11750 results for "cancer rna-seq"

in 33.75 milliseconds.

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_BG_T=24hrs_RPMI_T=5days, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_BG_T=24hrs_RPMI_T=5days sample(s). 7 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 EGAD00001002517

• DNA-seq BAM files from 16 carriers of the BMPR2 p.Arg491Gln mutation in a family affected by HPAH

  Paired-end DNA-seq BAM files from 16 carriers of the BMPR2 p.Arg491Gln mutation in a family affected by hereditary pulmonary arterial hypertension (HPAH). Whole genome sequencing of these samples was performed in an Illumina HiSeq 4000 instrument. Libraries were prepared using the Fisher PE Kit (Kapa Biosystems). FASTQ files were processed at the CNAG (Barcelona) using the GEM short-read aligner on the human genome version hs37d5, producing a total of 16 BAM files.

  Dataset EGAD00001005757

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

  ChIP-Seq data for 4 germinal center B cell sample(s). 24 run(s), 22 experiment(s), 22 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 EGAD00001002442

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

  ChIP-Seq data for 2 monocyte - RPMI_T=1hr sample(s). 4 run(s), 4 experiment(s), 4 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 EGAD00001002453

• BLUEPRINT release August 2016, ChIP-Seq for band form neutrophil, on genome GRCh38

  ChIP-Seq data for 4 band form neutrophil sample(s). 26 run(s), 23 experiment(s), 23 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 EGAD00001002454

• BLUEPRINT release August 2016, ChIP-Seq for segmented neutrophil of bone marrow, on genome GRCh38

  ChIP-Seq data for 4 segmented neutrophil of bone marrow sample(s). 24 run(s), 23 experiment(s), 23 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 EGAD00001002390

• 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

• BLUEPRINT release August 2016, ChIP-Seq for conventional dendritic cell, on genome GRCh38

  ChIP-Seq data for 1 conventional dendritic cell sample(s). 4 run(s), 4 experiment(s), 4 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 EGAD00001002310

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

  ChIP-Seq data for 2 monocyte - RPMI_LPS_T=1hr sample(s). 8 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 EGAD00001002376

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_BG_T=4hrs, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_BG_T=4hrs sample(s). 8 run(s), 8 experiment(s), 8 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 EGAD00001002368

• BLUEPRINT release August 2016, ChIP-Seq for immature conventional dendritic cell - GM-CSF_IL4_T=6_days, on genome GRCh38

  ChIP-Seq data for 3 immature conventional dendritic cell - GM-CSF_IL4_T=6_days sample(s). 20 run(s), 20 experiment(s), 20 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 EGAD00001002485

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_LPS_T=24hrs, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_LPS_T=24hrs sample(s). 8 run(s), 8 experiment(s), 8 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 EGAD00001002388

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

  ChIP-Seq data for 1 memory B cell 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002510

• Comparison of protocols for deriving pancreatic progenitors from hPSCs (ATAC-seq)

  This dataset contains 27 mapped bam files. The samples were generated with 3 different protocols for deriving pancreatic progenitors from hPSC. Three parallel differentiations were performed, all done in a hPSC NKX6.1-GFP reporter line. For each protocol there are three cellular populations: total (presort), GFP+ and GFP- . In summary: 3 differentiations x 3 protocols x 3 cellular populations. We prepared ATAC-seq libraries for the 27 samples, and sequenced them on Illumina HiSeq4000.

  Dataset EGAD00001004824

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_T=24hrs, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_T=24hrs sample(s). 7 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 EGAD00001002474

• BLUEPRINT release August 2016, ChIP-Seq for regulatory T cell, on genome GRCh38

  ChIP-Seq data for 2 regulatory T cell 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 EGAD00001002293

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

  Bisulfite-Seq data for 2 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 34 run(s), 3 experiment(s), 4 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_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002346

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days sample(s). 7 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 EGAD00001002448

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_T=6days_LPS_T=4hrs, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_T=6days_LPS_T=4hrs 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 EGAD00001002495

• BLUEPRINT release August 2016, ChIP-Seq for CD38-negative naive B cell, on genome GRCh38

  ChIP-Seq data for 9 CD38-negative naive B cell sample(s). 48 run(s), 44 experiment(s), 44 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 EGAD00001002524

• Tapestri snDNA-seq data along with matched bulk data for validation

  Files from Tapestri snDNA-seq of archival tissue samples from 16 pancreatic ductal adenocarcinoma (PDAC) patients. Matched bulk sequencing (whole-exome, whole-genome, MSK-IMPACT) data are attached for a subset of the patients.

