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• BLUEPRINT release August 2015, RNA-Seq for CD38-negative naive B cell, on genome GRCh38

  RNA-Seq data for 2 CD38-negative naive B cell sample(s). 2 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001500

• BLUEPRINT release August 2016, ChIP-Seq for adult endothelial progenitor cell, on genome GRCh38

  ChIP-Seq data for 2 adult endothelial progenitor cell sample(s). 16 run(s), 14 experiment(s), 14 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 EGAD00001002487

• BLUEPRINT release August 2015, ChIP-Seq for germinal center B cell, on genome GRCh38

  ChIP-Seq data for 2 germinal center B cell sample(s). 12 run(s), 11 experiment(s), 11 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001502

• BLUEPRINT release August 2015, ChIP-Seq for mature neutrophil, on genome GRCh38

  ChIP-Seq data for 9 mature neutrophil sample(s). 48 run(s), 45 experiment(s), 45 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001508

• BLUEPRINT release January 2015, ChIP-Seq for central memory CD4-positive, alpha-beta T cell

  ChIP-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 6 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001144

• BLUEPRINT release August 2015, RNA-Seq for regulatory T cell, on genome GRCh38

  RNA-Seq data for 1 regulatory T cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001546

• BLUEPRINT release January 2015, RNA-Seq for CD8-positive, alpha-beta T cell

  RNA-Seq data for 2 CD8-positive, alpha-beta T cell sample(s). 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001137

• BLUEPRINT release January 2015, ChIP-Seq for CD14-positive, CD16-negative classical monocyte

  ChIP-Seq data for 10 CD14-positive, CD16-negative classical monocyte sample(s). 73 run(s), 69 experiment(s), 69 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001179

• BLUEPRINT release August 2016, ChIP-Seq for endothelial cell of umbilical vein (proliferating), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 14 run(s), 14 experiment(s), 14 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 EGAD00001002424

• BLUEPRINT release January 2015, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001182

• BLUEPRINT release August 2015, ChIP-Seq for CD8-positive, alpha-beta thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD8-positive, alpha-beta thymocyte sample(s). 2 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001568

• BLUEPRINT release August 2015, RNA-Seq for CD8-positive, alpha-beta thymocyte, on genome GRCh38

  RNA-Seq data for 1 CD8-positive, alpha-beta thymocyte sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001478

• BLUEPRINT release January 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell sample(s). 10 run(s), 10 experiment(s), 10 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001127

• BLUEPRINT release January 2015, RNA-Seq for endothelial cell of umbilical vein (resting)

  RNA-Seq data for 1 endothelial cell of umbilical vein (resting) sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001142

• BLUEPRINT release August 2016, RNA-Seq for CD8-positive, alpha-beta thymocyte, on genome GRCh38

  RNA-Seq data for 1 CD8-positive, alpha-beta 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 EGAD00001002289

• BLUEPRINT release August 2016, ChIP-Seq for alternatively activated macrophage, on genome GRCh38

  ChIP-Seq data for 7 alternatively activated macrophage sample(s). 50 run(s), 49 experiment(s), 49 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 EGAD00001002317

• BLUEPRINT release August 2016, RNA-Seq for mature neutrophil, on genome GRCh38

  RNA-Seq data for 14 mature neutrophil sample(s). 14 run(s), 14 experiment(s), 13 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 EGAD00001002409

• BLUEPRINT release August 2016, RNA-Seq for CD4-positive, alpha-beta thymocyte, on genome GRCh38

  RNA-Seq data for 1 CD4-positive, alpha-beta 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 EGAD00001002299

• BLUEPRINT release August 2014, ChIP-Seq for CD8-positive, alpha-beta T cell

  ChIP-Seq data for 2 CD8-positive, alpha-beta T cell sample(s). 13 run(s), 13 experiment(s), 13 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000936

• BLUEPRINT release January 2015, RNA-Seq for effector memory CD8-positive, alpha-beta T cell

  RNA-Seq data for 1 effector memory CD8-positive, alpha-beta T cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001153

• BLUEPRINT release January 2015, ChIP-Seq for CD8-positive, alpha-beta T cell

  ChIP-Seq data for 5 CD8-positive, alpha-beta T cell sample(s). 28 run(s), 28 experiment(s), 28 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001154

