11750 results for "cancer rna-seq"

in 29.97 milliseconds.

• Dataset on keratinocytic gene expression pattern in Hidradenitis suppurativa

  Human skin samples were obtained from HS patients after informed consent (Ethical vote, University of Würzburg; No. 306/12). Lesional and perilesional were taken and epidermis and dermis separated. Isolated epidermal keratinocytes were further processed for RNA isolation. mRNA was extracted from five pairwise-matched lesional and perilesional epidermal HS pellets and RNA sequencing was performed.

  Dataset EGAD00001008005

• RNA-sequencing data of post-mortem brain tissue taken from individuals with SCA3 and controls

  Spinocerebellar ataxia type 3 (SCA3) is the most common autosomal dominant inherited ataxia worldwide, caused by a CAG repeat expansion in the Ataxin-3 gene resulting in a polyglutamine (polyQ)-expansion in the corresponding protein. Here we have RNA-sequencing data from the cerebellum of individuals with SCA3 and matched controls.

  Dataset EGAD00001009317

• Single-cell RNA sequencing on 5063 single T cells from six hepatocellular carcinoma patients

  T cells isolated from peripheral blood, tumors and adjacent normal tissues from six hepatocellular carcinoma patients. SmartSeq2 and Tang2009 protocol were used to amplify RNA from single T cells. High depth enables simultaneously expression profiling and TCR assembling.

  Dataset EGAD00001003337

• H3K4me3, IgG, and Input ChIP-seq in overexpression of pLV Control, CS-FL and CS-ΔEx4 in SW1116 cells.

  none

  Study EGAS00001008121

• Atezolizumab Plus Personalized Neoantigen Vaccination in Patients with Urothelial Cancer: a Phase 1 Trial

  This is a phase 1 single center clinical trial evaluating a multi-peptide personalized neoantigen vaccine, PGV001, in combination with atezolizumab for patients with urothelial cancer. Patients were enrolled in either the adjuvant or metastatic setting. Those in the adjuvant setting included patients considered high risk of recurrence (pathological stage ypT2-T4 or ypN+ for those treated with prior neoadjuvant chemotherapy or pT3-T4 or pN+ for those not treated with prior neoadjuvant chemotherapy). Those in the metastatic setting included patients progressing after prior chemotherapy or those with at least stable disease after chemotherapy treated as “switch maintenance” therapy. The primary endpoint was feasibility (including the number of neoantigens identified per patient, vaccine production time, the proportion of patients consenting to the vaccine development phase for whom a vaccine product was prepared, and the proportion of patients eligible for the treatment phase who completed the priming cycle 1 of PGV001 plus atezolizumab) and safety. Secondary endpoints included the objective response rate, progression-free survival or disease-free survival, overall survival, and immunomodulatory effects of treatment. Urothelial tumor tissue and normal tissue or peripheral blood specimens were utilized for DNA sequencing. HLA typing was performed utilizing genomic DNA isolated from whole blood, and RNA sequencing was performed utilizing tumor tissue specimens. Neoantigen prediction and final selection for incorporation into the vaccine was determined by the OpenVax computational pipeline. 12 patients were initially enrolled into the vaccine development phase and 10 patients initiated the treatment phase. PGV001 was administered with poly-ICLC and tetanus peptide on subsequent days for up to 10 doses, including an initial priming cycle 1. Atezolizumab was administered every 3 weeks for up to 1 year in the adjuvant setting or 2 years in the metastatic setting. The vaccine was feasible and safe, the median vaccine production time was 20.3 weeks, and 100% of patients that initiated the treatment phase completed the priming cycle. The most common treatment-related adverse events were local grade 1 injection site reactions, fever, and fatigue. Three of the 4 patients in the adjuvant setting remained disease-free at median follow-up of 39.5 months. There was a 40% objective response rate among patients with measurable disease in the metastatic setting. The single patient treated as “switch maintenance” therapy remained with no evidence of disease at 39.4 month follow-up. All patients demonstrated on-treatment neoantigen-specific T cell reactivity, as measured by ELISPOT and flow cytometry assays. Overall, PGV001 plus atezolizumab was feasible, safe, and warrants further investigation. Whole exome sequencing from tumor tissue and normal tissue or whole blood specimens and RNA sequencing from tumor tissue specimens will be available through dbGaP.