  Dataset EGAD00001009735

• BLUEPRINT release August 2016, ChIP-Seq for class switched memory B cell, on genome GRCh38

  ChIP-Seq data for 3 class switched memory B cell sample(s). 21 run(s), 21 experiment(s), 21 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 EGAD00001002430

• BLUEPRINT release August 2016, ChIP-Seq for monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days_LPS_T=4hrs, on genome GRCh38

  ChIP-Seq data for 3 monocyte - RPMI_LPS_T=24hrs_RPMI_T=5days_LPS_T=4hrs sample(s). 7 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 EGAD00001002296

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

  ChIP-Seq data for 1 CD4-positive, alpha-beta thymocyte sample(s). 3 run(s), 3 experiment(s), 3 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 EGAD00001002386

• 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 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 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 2014, ChIP-Seq for CD14-positive, CD16-negative classical monocyte

  ChIP-Seq data for 9 CD14-positive, CD16-negative classical monocyte sample(s). 59 run(s), 55 experiment(s), 55 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 EGAD00001000913

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

  ChIP-Seq data for 6 CD38-negative naive B cell sample(s). 20 run(s), 20 experiment(s), 20 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 EGAD00001001594

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

  ChIP-Seq data for 7 CD4-positive, alpha-beta T cell sample(s). 46 run(s), 45 experiment(s), 45 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 EGAD00001001147

• BLUEPRINT release August 2015, 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). 11 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 EGAD00001001559

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

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) 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 EGAD00001001183

• 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, 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 2014, ChIP-Seq for CD4-positive, alpha-beta T cell

  ChIP-Seq data for 3 CD4-positive, alpha-beta T cell sample(s). 21 run(s), 21 experiment(s), 21 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 EGAD00001000925

• 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 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 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 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, Bisulfite-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  Bisulfite-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 15 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_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001541

• 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 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 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 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, 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, 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, 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, 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

• ATAC-Seq/Hi-C/4C-Seq dataset for Malignant rhabdoid study

  Study describing the dynamics of chromatin organization within malignant rhabdoid tumors. This study describes how this chromatin organization changes upon SMARCB1 rescue within patient-derived organoid models from malignant rhabdoid tumors. Identification of a novel super-enhancer of MYC and identification of patient-specific enhancer utilization to activate MYC expression in these tumors.

  Dataset EGAD00001011820

• 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

• Combining dasatinib and IFN-α in CML – immunomodulatory effects linked to treatment response and adverse events

  We pooled 100,000 cells from the 12 scRNA+TCRab-seq samples from peripheral blood from three time points (n=4, samples at 0, 3, and 12 months) to understand the landscape of immune subsets in CML during the course of dasatinib+IFN-α treatment

  Study EGAS00001005049

• Characterization of Structural Variants in Acute Myeloid Leukemia Patients

  Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and is expected to increase in frequency as the population ages. Most subtypes of AML are difficult to treat, in part due to a lack of understanding of possible molecular targets. Furthermore, the peak incidence of AML is in the sixth decade, and patients over the age of 65 do not tolerate chemotherapeutic regimens well. Moreover, AML has been highly studied as a model for other cancers. An RNA Biology Program is established aiming to develop comprehensive datasets through the profiling of all different classes of RNA expression, RNA editing, alternative splicing, and non-coding RNAs in CD34+ normal hematopoietic stem cells (HSC), their differentiated progeny, and AML clinical samples that could be utilized to understand leukemia. These datasets will allow us to computationally identify and characterize various RNA species and processes to enable the development of effective therapeutics to AML, leukemia and other types of cancers. Epigenetic factors such as methylation and histone modifications will also be characterized from these clinical samples allowing for integrated dataset analysis to look at the cross-talk between RNA and DNA/epigenomics in leukemia. The following is a subsidiary study of the above study description where whole genome sequencing was performed on two collected AML patients' samples using Illumina and Oxford Nanopore sequencing technologies. The aim is to characterize the roles of structural variants (SV) in RNA biology which are potentially important for AML development using our own established long-read SV characterization tool, NanoVar. The advent of Oxford Nanopore sequencing technologies has opened a new avenue for better SV characterization by having longer sequencing reads. NanoVar is an accurate and rapid SV characterization tool that utilizes low-depth Nanopore sequencing data. NanoVar makes use of split-reads and hard-clipped reads for SV discovery and employs a simulation-trained neural network classifier for true positive enrichment. NanoVar exhibited higher accuracy and faster computational speed amongst other long-read SV detection tools in simulated data. In AML patient data, NanoVar displayed excellent accuracy in SV characterization (16/16 SVs validated in two patients) by using only 4x depth sequencing data. In summary, NanoVar improves the accuracy and speed of SV characterization at a lower sequencing cost, an approach compatible with clinical studies.