• BLUEPRINT release August 2016, RNA-Seq for alternatively activated macrophage, on genome GRCh38

  RNA-Seq data for 7 alternatively activated macrophage sample(s). 9 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_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002387

• BLUEPRINT release August 2015, RNA-Seq for mature neutrophil, on genome GRCh38

  RNA-Seq data for 14 mature neutrophil sample(s). 14 run(s), 14 experiment(s), 14 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001474

• BLUEPRINT release August 2015, ChIP-Seq for endothelial cell of umbilical vein (proliferating), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 12 run(s), 12 experiment(s), 12 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001487

• BLUEPRINT release August 2015, RNA-Seq for CD8-positive, alpha-beta T cell, on genome GRCh38

  RNA-Seq data for 2 CD8-positive, alpha-beta T cell sample(s). 2 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001488

• BLUEPRINT release August 2015, ChIP-Seq for band form neutrophil, on genome GRCh38

  ChIP-Seq data for 4 band form neutrophil sample(s). 18 run(s), 17 experiment(s), 17 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001511

• BLUEPRINT release August 2015, ChIP-Seq for alternatively activated macrophage, on genome GRCh38

  ChIP-Seq data for 4 alternatively activated macrophage sample(s). 22 run(s), 22 experiment(s), 22 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001514

• BLUEPRINT release August 2015, RNA-Seq for conventional dendritic cell, on genome GRCh38

  RNA-Seq data for 1 conventional dendritic cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001540

• BLUEPRINT release August 2015, RNA-Seq for macrophage, on genome GRCh38

  RNA-Seq data for 18 macrophage sample(s). 19 run(s), 18 experiment(s), 18 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001582

• BLUEPRINT release August 2015, ChIP-Seq for inflammatory macrophage, on genome GRCh38

  ChIP-Seq data for 7 inflammatory macrophage sample(s). 36 run(s), 36 experiment(s), 36 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001589

• BLUEPRINT release August 2015, ChIP-Seq for CD4-positive, alpha-beta thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD4-positive, alpha-beta thymocyte sample(s). 2 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001584

• BLUEPRINT release August 2015, RNA-Seq for alternatively activated macrophage, on genome GRCh38

  RNA-Seq data for 4 alternatively activated macrophage sample(s). 6 run(s), 4 experiment(s), 4 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001586

• BLUEPRINT release January 2015, RNA-Seq for cytotoxic CD56-dim natural killer cell

  RNA-Seq data for 3 cytotoxic CD56-dim natural killer cell sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001159

• High-throughput sequencing data of immunoglobulin gene rearrangements in acute lymphoblastic leukaemia - summary .fastq and .bcl files

  Immunoglobulin heavy chain gene high throughput sequencing of paediatric acute lymphoblastic leukaemia samples, for the purpose of MRD on the Illumina MiSeq platform. This dataset contains summary fastq files and raw bcl files from the MiSeq for this study. In the study we identify errors associated with multiplexing that could potentially impact on the accuracy of MRD analysis. We optimise a strategy combining high purity, sequence-optimised oligonucleotides, dual-indexing and an error-aware demultiplexing approach to minimise errors and maximise sensitivity.

  Dataset EGAD00001001983

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - Attached_T=1hr, on genome GRCh38

  ChIP-Seq data for 3 monocyte - Attached_T=1hr 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 EGAD00001002415

• ENU-LS-411N-TripleTherapy

  BRAF V600E colorectal cancers do not respond to the only currently FDA approved targeted therapy for CRC. There is currently a trial underway in the UK recruiting V600E CRC patients for treatment with a triple therapy combination of Cetuximab, Trametinib and Dabrafenib. We have mutagenized a pool of V600E CRC cell lines and treated with this triple therapy to select out drug resistant clones. We will now sequence these drug resistant clones with the aim of identifying common point mutations engendering resistance to this new therapy.

  Dataset EGAD00001002051

• Myeloproliferative Disease Whole Genomes

  Wholegenome libraries will be prepared from at least two serial samples reflecting different stages of disease progression and matched constitutional DNA for 30 Myeloproliferative Disease samples. Five lanes of Illumina HiSeq sequencing will be performed on each of the tumour samples and four lanes for each of the constitutional DNA. Sequencing data will mapped to build 37 of the human reference genome and analysis will be performed to characterize the spectrum of somatic variation present in these samples including single base pair mutations, insertions, deletions as well as larger structural variants and genomic rearrangements.