  Study phs003922

• RNA Sequencing of Surgically Resected Colorectal Liver Metastases

  Dac EGAC00001000904

• RNA sequencing of sorted human RCC immune populations

  Dac EGAC00001002861

• CNA differences between RNA-based subtypes of PDAC

  Dac EGAC50000000730

• Genetic characterization of B-cell prolymphocytic leukemia: a hierarchical prognostic model involving MYC and TP53 abnormalities - RNA-seq

  B-cell prolymphocytic leukemia (B-PLL) is a rare disease, whose molecular pathology is largely unknown. We report here the cytogenetic and molecular findings in a large series of 34 B-PLL. Karyotype (K) was complex (≥3 abnormalities) in 73%, and highly complex (HCK≥5) in 45%. The most frequent chromosomal aberrations were: translocation targeting the MYC gene [t(MYC)] (62%), 17p deletion including TP53 gene (38%), trisomy 18/18q (30%), 13q14 deletion (29%), trisomy 3 (24%), trisomy 12 (24%) and 8p deletion (23%). Whole-Exome Sequencing performed in 16 patients revealed recurrent mutations in TP53 (6/16, 38%), MYD88 (n=4), BCOR (n=4), MYC (n=3), SF3B1 (n=3), FAT1 (n=3), SETD2 (n=2), CHD2 (n=2), CXCR4 (n=2) and BCLAF1 (n=2). The main group of patients (21/34, 62%) had a t(MYC) associated with a higher percentage of prolymphocytes (p=0.03), CD38 expression (p<0.001), lower K complexity (p=0.0004), mutations in MYC and in genes involved in RNA metabolism and chromatin remodeling. Principal component analysis of gene expression data showed that patients with t(MYC) clustered together. A second group with MYC gain (5/34, 15%), was associated with HCK≥5 (p=0,01) and trisomy 3 (p=0,008). Altogether, 26/34 patients (76%) had a MYC activation, translocation or gain, that were mutually exclusive. We identified 3 distinct cytogenetic prognostic groups (p=0.0006): lower risk: absence of MYC activation (median not reached); intermediate risk: MYC activation without del17p (125 months); high risk: MYC activation + del17p (11 months). Our results show that cytogenetic analysis is a useful diagnostic tool in B-PLL that improves prognostic stratification.

  Study EGAS00001003274

• BIRC5 Upregulation Enhances DNMT3A-Mutant T-ALL Cell Survival and Pathogenesis

  The goal of this study was to determine how patients with T-ALL who have DNA methyltransferase 3 alpha (DNMT3A) mutations develop resistance to therapy. Since DNMT3A is a methyltransferase, we examined the effect of those mutations on epigenetic regulation using whole genome bisulfite sequencing (WGBS). We also assessed any changes in gene expression using RNA-sequencing. We found that T-ALL patients with DNMT3A mutations are resistant to apoptosis and some chemotherapies. WGBS showed that DNMT3A mutated patients clustered together by their epigenetic profile, which was associated with hypomethylation at TERT and HOX genes. RNA sequencing identified differences in JAK/STAT signaling.

  Study phs003623

• Development of hunanized mice for human hematopoisis and immunity research

  We obtained peripheral blood or bone marrow samples from 112 acute myeloid leukemia patients and examined sensitivity and resistance of the AML samples to molecular targeting drugs. By doing RNA sequencing using AML cells, we found heterogenous gene expression patterns in AML with different mutational landscapes. From the RNA sequencing, we identified anti-apoptosis, cell cycle progression, and immune-related signals as pathways up-regulated in AML cells as compared with normal CD34+ cells. From the pathway analyses, we further setup in vitro experiments to find vulnerability genes in clinically-aggressive AML. BIRC, BCL2, MCL1, AURKB, and KIF10 may be essential molecules for AML cell survival and proliferation.