  Study phs001847

• PEDS-PLAN - Pediatric Precision Laboratory Advanced Neuroblastoma Therapy

  A multi-center clinical trial for newly diagnosed high-risk neuroblastoma patients. Molecular tumor boards selected one of six targeted agents based on tumor-normal whole exome sequencing and tumor RNA sequencing data.

  Study phs002303

• mUM patient biopsies bulk RNAseq

  Tumour biopsies were obtained from uveal melanoma patients (n = 35) pre and 16 days post treatment with tebentafusp. Biopsies, which were either snap frozen or put in RNA later, were analysed by bulk RNA sequencing (50 million reads per sample) using the Illumina NovaSeq system

  Dataset EGAD50000001225

• Small intestinal neuroendocrine tumors

  The goal of this study was to gain insights into the transcriptomes, mutations and copy number variants present in small intestinal neuroendocrine tumors. The present dataset contains RNA-sequencing performed on seven such tumors.

  Study EGAS00001003358

• RNA sequencing and whole-genome mate-pair sequencing of osteosarcoma

  This data set includes 72 mate pair sequenced osteosarcomas (36 as part of a discovery cohort and 36 as part of a validation cohort). It also includes RNA-sequencing data on 67 osteosarcomas (mostly overlapping with the above mate pair sequenced cases) and 13 osteoblastomas used as controls for gene expression levels.

  Dataset EGAD00001005307

• Blood and skin fibroblasts PSA study

  This dataset contains RNA-sequencing of blood samples from Healthy Controls (n=7), PSA patients (n=27) and RA patients (n=9). It also contains RNA-sequencing data of skin fibroblasts from healthy controls (n=3) and PSA patients (n=3).

  Dataset EGAD00001009069

• Single-cell RNA sequencing of chronic-phase chronic myeloid leukemia patients

  Single-cell RNA sequencing of viably frozen cells from 12 samples from four chronic-phase chronic myeloid leukemia patients. The raw data is available as fastq files.

  Dataset EGAD00001009086

• Illumina 5'end RNA sequencing

  The dataset contains 200 fastq files of Illumina 5'end RNA sequencing data of 50 PBMC samples. The paired-end data includes 100 fastq files (R1 and R2) of 50 full-length cDNA sequencing libraries and 100 fastq files (R1 and R2) of 50 HLA amplicon sequencing libraries.

  Dataset EGAD00001006838

• Ovarian Carcinosarcoma DNA and RNA sequencing of patient samples in the UK cohort.

  Ovarian Carcinosarcoma DNA and RNA sequencing of patient samples in the UK cohort (n=18).

  Dataset EGAD00001011068

• 3D-GSC_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG-derived glioma stem cell (GSC) models. Twenty-two primary GSC models derived from biopsies collected at diagnosis were cultured in an ECM-mimicking compound before for RNA extraction and subsequent rRNA-depletion and sequencing .

  Dataset EGAD00001011079

• RNA sequencing data from children with febrile illness and multisystem inflammatory syndrome in children (MIS-C)

  RNA sequencing data from children with febrile illness and multisystem inflammatory syndrome in children (MIS-C). Samples used were Whole Blood. Febrile illness controls include children with bacterial and viral infections and healthy controls. This dataset contains samples from patients recruited into the DIAMONDS study.

  Dataset EGAD00001011134

• scRNA-seq of human follicular lymphoma and lymph node

  Cryopreserved cells from human nodal follicular lymphoma and lymph node samples were thawed and processed for scRNA-seq analysis. CD3+ CD19− 7-AAD− live T cells (six FL samples from the Original cohort) or 7-AAD− whole live hematopoietic cells (five lymph node and nine FL samples from Validation cohort 1) were sorted using a FACS Aria II or III instrument (BD Biosciences). To generate single-cell emulsions, 6,000–10,000 live cells were loaded into the 10X Genomics Chromium Controller for single-cell partitioning, along with the reverse transcriptase reagent mixture and 3' or 5' gel beads. Sorted T or whole hematopoietic cells were converted to barcoded scRNA-seq libraries using 10X Genomics Chromium single cell 3' reagent kits (v3) for the Validation cohort 1, 5' reagent kits (v1) for the Original cohort, according to the manufacturer’s instructions. The libraries were sequenced on an Illumina HiSeq X Ten or NovaSeq X Plus system, mapped to the human genome (build GRCh38), and demultiplexed using CellRanger pipelines (v3.1.0, 10x Genomics).