  Dataset EGAD00001000385

• Myelodysplastic syndrome whole genomes

  Wholegenome libraries will be prepared from at least two serial samples reflecting different stages of disease progression and matched constitutional DNA for 30 Myelodysplastic syndrome patient samples. Five lanes of Illumina HiSeq sequencing will be performed on each of the tumour samples and four lanes for each of the constitutional DNA. Sequencing data will mapped to build 37 of the human reference genome and analysis will be performed to characterize the spectrum of somatic variation present in these samples including single base pair mutations, insertions, deletions as well as larger structural variants and genomic rearrangements.

  Dataset EGAD00001000386

• BLUEPRINT release August 2014, RNA-Seq for CD8-positive, alpha-beta T cell

  RNA-Seq data for 1 CD8-positive, alpha-beta T cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000912

• BLUEPRINT release January 2015, ChIP-Seq for endothelial cell of umbilical vein (proliferating)

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 13 run(s), 13 experiment(s), 13 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001136

• BLUEPRINT release January 2015, RNA-Seq for CD14-positive, CD16-negative classical monocyte

  RNA-Seq data for 8 CD14-positive, CD16-negative classical monocyte sample(s). 8 run(s), 8 experiment(s), 8 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001148

• BLUEPRINT release January 2015, ChIP-Seq for cytotoxic CD56-dim natural killer cell

  ChIP-Seq data for 4 cytotoxic CD56-dim natural killer cell sample(s). 16 run(s), 16 experiment(s), 16 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001158

• BLUEPRINT release January 2015, RNA-Seq for effector memory CD4-positive, alpha-beta T cell

  RNA-Seq data for 1 effector memory CD4-positive, alpha-beta T cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001163

• BLUEPRINT release January 2015, RNA-Seq for central memory CD4-positive, alpha-beta T cell

  RNA-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001172

• BLUEPRINT release January 2015, ChIP-Seq for CD38-negative naive B cell

  ChIP-Seq data for 4 CD38-negative naive B cell sample(s). 14 run(s), 14 experiment(s), 14 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001207

• BLUEPRINT release August 2015, ChIP-Seq for naive B cell, on genome GRCh38

  ChIP-Seq data for 6 naive B cell sample(s). 34 run(s), 28 experiment(s), 28 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001519

• BLUEPRINT release August 2015, RNA-Seq for class switched memory B cell, on genome GRCh38

  RNA-Seq data for 1 class switched memory B cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001531

• BLUEPRINT release August 2015, RNA-Seq for memory B cell, on genome GRCh38

  RNA-Seq data for 1 memory B cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001542

• BLUEPRINT release August 2015, ChIP-Seq for adult endothelial progenitor cell, on genome GRCh38

  ChIP-Seq data for 1 adult endothelial progenitor cell sample(s). 8 run(s), 7 experiment(s), 7 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001554

• BLUEPRINT release August 2015, ChIP-Seq for macrophage, on genome GRCh38

  ChIP-Seq data for 12 macrophage sample(s). 49 run(s), 49 experiment(s), 49 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001576

• BLUEPRINT release August 2015, ChIP-Seq for segmented neutrophil of bone marrow, on genome GRCh38

  ChIP-Seq data for 4 segmented neutrophil of bone marrow sample(s). 20 run(s), 19 experiment(s), 19 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001588

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - T=10day_RANK_M-CSF, on genome GRCh38

  ChIP-Seq data for 1 monocyte - T=10day_RANK_M-CSF sample(s). 2 run(s), 2 experiment(s), 2 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 EGAD00001002420

• BLUEPRINT release August 2016, ChIP-Seq for naive B cell, on genome GRCh38

  ChIP-Seq data for 15 naive B cell sample(s). 67 run(s), 59 experiment(s), 59 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 EGAD00001002466

• BLUEPRINT release August 2016, ChIP-Seq for endothelial cell of umbilical vein (resting), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) sample(s). 13 run(s), 13 experiment(s), 13 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 EGAD00001002488

• BLUEPRINT release August 2016, RNA-Seq for colony forming unit erythroid, on genome GRCh38

  RNA-Seq data for 1 colony forming unit erythroid 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 EGAD00001002509

• RNA sequencing of human intra- and extracranial endothelial cells

  This data set contains the raw .fastq files from one RNA-sequencing experiment. Endothelial cells of the basilar artery and endothelial cells of the carotid artery were post-mortem derived with laser microdissection and sequenced. For this analysis both the basilar- and the carotid artery endothelial cells of eleven individuals were sequenced. For more details please see: DMA Hermkens et al. "Profiling the Unique Protective Properties of Intracranial Arterial Endothelial Cells" Acta Neuropathol Commun. 2019 Oct 14; PMID: 31610812.