  Study JGAS000253

• Meisal temporal lobe epilepsy sequencing study

  Our understanding of mesial temporal lobe epilepsy (MTLE) is one of the most common forms of drug-resistant epilepsy in humans. Using RNA- and small RNA-sequencing in parallel, we explored differentially expressed genes in the hippocampus and cortex of MTLE patients who had undergone surgical resection and non-epileptic controls. We found significant enrichment for astrocytic and microglial genes amongst up-regulated genes and down-regulation of neuron-specific genes in the hippocampus of MTLE patients. The transcriptome profile of the small RNAs reflected disease state more robustly than mRNAs, even across brain regions which show very little pathology.

  Study EGAS00001003922

• HiChIP fastq files

  HiChIP experiments with two sequencing libraries each. Illumina HiSeq 4000/2500.

  Dataset EGAD00001006278

• Human Biofluid RNA Atlas Project - raw data

  Study EGAS00001003917

• FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data

  We have developed FusionSeq to identify fusion transcripts from paired-end RNA-sequencing. FusionSeq includes filters to remove spurious candidate fusions with artifacts such as misalignments or random pairing of transcript fragments and it ranks candidates according to several statistics. It also has a module to identify exact sequences at breakpoint junctions. FusionSeq detected known and novel fusions in a specially sequenced calibration data set, including 8 cancers with and without known rearrangements.

  Study phs000311

• Total RNA expression in benign ovarian and malignant ovarian tumours

  This study investigates the total RNA expression profiles of fresh-frozen ovarian tumors, providing a comprehensive analysis of 112 samples from 111 women in Sweden, two samples being replicates. The dataset includes 18 benign and 94 malignant tumours, collected as part of the U-CAN initiative from Uppsala Biobank. The sequencing data offers valuable insights into ovarian tumour biology and can be used to enhance understanding of tumour classification and molecular characteristics.

  Study EGAS50000001045

• Bulk RNAseq from in vitro generated macrophages and T cells, and mUM tumour biopsies

  Monocyte derived macrophages were polarised into M1 or M2 using IFNg+ ILP or IL-4 respectively (n =4) . Pan T cells were cultured with or without IL-2 for 4 days (n=4 - 6). Pan T cells were co-cultured with THP1 tumour cells for 20 hours in the presence or absence of ImmTAC molecules and in the presence or absence of M2 macrophages. Sorted T cells and macrophage populations prior and post co-culture were analysed by bulk RNA sequencing (60 million reads per sample) using the Illumina NovaSeq system. 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

  Dac EGAC50000000518

• Single nuclei RNA sequencing on Primary and Maladaptive FSGS patient samples

  We used single-nucleus RNA-sequencing to identify differentially expressed transcriptional signatures in kidney biopsies of well-phenotyped primary vs. maladaptive FSGS patients. We included cryopreserved kidney biopsy cores from adult patients with newly diagnosed primary FSGS (n = 9, all nephrotic), maladaptive FSGS (n = 9, not nephrotic), proteinuric controls (PLA2R-positive membranous nephropathy, n = 3), and healthy controls (n = 4). We identified 120,751 high-quality nuclei, including 2,471 podocytes and 1,574 parietal epithelial cells (PECs).

  Study EGAS50000001070

• Single-cell RNA sequence analysis of iPS cell-derived cardiomyocytes treated with S-RBD-sfGFP or GFP

  To determine the transcriptional response in human cardiomyocytes treated with SARS-CoV-2 spike receptor-binding domain (S-RBD-sfGFP), human cardiomyocytes differentiated from induced pluripotent stem cells (iPSC-CMs) were replated on day 21 after differentiation and treated with S-RBD-sfGFP or control-GFP for 48 hours on day 26-28. iPSC-CMs were dissociated and the single cell suspension was analyzed by single cell RNA-sequence.

  Study JGAS000620

• single-cell RNA-sequencing of human/mouse colonic crypts

  Human colonic stem cells are slow cycling compared with murine rapid cycling stem cells. Our study aims to identify slow cycling stem cell in human normal colon in vivo and to analyze gene signatures among rapid cycling stem cells, slow cycling stem cells and secretory progenitor cells using single cell RNA-seqencing . We further investigated in vivo dynamics of human colonic stem cell using patient-derived gene-enginered organoids and orthotopic xenotransplantation.