  Dataset EGAD50000001143

• Human scMultiome data (GEX and ATAC paired) from CD34+ Bone Marrow (BM) and mobilized peripheral blood (mPB), including CITE-Seq data from mPB CD34+ samples

  We analyzed healthy CD34+ samples from Bone Marrow (BM) and mobilized Peripheral Blood (mPB) in gene therapy experiments within the X-PAND project. BM samples underwent scMultiome analysis, providing paired GEX and ATAC data. For the mPB samples, both scMultiome and CITE-Seq were performed on the same donors, offering additional protein marker information. These datasets allow for an in-depth exploration of chromatin accessibility, gene expression, and surface protein profiling in hematopoietic progenitor cells from healthy donors.

  Study EGAS50000000750

• Genome-wide identification of distinct miRNA-mRNA target regulation pairs in Non-Hodgkin lymphomas: a report from the ICGC MMML-Seq consortium

  We obtained miRNA profiles by miRnome sequencing from ICGC MMML-Seq patients diagnosed with Burkitt lymphoma, diffuse large B-cell lymphoma and follicular lymphoma and provide evidence of subtype-specific miRNA expression differences. We describe differentially expressed, mutated, edited and not previously annotated miRNAs. Addditionally, by performing argonaute-2 photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) experiments, we obtained a set of biochemically-validated miRNA binding sites and identified miRNA-mRNA interaction pairs with a negative correlation in patient RNASeq data.

  Study EGAS00001001394

• ATAC

  ATAC-seq non-overlapping fixed width peaks with score normalization. Software: MACS2 v2.1.2

  Dataset EGAD00010001928

• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  Nodal B-cell non-Hodgkin lymphomas (B-NHL) are a clinically diverse set of cancers thought to originate from distinct stages of B-cell maturation. To characterize intratumor heterogeneity across B-NHL entities, we performed CITE-Seq (cellular indexing of transcriptomes and epitopes) on 51 lymph node samples including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL, n = 11) tumors, in addition to non-malignant reactive lymph nodes (rLN, n = 8). We also performed 5 prime scRNA-seq with immune receptor profiling for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL, and 2 MZL).

  Study EGAS50000000342

• prDLBCL WXS/RNA Data Commitee

  Dac EGAC50000000284

• Adult Eosinophilic Esophagitis Registry Atlas

  Eosinophilic esophagitis (EoE) arises in the setting of type 2 cytokine-driven inflammation, is characterized by the infiltration of eosinophils in the esophageal mucosa, and is associated with dysphagia and fibrostenotic complications. Disease development and progression involve complex interactions between epithelial cells, stromal cells and immune cells. Indeed, increased epithelial permeability is consistently found in EoE, potentially leading to increased antigen exposure and inflammation. Persistent inflammation can in turn lead to fibroblast activation and fibrosis, which, together with the mediators produced by eosinophils and mast cells, leads to dysmotility. To decipher the interactions between these cell types across distinct disease states, we obtained biopsies from 7 healthy controls and 15 EoE subjects, including both active disease and clinical remission, to perform single cell RNA-sequencing (scRNA-seq).Each biopsy sample was enzymatically digested within 3 hours of collection with collagenase/dispase to generate a single cell suspension. Suspensions were then loaded on a 10x Chromium controller, and libraries were generated with the 3' Chemistry pipeline (v2 or v3) before sequencing on an Illumina sequencer. The resulting sequence data was demultiplexed and analyzed with the CellRanger pipeline to generate raw fastq files and processed gene-count matrices for each sample. Altogether, our study generated transcriptomic profiles from 421,312 individual cells after quality control and filtering.Our analyses reveal 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unknown features of EoE. Active disease displayed enrichment of ALOX15+ macrophages, novel PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Receptor–ligand expression uncovered eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures included mast and CD4+ Tissue-resident memory T (TRM) cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors.

  Study phs003574

• Ipilimumab plus Decitabine for Patients with MDS or AML in Post-Transplant or Transplant Naïve Settings