  Dataset EGAD00001006203

• VCF file from 16 patients affected by acute intermittent porphyria

  VCF file from 16 patients affected by acute intermittent porphyria. Whole genome sequencing of these samples was performed in an Illumina HiSeq 4000 instrument. Libraries were prepared using the Fisher PE Kit (Kapa Biosystems). BAM files were processed at the CNAG (Barcelona) with their pipeline, including GATK v3.6 for genotyping and other tools such as snpEff for annotating variants, to produce this VCF file with a total of 10,630,259 variants, out of which 8,731,523 are SNVs.

  Dataset EGAD00001006952

• The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

  The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes includes 444 BAM files from whole genome next-generation sequencing on the Illumina NovaSeq 6000. The samples analyzed include plasma samples from individuals with and without cancer.

  Dataset EGAD00001010931

• NHLBI TOPMed - NHGRI CCDG: The BioMe Biobank at Mount Sinai

  The IPM BioMe Biobank, founded in September 2007, is an ongoing, broadly-consented electronic health record (EHR)-linked clinical care biobank that enrolls participants non-selectively from the Mount Sinai Medical Center patient population. BioMe currently comprises >42,000 participants from diverse ancestries, characterized by a broad spectrum of longitudinal biomedical traits. Participants are enrolled through an opt-in process and consent to be followed throughout their clinical care (past, present, and future) in real-time, allowing us to integrate their genomic information with their EHRs for discovery research and clinical care implementation. BioMe participants consent for recall, based on their genotype and/or phenotype, permitting in-depth follow-up and functional studies for selected participants at any time. Phenotypic and genomic data are stored in a secure database and made available to investigators, contingent on approval by the BioMe Governing Board. BioMe uses a "data-broker" system to protect confidentiality. Ancestral diversity - BioMe participants represent a broad racial, ethnic and socioeconomic diversity with a distinct and population-specific disease burden. Specifically, BioMe participants are of African (AA), Hispanic/Latino (HL), European (EA) and other/mixed ancestry. BioMe participants are predominantly of African (AA, 24%), Hispanic/Latino (HL, 35%), European (EA, 32%), and other ancestry (OA, 10%). Participants who self-identify as Hispanic/Latino further report to be of Puerto Rican (39%), Dominican (23%), Central/South American (17%), Mexican (5%) or other Hispanic (16%) ancestry. More than 40% of European ancestry participants are genetically determined to be of Ashkenazi Jewish ancestry. With this broad ancestral diversity, BioMe is uniquely positioned to examine the impact of demographic and evolutionary forces that have shaped common disease risk. Phenotypes available in BioMe - BioMe has a high-quality and validated set of fully implemented clinical phenotype data that has been culled by a multi-disciplinary team of experienced investigators, clinicians, information technologists, data-managers, and programmers who apply advanced medical informatics and data mining tools to extract and harmonize EHRs. BioMe, as a cohort, offers a great versatility for designing nested case-control sample-sets, particularly for studying longitudinal traits and co-morbidity in disease burden. Biomedical and clinical outcomes: The BioMe Biobank is linked to Mount Sinai's system-wide Epic EHR, which captures a full spectrum of biomedical phenotypes, including clinical outcomes, covariate and exposure data from past, present and future health care encounters. As such, the BioMe Biobank has a longitudinal design as participants consent to make all of their EHR data from past (dating back as far as 2003), present and future inpatient or outpatient encounters available for research, without restriction. The median number of outpatient encounters is 21 per participant, reflecting predominant enrollment of participants with common chronic conditions from primary care facilities. Environmental data: The clinical and EHR information is complemented by detailed demographic and lifestyle information, including ancestry, residence history, country of origin, personal and familial medical history, education, socio-economic status, physical activity, smoking, dietary habits, alcohol intake, and body weight history, which is collected in a systematic manner by interview-based questionnaire at time of enrollment. The IPM BioMe Biobank contributed ~10,600 DNA samples for whole genome sequencing to the TOPMed program. Samples were selected for the Coronary Artery Disease (CAD) and the Chronic Obstructive Pulmonary Disease (COPD) working groups. Using a Case-Definition-Algorithm (CDA), we identified ~4,100 individuals with CAD (~50% women) and ~3,000 individuals as controls (65% women). In addition, we identified ~800 individuals with COPD (62% women) and 1800 individuals as controls (72% women). Another 600 BioMe participants with Atrial Fibrillation, all of African ancestry, were included.