  Study JGAS000550

• RNA sequencing in primary human macrophages overexpressing ETS2

  We investigated an intergenic haplotype on chr21q22, linked to five different inflammatory diseases, and discovered a mechanism that orchestrates macrophage responses during chronic inflammation. We delineated how the risk haplotype increases expression of the causal gene, ETS2, and demonstrated that ETS2 is necessary for inflammatory macrophage effector functions. To establish whether ETS2 is sufficient to drive inflammatory responses, we overexpressed ETS2 in a dose-dependent manner and performed RNA-sequencing to characterise the transcriptional effects.

  Study EGAS00001007554

• An IL-1β driven neutrophil-stromal cell axis fosters a BAFF-rich microenvironment in multiple myeloma

  This depository contains data from two bulk RNA sequencing experiments: 1) Bulk RNA sequencing data of peripheral blood neutrophils from healthy donors cultured with a) human adipose-derived stromal cells (ADSC) as a model for mesenchymal stromal cells (MSC), and b) IL-1β stimulated ADSC as a model for inflammatory MSC as found in multiple myeloma (MM). 2) Bulk RNA sequencing data from ADSCs cultured a) without stimuli, b) with recombinant human IL-1β, c) with supernatant from iMSC-like cells, d) with neutrophils previously cultured with MSC, e) with neutrophils previously cultured with iMSC, f) with neutrophils previously cultured with iMSC in the presence of anti-human IL-1β or g) with neutrophils previously cultured with iMSC in the presence of an isotype control.

  Dataset EGAD00001010080

• RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC

  RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC. CD34+ hematopoietic stem cells (HSC) were isolated from healthy controls before transduction with WT-SRC and E527K-SRC lentiviral vectors in triplicate and differentiation to MK. Three replicates each of two pools were generated for both WT and E527K SRC transduced cells, resulting in 3 WT pool 1 samples, 3 WT pool 2 samples, 3 E527K pool 1 samples and 3 E527K pool 2 samples for a total of 12 samples. RNA was extracted and sequenced with following parameters: Platform: Illumina HiSeq4000, Library Prep Kit: TruSeq stranded mRNA, Sequencing Kit: Illumina HiSeq4000 100 cycles (76-8-8-7), Fragments: single end / fr-firststrand.

  Dataset EGAD00001008356

• Whole genome, exome and RNA sequencing of TFCP2-rearranged rhabdomyosarcoma

  Whole genome, exome and RNA sequencing of TFCP2-rearranged rhabdomyosarcoma, 86 samples of paired fastq files, sequenced on: Illumina HiSeq 2500 using Agilent SureSelect WGS, Illumina HiSeq 2500 using Illumina TruSeq RNA, Illumina HiSeq 2500 using Agilent SureSelect v5 WES (+UTRs), Illumina HiSeq 4000 using Agilent SureSelect WGS, Illumina HiSeq 4000 using Illumina TruSeq stranded mRNA Kit, Illumina HiSeq 4000 using Agilent SureSelect v5 WES stranded mRNA Kit, Illumina NovaSeq 6000 using Illumina TruSeq Stranded mRNA, Illumina NovaSeq 6000 using Illumina TruSeq Nano DNA, Illumina HiSeq X Ten using TruSeq Nano DNA.

  Dataset EGAD00001010108

• RNA-sequencing of a representative cohort of 209 AML cases

  Sample libraries were prepped using 500 ng of input RNA according to the KAPA RNA HyperPrep Kit with RiboErase (HMR) (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Amplified sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned against the human genome (hg19) using STAR v2.5.4b2.

  Dataset EGAD00001007581

• RNAseq data of polyA+ RNA from Leukocytes from 624 individuals of the ProgeNIA cohort.

  We sequenced the polyA+ fraction of the RNA of the leukocytes from 624 sardinian individuals with RNAseq. Prior to library preparation we added either ERCC ExFold RNA Spike-In. An average of 60M reads per samples with 51 bp paired-end reads were generated on a HiSeq 2000 (Illumina). Sequencing reads were then aligned using STAR-2.2.0c2 to the h37d5 reference genome supplemented with the ERCC spike-ins sequences. We further provided an exon-exon junction database that we generated from the GENCODE v14 annotation. In order to remove a contamination from a parallel experiment, we discarded any reads that mapped to the genomic regions of CBLB (chr3:105370773-105592330) and BCL11A (chr2:60672555-60784156). Filtered aligned reads (bam format) are shared.