  Design and AimsThis study was a phase 1 clinical trial investigating the combination of ipilimumab (IPI), a CTLA-4 checkpoint inhibitor, and decitabine (DEC), a hypomethylating agent, for treating relapsed/refractory (R/R) myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). The goal was to determine whether DEC could enhance the immune response to IPI without causing excessive immune toxicity. This study provides important insights into the use of immune checkpoint inhibitors with hypomethylating agents in treating high-risk MDS/AML and highlights the need for further research on optimizing immune-based therapies in these settings.Population InformationPost-HSCT arm: 25 patients (23 AML, 2 MDS; all relapsed/refractory).Transplant-naïve arm: 23 patients (15 AML, 8 MDS; 20 relapsed/refractory, 3 untreated).Data available through dbGaPDeidentified individual participant data, Multiplex immunofluorescence staining, Olink, RNA-Seq and Whole Exome SequencingPrincipal FindingsSafety & Tolerability: The maximum tolerated dose (MTD) for IPI was determined to be 10 mg/kg, though higher doses were associated with increased immune-related adverse events (irAEs).Efficacy: The overall response rate (ORR) was higher in transplant-naïve patients (52%) compared to post-HSCT patients (20%).Responses were more frequent at higher IPI doses (10 mg/kg). Some patients achieved durable remission (>1 year), often linked to irAE development.Toxicity: The most common grade ≥3 treatment-emergent adverse events included neutropenia (32%-48%), thrombocytopenia (28%-48%), and febrile neutropenia (36%-61%). irAEs were observed in 44%-48% of patients, with GVHD occurring only in the post-HSCT group.Survival Outcomes: Median overall survival and progression-free survival did not significantly differ between the two arms. However, in transplant-naïve patients, 1-year survival was higher in those who developed irAEs (72.7%) versus those who did not (33.3%).

  Study phs003292

• Repurposing azacitidine and carboplatin to prime immune checkpoint blockade-resistant melanoma for anti-PD-L1 re-challenge

  Purpose: Drug repurposing offers the opportunity for chemotherapy to be used to re-establish sensitivity to immune checkpoint blockade (ICB) therapy. Here we investigated the clinical and translational aspects of an early phase II study of azacitidine and carboplatin priming for anti-PDL1 immunotherapy (Avelumab) in patients with advanced ICB-resistant melanoma. Experimental Design: 20 participants with ICB resistant metastatic melanoma received 2 x 4-week cycles of azacitidine and carboplatin followed by ICB re-challenge with anti-PD-L1 avelumab. The primary objective was overall response rate after priming and ICB re-challenge. Secondary objectives were Clinical benefit rate (CBR), progression-free survival (PFS) and overall survival (OS). Translational correlation analysis of HLA-A and PD-L1 expression, RNA-seq and reduced representation bisulfite sequencing (RRBS) of biopsies at baseline, after priming and after 6 cycles of avelmuab was performed. Results: The ORR determined after azacitidine and carboplatin priming was 10% (2/20) with 2 partial responses (PR). The ORR determined after priming followed by 6 cycles of avelumab (week 22) was 10%, with 2/20 participants achieving iPR. The CBR for azacitidine and carboplatin priming was 65% (13/20) and after priming followed by 6 cycles of avelumab CBR was 35% (n = 7/20). The median PFS was 18.0 weeks (95% CI: 14.87 – 21.13 weeks) and the median OS was 47.86 weeks (95% CI: 9.67 – 86.06 weeks). Translational correlation analysis confirmed HLA-A generally increased after priming with azacitidine and carboplatin, particularly if it was absent at the start of treatment. Average methylation of CpGs across the HLA-A locus was decreased after priming and T-cells, in particular CD8+, showed the greatest increase in infiltration. Conclusions: Priming with azacitidine and carboplatin can induce disease stabilization and re-sensitisation to ICB for metastatic melanoma.

  Study EGAS00001006419

• Repurposing azacitidine and carboplatin to prime immune checkpoint blockade-resistant melanoma for anti-PD-L1 re-challenge

  Purpose: Drug repurposing offers the opportunity for chemotherapy to be used to re-establish sensitivity to immune checkpoint blockade (ICB) therapy. Here we investigated the clinical and translational aspects of an early phase II study of azacitidine and carboplatin priming for anti-PDL1 immunotherapy (Avelumab) in patients with advanced ICB-resistant melanoma. Experimental Design: 20 participants with ICB resistant metastatic melanoma received 2 x 4-week cycles of azacitidine and carboplatin followed by ICB re-challenge with anti-PD-L1 avelumab. The primary objective was overall response rate after priming and ICB re-challenge. Secondary objectives were Clinical benefit rate (CBR), progression-free survival (PFS) and overall survival (OS). Translational correlation analysis of HLA-A and PD-L1 expression, RNA-seq and reduced representation bisulfite sequencing (RRBS) of biopsies at baseline, after priming and after 6 cycles of avelmuab was performed. Results: The ORR determined after azacitidine and carboplatin priming was 10% (2/20) with 2 partial responses (PR). The ORR determined after priming followed by 6 cycles of avelumab (week 22) was 10%, with 2/20 participants achieving iPR. The CBR for azacitidine and carboplatin priming was 65% (13/20) and after priming followed by 6 cycles of avelumab CBR was 35% (n = 7/20). The median PFS was 18.0 weeks (95% CI: 14.87 – 21.13 weeks) and the median OS was 47.86 weeks (95% CI: 9.67 – 86.06 weeks). Translational correlation analysis confirmed HLA-A generally increased after priming with azacitidine and carboplatin, particularly if it was absent at the start of treatment. Average methylation of CpGs across the HLA-A locus was decreased after priming and T-cells, in particular CD8+, showed the greatest increase in infiltration. Conclusions: Priming with azacitidine and carboplatin can induce disease stabilization and re-sensitisation to ICB for metastatic melanoma.