  Study phs001644

• DMET Genes, Nicotine Metabolism and Prospective Abstinence

  This study (DA033813; PI: Andrew W Bergen; PMID:26132489) includes samples from two laboratory studies of nicotine metabolism. The Pharmacokinetics of Nicotine Metabolism in Twins study (PKTWIN; PI: Gary E Swan; PMID: 15527659) was based on recruitment from a twin registry (PMID: 23084148). The Integrated Research Project on Tobacco Use and Dependence (IRP; PI: Gary E Swan; PMID: 14578134) was based on recruitment from a pedigree-based longitudinal study of risk factors for substance use, the Smoking in Families study (SMOFAM; DA03706; PI: Hy Hops). These two laboratory studies (PKTWIN and IRP/SMOFAM) served as the Stage I dataset to interrogate Drug Metabolizing Enzyme and Transporter genes with a targeted SNP array for association with the Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine), an established biomarker of nicotine metabolism. In addition to the laboratory studies, samples from eight RCTs (PMID: 23249876) with the NMR and smoking-related measures used to test SNPs identified in Stage I (PMID: 26132489). In a third stage, a lung cancer meta-analysis database (PMID: 24880342) was used to assess association of SNPs identified in Stage II with lung cancer. The objectives of the study were to identify novel genes and SNPs contributing to nicotine metabolism (Stage I), and to validate PK SNPs associated with the NMR from individuals participating in a clinical laboratory protocol with the NMR obtained from treatment-seeking smokers, and then to investigate association with prospective smoking cessation (Stage II). This study built upon existing studies of nicotine metabolism and randomized trials of smoking cessation therapies. Enhanced knowledge of the genes influencing nicotine metabolism and prospective abstinence may help personalize smoking cessation treatment and risk assessment for smoking-related diseases. For Stage I, both subject [fixed-dose NMR, covariates (age, BMI, ethnicity, sex, smoking status, and hormone use), and pedigree relationships] and sample (common DMET SNP genotype, genotyping quality control) data are available in this accession. The analysis protocol, quality control summaries, summary genotype, summary phenotype, and analysis results are available for Stage I, II and III samples (PMID: 26132489). Extensive discussion of the prior CYP2A6 association literature with the NMR, abstinence, smoking heaviness and lung cancer risk is available (PMID: 26132489). The NMR has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.

  Study phs000931

• EGAD00010000538

  28 unlinked autosomal microsatellite loci for 20 African and 4 philippine populations

  Dataset EGAD00010000538

• SAIF Alignment File

  This is the bam file generated after alignment using BWA program for the SAIF genome

  Dataset EGAD00001000249

• RNA Editing Exome

  Exome sequencing data for tumor and matched normal samples of the EGAS00001000495 project.

  Dataset EGAD00001000626

• TP53_KO_RPE1_SNPs

  Allelic imbalance data for cell lines derived from RPE1 with TP53 knockout

  Dataset EGAD00010001566

• SOGEN_MATRILINEAL_PUZZLE

  598764 SNPs genotyped for 719 indivuals, merge from Illumina Omni1 and Illumina Omni2.5

  Dataset EGAD00010001724

• README-for-EGAS00001004349-linking-HIPO-K09R-WGS-files

  Dataset EGAD00001006739

• Genotypes_Agta

  Genotype data for Agta hunter-gatherers Philippines

  Dataset EGAD00010002140

• Access to dataset, "Feasibility of Functional Precision Medicine for Guiding Treatment of Relapsed/Refractory Pediatric Cancers"

  Access to sequencing data from project.