  Dataset EGAD00001003102

• Total RNA sequencing of 32 chronic lymphocytic leukemia (CLL) patients

  Peripheral blood mononuclear cells (PBMC) of CLL patients were isolated by density-gradient centrifugation over Linfosep (Biomedics, Madrid, Spain). B cells were purified with a CD19+ magnetic-bead system (MidiMACS, Miltenyi Biotec, Bergish Gladbash, Germany) according to the manufacturers’ instructions. Mean B-cell purity was >99% and the mean percentage of CD5+/CD19+ cells after purification was >98%, as measured by flow cytometry. Total RNA was extracted from purified cells in a single step using TriPure Isolation Reagent (Roche Applied Science, Vilvoorde, Belgium).Whole transcriptome sequencing libraries were prepared using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina). Libraries underwent 2 × 76 bp paired-end sequencing on a HiSeq 2500 instrument (Illumina). The median number of paired-end reads was 60.5 million (range, 49.7-79.7 million).

  Dataset EGAD00001004047

• EM-seq datatset of primary human thymocyte subsets

  FASTQ-files of EM-seq experiments of 6 primary human thymocyte subsets, isolated by flow-cytometry (FACS) from neonatal thymus biopsies. All samples were sorted to be negative for the expression of the lineage surface markers CD11c, CD19, CD56, CD141 and CD303 and additionally had the following surface phenotypes: DN2 (CD4-CD8-CD7+CD1a-CD161-CD38+CD3-TCRab-); DN3 (CD4-CD8-CD7+CD1a+); ISP (CD4+CD8-CD3-TCRab-CD1a+); DPE (CD4+CD8+TCRab-CD69-); DPL (CD4+CD8+TCRab+CD69+); SP4 (CD4+CD8-). Samples were sorted from 1-2 pediatric patients undergoing heart surgery.

  Dataset EGAD50000001600

• mRNA-seq data from ALL patients with NUP214::ABL1 disease

  Dataset contains mRNA-sequencing data (fastq files) from 2 patients with NUP214::ABL1 disease (one T-ALL and one B-ALL patient). Patient samples (peripheral blood or bone marrow) were taken at diagnosis (B-ALL) or relapse (T-ALL). mRNA was extracted from lymphoblasts and underwent paired-end sequencing (75bp) using the Illumina NextSeq platform. Transcriptomic data was used for identification of gene fusions, SNVs and InDels. mRNA-seq data for an additional B-ALL patient with NUP214::ABL1 disease utilised in this study has already been published (EGAS00001006460).

  Dataset EGAD50000001395

• scRNA-seq data of Anti-Her2-CAR T cells treated with immunomodulatory metabolites

  Anti-Her2-CAR T cells produced from T cells derived from healthy human donors were treated with DMSO (control) or either of the immunomodulatory metabolites indole‐3‐carboxaldehyde (ICA), valeric acid (VA) and isovaleric acid (IVA) to assess effects on CAR T cell function. scRNA-seq libraries were generated on a 10X Chromium controller with On-Chip-Multiplexing. The individual samples in a library are distinguished during analysis on the single-cell level via the cell barcode class.

  Dataset EGAD50000002012

• Investigation of chromatin accessibility in clear cell renal cell carcinoma using ATAC-seq

  To identify regions of open and accessible chromatin in clear cell renal cell carcinoma (ccRCC), we performed ATAC-seq on the 786-O, A-498, UM-RC-2 ccRCC cell lines, as well as HK-2 normal renal epithelium cell line, cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions. Data are provided in raw fastq and processed narrowPeak fomats. Data were processed using the nf-core atacseq pipeline.