  Study EGAS00001006420

• Whole exome sequencing and RNA sequencing of cervical cancer

  We designed this study to identify molecular markers involved in the development and prognosis of cervical adenocarcinoma. In this study, we would like to elucidate the molecular biological characteristics of cervical adenocarcinoma in relation to the carcinogenesis of other cervical cancers (squamous cell carcinoma), vaginal and vulvar cancers. In addition, some squamous cell carcinomas of the cervix develop at a young age and have a poor prognosis. Furthermore, small cell carcinoma of the cervix also progresses rapidly and has a poor prognosis, making the establishment of diagnostic and therapeutic methods an urgent task.

  Study JGAS000586

• Whole exome sequencing and RNA sequencing of cervical cancer

  We designed this study to identify molecular markers involved in the development and prognosis of cervical adenocarcinoma. In this study, we would like to elucidate the molecular biological characteristics of cervical adenocarcinoma in relation to the carcinogenesis of other cervical cancers (squamous cell carcinoma), vaginal and vulvar cancers. In addition, some squamous cell carcinomas of the cervix develop at a young age and have a poor prognosis. Furthermore, small cell carcinoma of the cervix also progresses rapidly and has a poor prognosis, making the establishment of diagnostic and therapeutic methods an urgent task.

  Study JGAS000582

• Dataset for other_cancer-RNA

  Rare cancer sequencing data of 95 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008847

• Myeloid cell programming in patients with non-medullary thyroid carcinoma

  This dataset contains single-cell RNA sequencing data from patients with thyroid cancer (n=7), multinodal Goiter (n=3) and healthy individuals (n=5). Mononuclear cells were taken from both the peripheral blood and the bone marrow compartments. We used a pooled single-cell design where multiple individuals were pooled in a single sample for sequencing (NextSeq 500-V2) and later demultiplexed using their genotype data. Associated metadata contains information on the phenotypes per individual, the pooling design and the linkage between the supplied files and sequenced pools. Due to limitations from EGA in uploading single-cell data, the raw fastq files were processed as follows: (i) I1/I2/R1/R2 fast files were concatenated over the different lanes. (ii) Concatenated I1 and I2 files were interleaved, as were the concatenated R1 and R2 files to generate two fastq files per pool containing all the information. To interleave the fastq files, the BBmap tool bbmap/reformat.sh was used, which can also be used to de-interleave the files.

  Dataset EGAD00001008108

• Single-Cell RNA-Sequencing of Bone Marrow and Circulating Tumor Cells from Patients with Multiple Myeloma and its Precursor Conditions

  We performed single-cell RNA and V(D)J-sequencing of tumor cells from bone marrow and peripheral blood specimens and patients with Monoclonal Gammopathy of Undetermined Significance (MGUS), Smoldering Multiple Myeloma (SMM), and Multiple Myeloma (MM), as well as healthy donors. In this study we aimed to shed light on the mechanisms underlying tumor cell circulatory capacity and dissemination.

  Study phs003855

• DNA and RNA sequencing data from Ovarian Carcinosarcoma patients from the Glasgow Cohort.

  In this study, we analysed DNA and RNA sequencing data from Ovarian Carcinosarcoma patients, from isolated carcinoma and sarcoma components and investigated DNA variants as well as gene expression. Genomic analyses from this data and further investigation from preclinical models of ovarian carcinosarcoma support the conversion theory for disease development and indicate that microtubule inhibitors could be used to suppress EMT and stimulate anti-tumour immunity.

  Study EGAS00001006605

• AML Proteogenomic Landscape Whole-transcriptome RNA-Sequencing

  Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).