  Dac EGAC50000000124

• PGx validation GSAv3

  Samples genotyped using Illumina Infinium Global Screening Array v3 for assessing pharmacogenomic genes

  Dataset EGAD00010002645

• Internal Medicine II Technical University Munich DAC

  Data sharing agreement Klinikum rechts der Isar der Technischen Universität München for MEMORI NGS files

  Dac EGAC50000000165

• PDX WES for #87, #95, #32, #217, #86

  Exome sequencing of tumour PDX and matched patient blood

  Dataset EGAD50000000215

• SMPaeds cfDNA lcWGS

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

  Dataset EGAD50000000782

• Indonesian Genome Diversity Project 3

  Term and condition for Indonesian Diversity Project v3. 254 Low Coverage genome from Wallacean and West Papuan, Indonesia

  Dac EGAC50000000312

• DAC for Hematological toxicity following CAR-T cells injection

  Data access committee to have access to Hematological toxicity following CAR-T cells injection

  Dac EGAC50000000490

• Cardiac fibroblast DAC

  DAC for reviewing requests to access data on cardiac fibroblasts as described by Aguado-Alvaro et al (Nature Communications, 2025)

  Dac EGAC50000000479

• BCR-HGBCL-DH-BCL2 project DAC

  Data Access Committee for high-grade B cell lymphomas MYC-BCL2 datasets

  Dac EGAC50000000500

• Therapy and RNA group Ghent DAC

  This is a Data Access Committee for datasets of the Therapy and RNA group Ghent (TaRGeT, Belgium).

  Dac EGAC50000000491

• Small Intestine Adenocarcinoma Subtyping Data Access Committee

  DAC created for the Molecular classification of small intestinal adenocarcinomas Study in Amsterdam UMC.

  Dac EGAC50000000663

• Genome-wide DNA Methylation Analysis Reveals a Unique Methylation Pattern for Pleural Mesothelioma Compared to Healthy Pleura and Other Lung Diseases

  Study EGAS00001007783

• Brain mets discovery cohort variant calls

  These are the variant calls for the 50 brain tumour samples in the discovery cohort.

  Dataset EGAD00001005982

• RNAseq data

  RNAseq data for 10 patients: 10 tumors and 10 cell lines

  Dataset EGAD00001002691

• Exome sequencing VCF files for glioma progression

  Exome sequencing VCF files describing mutations during glioma progression.

  Dataset EGAD00001001887

• Whole genome sequencing data of ccRCCs

  Whole genome sequencing data of ccRCCs were utilized for somatic variations calling.

  Dataset EGAD00001004588

• Biomarker Data Subset

  This dataset contains data for CBC counts and absolute protein abundance measurements from ELISA experiments.

  Dataset EGAD00001011163

• Phylogenetic reconstruction of breast cancer

  This dataset contains the fastq files used for this study

  Dataset EGAD00001006121

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• Shallow Whole Genome Sequencing of Patient Derived Xenografts

  Chemotherapy is the standard-of-care treatment for metastatic colorectal cancer (mCRC) and benefits some patients, but what distinguishes responders from non-responders is unclear. In this study, we leveraged a comprehensive collection of 27 molecularly annotated patient-derived xenografts to uncover functional predictors of response to 5-FU and irinotecan combination therapy (FOLFIRI) in mCRC. Genetic analyses revealed that treatment sensitivity was marked by genomic scars indicative of BRCAness, suggesting homologous recombination (HR) deficiency as a key determinant. Accordingly, we surveyed a manually curated panel of 44 genes with a documented role in HR for the potential presence of pathogenic mutations. We did not observe a specific enrichment of HR gene mutations based on response to FOLFIRI. This result, combined with the absence of widespread biallelic inactivation of the analyzed genes and the predominance of mutations categorized as variants of unknown significance, suggests that FOLFIRI sensitivity is not primarily governed by underlying mutations in HR genes responsible for mitigating the genotoxic effects of this therapeutic regimen.

  Dataset EGAD50000000277

• A Somatic Reference Standard for Cancer Genome Sequencing

  In this study, we separately performed PCR-free whole genome sequencing on the paired tumor/normal melanoma COLO-829 and lymphoblastoid COLO-829BL cell lines across three separate institutions (TGen, Illumina, Genome Sciences Centre at the British Columbia Cancer Agency). We performed a meta-analysis of all data, in combination with the originally reported analysis of these cell lines by Pleasance et al., and report a somatic reference standard based on consensus events. This standard can be used as a reference for tumor/normal whole genome analyses.