  Dataset EGAD50000001885

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

  ChIP-Seq data for 3 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte sample(s). 11 run(s), 11 experiment(s), 11 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 EGAD00001002445

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

  ChIP-Seq data for 3 central memory CD8-positive, alpha-beta T 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 EGAD00001002450

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

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

  Dataset EGAD00001002506

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

  ChIP-Seq data for 3 effector memory CD8-positive, alpha-beta T cell sample(s). 16 run(s), 15 experiment(s), 15 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 EGAD00001002283

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

  ChIP-Seq data for 3 central memory CD4-positive, alpha-beta T cell sample(s). 11 run(s), 9 experiment(s), 9 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 EGAD00001002439

• BLUEPRINT release August 2016, ChIP-Seq for mature conventional dendritic cell - GM-CSF_IL4_T=6_days_R848_T=24hrs, on genome GRCh38

  ChIP-Seq data for 3 mature conventional dendritic cell - GM-CSF_IL4_T=6_days_R848_T=24hrs sample(s). 20 run(s), 19 experiment(s), 19 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 EGAD00001002462

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

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

  Dataset EGAD00001002503

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

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

  Dataset EGAD00001002362

• BLUEPRINT release August 2016, ChIP-Seq for cytotoxic CD56-dim natural killer cell, on genome GRCh38

  ChIP-Seq data for 6 cytotoxic CD56-dim natural killer cell sample(s). 34 run(s), 34 experiment(s), 34 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 EGAD00001002463

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

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

  Dataset EGAD00001002369

• BLUEPRINT release August 2016, ChIP-Seq for mesenchymal stem cell of the bone marrow, on genome GRCh38

  ChIP-Seq data for 2 mesenchymal stem cell of the bone marrow 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 EGAD00001002381

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

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

  Dataset EGAD00001002514

• scRNA-seq raw data

  scRNA-seq data of B-lineage cells from the cerebrospinal fluid of 21 patients with multiple sclerosis. The data was generated with the Smart-seq2 protocol and sequenced on Illumina NextSeq500.

  Dataset EGAD00001008481

• BLUEPRINT release August 2016, ChIP-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  ChIP-Seq data for 10 CD14-positive, CD16-negative classical monocyte sample(s). 80 run(s), 76 experiment(s), 76 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 EGAD00001002484

• BLUEPRINT release August 2016, ChIP-Seq for mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs), on genome GRCh38

  ChIP-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 23 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 EGAD00001002470

• BLUEPRINT release August 2015, ChIP-Seq for CD3-positive, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte sample(s). 3 run(s), 3 experiment(s), 3 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 EGAD00001001503

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

  ChIP-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 4 run(s), 4 experiment(s), 4 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 EGAD00001001195

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

  ChIP-Seq data for 7 CD4-positive, alpha-beta T cell sample(s). 39 run(s), 39 experiment(s), 39 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 EGAD00001001505

• BLUEPRINT release August 2015, ChIP-Seq for mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs), on genome GRCh38

  ChIP-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 18 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001527

• BLUEPRINT release August 2015, ChIP-Seq for central memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 9 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 EGAD00001001499

• BLUEPRINT release August 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  ChIP-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 4 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001577

• BLUEPRINT release August 2015, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 6 experiment(s), 6 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 EGAD00001001557

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

  ChIP-Seq data for 5 CD8-positive, alpha-beta T cell sample(s). 26 run(s), 26 experiment(s), 26 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 EGAD00001001513

• BLUEPRINT release August 2015, ChIP-Seq for mesenchymal stem cell of the bone marrow, on genome GRCh38

  ChIP-Seq data for 1 mesenchymal stem cell of the bone marrow sample(s). 9 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 EGAD00001001578

• BLUEPRINT release August 2015, ChIP-Seq for CD3-negative, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD3-negative, CD4-positive, CD8-positive, double positive 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 EGAD00001001570

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

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell sample(s). 10 run(s), 10 experiment(s), 10 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 EGAD00001001472

• BLUEPRINT release August 2015, ChIP-Seq for cytotoxic CD56-dim natural killer cell, on genome GRCh38

  ChIP-Seq data for 4 cytotoxic CD56-dim natural killer cell sample(s). 17 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 EGAD00001001518

• BLUEPRINT release August 2015, ChIP-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  ChIP-Seq data for 9 CD14-positive, CD16-negative classical monocyte sample(s). 56 run(s), 53 experiment(s), 53 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 EGAD00001001552