  Dataset EGAD00001008484

• Custom long non-coding RNA capture DAC

  Dac EGAC00001002198

• GLASS-NL RNA-sequencing Study

  Study EGAS00001007551

• A Platform Study of Combination Immunotherapy for the Neoadjuvant and Adjuvant Treatment of Patients with Surgically Resectable Adenocarcinoma of the Pancreas

  This is a single-institutional, randomized, open label platform clinical trial testing various immunotherapy combinations. Patients with newly diagnosed and surgically resectable pancreatic ductal adenocarcinoma (PDA) and scheduled to undergo the Whipple procedure at Johns Hopkins Hospital were eligible to participate in this study. Criteria for determining resectability strictly followed National Comprehensive Cancer Network (NCCN) guidelines. The study consisted of 6 parts: parts 1-5 constituted the Prime Phase (see below), and part 6 was the Extended Treatment Phase. Part 1: Participants received one cycle of immunotherapy two weeks prior to undergoing the Whipple procedure. Part 2: Subjects underwent the Whipple procedure. Part 3: Subjects received a second cycle of immunotherapy 4-10 weeks following the Whipple procedure (2-4 weeks prior to adjuvant chemoradiation). Part 4: Subjects underwent chemoradiation. Part 5: Subjects received 4 additional 28-day cycles of immunotherapy beginning 1-2 months after completing chemoradiation for a total of six cycles (two before chemoradiation and 4 following chemoradiation). Eligible subjects were randomized to Arms A and B in a 1:1 ratio, stratified up-front by age (≤65, >65). Arm A participants received Cy/GVAX. Arm B participants received the combination of Cy/GVAX and nivolumab. GVAX Vaccine consists of equal numbers (2.5 x 108 each) of Panc 6.03 pcDNA1GM-CSF and Panc 10.05 pcDNA1GM-CSF combined into a single vaccination. Each vaccination consisted of six total intradermal injections. A single intravenous (IV) dose of 200 mg/m2 Cy (CytoxanR) was given one day prior to GVAX. Anti-PD-1 Therapeutic Antibody Nivolumab 480 mg every 4 weeks was administered as an intravenous infusion. The primary objective for Arms A and B was to compare IL17 expression (Th17) in vaccine-induced lymphoid aggregates between resected PDAs from patients treated with the combination of Cy/GVAX and anti-PD-1 blockade antibody vs. the treatment of Cy/GVAX alone. Secondary objectives were to assess the safety, overall survival (OS), and disease free survival (DFS) of patients treated with each of the study drug combinations, and to assess the effects of each of the immunotherapy study drug combinations on PD-L1/PD-1 associated pathways, vaccine-induced immune regulatory signatures, and peripheral and intratumoral antigen specific T cell responses. The exploratory objective was to explore the effects of therapy on tumor and peripheral blood and tumor infiltrating immune cells, and to explore potential molecular determinants of response, progression and disease stability. To these ends, tumor infiltrating immune cells (TIICs) were obtained from surgically resected tumors following the neoadjuvant treatment of the study immunotherapy. TIICs were sorted into CD3+CD4+ T cells, CD3+CD8+ T cells, CD3-CD19+ B cells, and CD3-CD19-CD11b+ myeloid cells by flow cytometry. The sorted immune cell subtype pellets were sent to the commercial vendor (Eurofins) for RNA extraction and sequencing. SMART-seq v4 Ultra Low Input mRNA Sample Prep Kit was used for library preparation with 100bp paired end reads. FASTQ files, that were returned from the vendor, are deposited here.

  Study phs003002

• Profiles of Extracellular RNA in Cerebrospinal Fluid and Plasma from Subarachnoid Hemorrhage Patients

  Brain injury resulting from hemorrhagic stroke is clinically challenging to manage and results in high rates of morbidity and mortality. The pathophysiology of brain damage resulting from aneurysmal subarachnoid hemorrhage (aSAH) is largely unknown, and methods to treat and monitor patients are variable with no meaningful correlations to patient outcome. Prediction of patient risk for serious neurological complications is currently a significant clinical obstacle. An extracellular RNA (exRNA) biomarker to predict onset and severity of brain damage would improve patient outcomes. We sequenced plasma and CSF samples from adult patients with SAH. Samples were collected from post bleed day 1 to day 7. Total exRNA was isolated from each sample. In addition, we prepared a subset of 140 CSF samples, isolating the RNA contained within extracellular vesicles and vesicle-depleted biofluid.