  Study phs000932

• Leveraging Extended Families for Cardiovascular Disease Genomic Discovery

  This study characterizes a large early onset atrial fibrillation family and identifies a rare causative mutation (R231H) in the KCNQ1 gene. The age of this mutation is ~5000 years, as estimated using IBD-coalescent methods. The geographic distribution and demographic history of the mutation is described. Induced pluripotent stem cell derived cardiomyocytes carrying the mutation have abbreviated action potential duration, indicating a gain-of-function. This data resource provides exome or whole genome sequencing data for six patients and RNA-seq data from the cardiomyocyte experiments. 

  Study phs002464

• NHLBI's Collection of Datasets for General Research Use (Public Posting of Genomic Summary Results: Not Allowed)

  This collection contains all of NHLBI's authorized individual-level genomic datasets currently in dbGaP that are approved for General Research Use (GRU) and have no further limitations beyond those outlined in the model Data Use Certification Agreement. The public posting of Genomic Summary Results are not allowed. Access to this study collection will include additional authorized individual-level GRU datasets that become available. Renewal of this study collection is required annually. To request access to this study collection, select phs003132 in the dbGaP Authorized Access System.

  Study phs003131

• NHLBI's Collection of Datasets for Health / Medical / Biomedical Research Use (Public Posting of Genomic Summary Results: Allowed)

  This collection contains all of NHLBI's authorized individual-level genomic datasets currently in dbGaP that are approved for Health/Medical/Biomedical (HMB) Research Use and have no further limitations beyond those outlined in the model Data Use Certification Agreement. The public posting of Genomic Summary Results are allowed. Access to this study collection will include additional authorized individual-level HMB datasets that become available. Renewal of this study collection is required annually. To request access to this study collection, select phs003134 in the dbGaP Authorized Access System.

  Study phs003134

• NHLBI's Collection of Datasets for Health / Medical / Biomedical Research Use (Public Posting of Genomic Summary Results: Not Allowed)

  This collection contains all of NHLBI's authorized individual-level genomic datasets currently in dbGaP that are approved for Health/Medical/Biomedical (HMB) and have no further limitations beyond those outlined in the model Data Use Certification Agreement. The public posting of Genomic Summary Results are not allowed. Access to this study collection will include additional authorized individual-level HMB datasets that become available. Renewal of this study collection is required annually. To request access to this study collection, select phs003133 in the dbGaP Authorized Access System.

  Study phs003133

• Molecular correlates for HPV-negative head and neck cancer engraftment prognosticate patient outcomes

  Head and neck squamous cell carcinoma (HNSCC) tumors were sampled from 88 patients. Samples were implanted subcutaenously into the flank of NOD/SCID/IL2R-/- mice. Mice were then monitored weekly for tumor growth and the time of initial palpation was recorded. Mice were euthanized when tumors reached 15 mm in diameter or after 6 months. If no tumor formed by 6 months, the patient tumor was defined as Non-engrafter (N). RNA-seq was performed on all primary tumor samples and successfully engrafted patient-derived xenograft (PDX) samples.

  Study EGAS50000000714

• Molecular counting enables accurate and precise quantification of methylated ctDNA for tumor-naive cancer therapy response monitoring

  Personalized cancer treatment can significantly extend survival and improve quality of life for many patients, but accurate and real-time therapy response monitoring remains challenging. To overcome logistical and technical challenges associated with therapy response monitoring via imaging scans or assays that track the variant allele fraction (VAF) of somatic mutations in circulating tumor DNA (ctDNA), we developed a tumor-naive liquid biopsy assay that leverages Quantitative Counting Template (QCT) technology to accurately and precisely quantify methylated ctDNA (Northstar ResponseTM).

