 80 Multiple Myeloma Patients and 12 cell-lines

  Dataset EGAD00001005743

• Melanoma multi site metastases

  These are caveman, pindel, battenberg and brass calls for index patients' metastatic melanoma genomes within this study.

  Dataset EGAD00001005483

• IMpower133 subtype assignments

  This dataset contains subtype assignments for 271 tumor samples profiled by RNA-seq.

  Dataset EGAD00001006926