  Study phs001759

• RNA Ligation Precedes U6 snRNA/LINE-1 Retrotransposition

  Long Interspersed Element-1 (L1) retrotransposons make up approximately 17% of the human genome. L1 moves throughout the genome via a copy-and-paste mechanism termed retrotransposition. L1 retrotransposition also mobilizes other cellular RNAs such as SINES, U6 snRNA and, mRNA, which for the latter results in the formation of processed pseudogenes. In this study we demonstrate that the RNA ligase RtcB can create chimeric RNAs by joining U6 snRNAs to 5'-truncated L1 or other cellular RNAs. We also show that the retrotransposition of U6/L1 chimeric RNA leads to U6/L1 pseudogene formation. In sum, our data suggests that U6 snRNA and RtcB may have biological roles in addition to their essential functions in mRNA and tRNA splicing, and that the retrotransposition of chimeric RNAs could be a mechanism to create novel genes.

  Study phs001671

• RNA sequencing in primary inflammatory (TPP) macrophages following deletion of a disease-associated gene desert at chr21q22, disruption of ETS2, or treatment of ETS2-edited macrophages with a HIF1α stabiliser.

  GWAS studies in five different inflammatory diseases have identified a strong genetic association at a gene desert at the chr21q22 locus. We have shown that this locus contains a monocyte/macrophage-specific enhancer that regulates ETS2 - a gene whose role in primary human monocytes/macrophages is incompletely understood. We therefore used a CRISPR-Cas9-based approach to delete the enhancer region or to disrupt ETS2, and performed RNA-sequencing to examine the transcriptional consequences. One effect of ETS2 disruption was upregulation of genes involved in aerobic respiration and oxidative phosphorylation. We therefore treated ETS2-edited inflammatory macrophages with roxadustat, a HIF1α stabiliser that can promote glycolysis via HIF1α-mediated metabolic reprogramming, and performed RNA-sequencing to determine whether this drug might rescue the transcriptional effects of ETS2 disruption.

  Study EGAS00001007553

• Rapid Idiosyncratic Mechanisms of Clinical Resistance to KRAS G12C Inhibition

  Using deep RNA and whole exome sequencing of pre- and post-treatment autopsy samples, we reveal diverse clonal populations that occurred through altered cell intrinsic, tumor microenvironment and immunologic remodeling mechanisms of resistance.

  Study phs002734

• Targeting Heterochromatin Eliminates Malignant Stem Cells in Chronic Myelomonocytic Leukemia Through Reactivation of Retroelements and Innate Immune pathways

  RNA-sequencing, ATAC-sequencing, CUT&Tag-sequencing, data from CMML patient and aged-matched healthy donor CD34+ cells. RNA-sequencing data from patient CD34+ cells after treatment for 4 days with decitabine or UNC0638 inhibitors, alone or together, or left untreated

  Dataset EGAD50000000542

• RNASeq profiles from Indian HFrEF Cohort

  The dataset contains whole transcriptomics profiling from a cohort of 49 HFrEF patients. The myocardium samples were collected from these patients during heart transplant of LVAD implantation procedure. The RNA samples were collected in RNAlater. Total transcriptome libraries were prepared using TruSeq Stranded Total RNA Library Prep with Ribo-Zero Gold kit and sequenced on Illumina HiSeq4000.

  Dataset EGAD50000001196

• Multi-omics profiling identifies two epithelioid sarcoma molecular subtypes with distinct signaling and immune characteristics

  Bulk RNA Sequencing of 33 epithelioid sarcomas (EpS) and 3 extracranial extra-renal rhabdoid tumors (EERT) Whole-exome sequencing of 30 EpS and 3 EERT Single-cell RNA Sequencing of 8 EpS Visium Spatial Transcriptomics of 3 EpS

  Dataset EGAD50000001419

• The study on genomic profiling using clinical specimens (tissue, blood, etc.) form patients with lung and thymic tumors

  The objective is to generate a comprehensive immunogenomic profiling in thymoma using RNA sequencing and TCR repertoire analysis and to evaluate the potential of immunotherapy in thymoma from multiple perspectives.

  Study JGAS000552

• 5' single cell RNA sequencing

  Pared-end fastq files of 22 AML and 2 normal bone marrow samples were generated using 10x Genomics 5' single cell RNA sequencing following manufacturer's protocol (CG000086 User Guide RevG) for chemistry version 1.0 and targeting 10000 cells per sample. Libraries were sequenced using Illumina HiSeq 4000 using recommended cycling parameters.

  Dataset EGAD00001007672

• Single Cell RNA sequencing of Organoids from TSC2+/- patient IPSCs in H- and L-medium

  This dataset contains single cell RNA sequencing data from Organoids grown in high nutrient (H) and low nutrient (L) medium. Organoids were grown for 110 days. Organoids were grown from a patient IPS cell line with a heterozygous mutation in TSC2 (Patient 1 TSC2+/- iPSCs) and an isogenic control cell line (TSC2+/+).

  Dataset EGAD00001006332