  Study EGAS50000000734

• Estonian Microbiome Project second time point dataset

  Out of the 2,509 Estonian Microbiome Project participants, a sub-cohort of over 300 individuals provided an additional stool sample after a median follow-up period of 4.4 years. All participants of the EstMB cohort gave informed consent for the data and samples to be used for scientific purposes. In the second time point, the participants were instructed to send the samples by post immediately after taking the sample and time their sample collection to avoid shipping on the weekends. The median time between sampling and arrival at the freezer in the core facility for the second time point was 53 hours and 1 minute (mean: 59 hours and 22 minutes; minimum: 34 minutes; maximum: 168 hours and 7 minutes [note: additionally, one outlying measurement was 508 hours and 27 minutes. The shotgun metagenomics paired-end sequencing was performed by Novogene Bioinformatics Technology Co., Ltd., using Illumina NovaSeq6000 platform, resulting in 4.62 ± 0.44 Gb of data per sample (insert size 350 bp, read length 2 × 250 bp).

  Dataset EGAD50000001686

• Intrapatient tumor heterogeneity and clonal evolution in metastatic salivary gland cancer: an autopsy study: access to data

  Funded by the Radboud Oncology Fund and the Dr. C.J. Vaillant Fund, the Salivary Gland Cancer Autopsy research group (Research Group) has sequenced DNA of 17 salivary gland cancer samples of and three healthy control samples from three patients with salivary gland cancer. This research project is led by Carla M.L. van Herpen from Radboud University Medical Centre in Nijmegen, the Netherlands (“Radboudumc”). The DNA sequencing data have been used for an analysis of intrapatient tumor heterogeneity and clonal evolution of metastatic salivary gland cancer. The Research Group is willing to consider applications from third party researchers for access to the DNA sequence data. Access to the data will be granted to qualified researchers for approved use. Access to DNA Sequencing Data is available by application to the Data Access Committee (DAC). Researchers granted access to DNA Sequencing Data must feedback the results of their research to the Research Group, after publication in accordance with the Research Group publication policy set forth in the Data Access Agreement.

  Dac EGAC50000000792

• ChIP sequencing for β-catenin and histone modifications in HCC cell lines and organoids with CTNNB1 mutations

  CTNNB1 mutation is a major driver of HCC, particularly in metabolic-associated subtype. This study utilized patient-derived tumour organoids and cell lines harbouring various endogenous CTNNB1 mutations to comprehensively map the transcriptional repertoire regulated by mutant β-catenin through ChIP-seq under normal and fatty conditions. Genome-wide binding patterns of β-catenin were integrated with ChIP-seq data for histone modifications H3K27ac and H3K27me3 to assess active and repressive chromatin states. Oleic acid treatment was used to model fatty conditions.

  Study EGAS50000001274

• Whole-genome sequencing of cell-free DNA from pancreatic and breast cancer cohorts for fragmentomic and tumor fraction analysis

  This project provides whole-genome sequencing data from cell-free DNA collected from patients with pancreatic ductal adenocarcinoma and breast cancer, alongside healthy and benign controls. The study aims to identify novel liquid biopsy biomarkers by exploring the relationship between per-base quality scores and fragmentomic signals like end-motifs and fragment length. Furthermore, the data supports the development of Bayesian deep learning frameworks for the uncertainty-aware estimation of tumor fraction and ploidy in low-concentration clinical samples.

  Study EGAS50000001620

• Sequencing data for Clinical Trial

  This study is a tri-institutional Phase 1/randomized Phase 2 trial, bevacizumab-naïve rHGA patients were randomized to either LITT (laser interstitial thermal therapy) plus pembrolizumab (LITT+PEM) or non-LITT surgery plus pembrolizumab (NLS+PEM). Randomization ceased after 21 patients when emerging data showed limited adjuvant ICI (immune checkpoint inhibitors) benefit, and the next 24 patients all received LITT+PEM. The goal of the trial is to use LITT, a minimally invasive cytoreductive approach, to prime these “cold” tumors for enhanced ICI response. rHGA= high-grade astrocytoma

  Study EGAS50000001144

• Y90 radioembolization followed by intravenous Nivolumab for advanced hepatocellular carcinoma

  To evaluate the efficacy of combination therapy with ICI and RT in patients, a Phase II trial (CA209-678) was conducted to investigate the efficacy and safety of Y90-RE followed by nivolumab in patients with advanced HCC (NCT03033446, n = 36). Interestingly, the ORR was found to be 30.6% in all patients and 43.5% when restricted to patients with only intrahepatic tumors, suggesting possible synergism between Y90-RE and ICI. Here we present whole exome sequencing (WES) and RNA-seq data for 33 patients treated with Y90 radioembolization followed with Nivolumab.

  Study EGAS00001006834