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

  ChIP-Seq data for 9 CD4-positive, alpha-beta T cell sample(s). 68 run(s), 63 experiment(s), 63 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 EGAD00001002444

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

  ChIP-Seq data for 7 CD8-positive, alpha-beta T cell sample(s). 38 run(s), 38 experiment(s), 38 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 EGAD00001002455

• BLUEPRINT RNA-seq of Epigenetic programming during monocyte to macrophage differentiation and trained innate immunity

  Monocyte differentiation into macrophages represents a cornerstone process for host defense. Concomitantly, immunological imprinting of either tolerance or trained immunity determines the functional fate of macrophages and susceptibility to secondary infections. Transcriptomes and epigenomes in four primary cell types: monocytes, in vitro differentiated naïve, tolerized and trained macrophages were characterized. Inflammatory and metabolic pathways were modulated in macrophages, including decreased inflammasome activation, and pathways functionally implicated in trained immunity were identified. Strikingly, β-glucan training elicits an exclusive epigenetic signature, revealing a complex network of enhancers and promoters. Analysis of transcription factor motifs in DNase I hypersensitive sites at cell-type specific epigenetic loci unveiled differentiation and treatment specific repertoires. Altogether, this study provides a resource to understand the epigenetic changes that underlie innate immunity in humans.

  Study EGAS00001000953

• RNA-seq, WGS and WES of Hepatocellular carcinomas, enriched in fibrolamellar carcinomas

  DNAJB1-PRKACA fusion is a specific driver event in fibrolamellar carcinoma (FLC), a rare subtype of hepatocellular carcinoma (HCC) occurring in adolescents and young adults. In older patients, molecular determinants of HCC with mixed histological features of HCC and FLC (mixed-FLC/HCC) remain to be discovered.This study aims to molecularly caracterize mixed fibrolamellar hepatocellular carcinoma (mixed FLC/HCC) within the molecular liver tumors diversity, in respect of pure FLC and different HCC subtypes. This study reunites WGS data (n=3, Tumoral/Non-Tumoral pairs), WES data (n=43, Tumoral/Non-Tumoral pairs) and RNAseq data (n=66, Tumoral only). On a trascriptomic level, most of the mixed-FLC/HCC RNAseq clustered in a robust subgroup of tumors all showing mutation or translocation inactivating BAP1 that codes for the BRCA1 associated protein-1.

  Study EGAS00001003837

• Single cell eQTL map of T cell activation

  We isolated naive and memory CD4+ T cells from 119 healthy individuals and stimulated the cells using anti-CD3/anti-CD28 coated beads. We profiled gene expression using single cell RNA-seq (10X-Genomics 3’ v2 kit) at resting state and three time points of activation (16h, 40h and 5 days post stimulation) and mapped expression quantitative trait loci.

  Dataset EGAD00001008197

• SiMSen‑Seq

  High resolution amplicon-bases sequencing o screen for PIK3CA hot spot mutations

  Dataset EGAD50000001668

• H3K27ac ChIP-seq in a selected group of AML patients

  ChIP-seq data were generated for a number of selected patients to investigate changes in enhancer and promoter regions. ChIP was performed as described previously with slight modifications27. Briefly, cells were crosslinked with 1% formaldehyde for 10 minutes at room temperature and the reaction was quenched with glycine at a final concentration of 0.125 M. Chromatin was sheared using the Covaris S220 focused-ultrasonicator to an average size of 250–350 bp. A total of 2.5 µg of antibody against H3K27ac (Abcam, ab4729) was added to sonicated chromatin of 2 × 106 cells and incubated overnight at 4 °C. Protein A sepharose beads (GE healthcare) were added to the ChIP reactions and incubated for 2 h at 4 °C. Beads were washed and chromatin was eluted. After crosslink reversal, RNase A and proteinase K treatment, DNA was extracted with the Monarch PCR & DNA Cleanup kit (NEB). Sequencing libraries were prepared with the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB) according to the manufacturer’s instructions. The quality of dsDNA libraries was analyzed using the High Sensitivity D1000 ScreenTape Kit (Agilent) and concentrations were assessed with the Qubit dsDNA HS Kit (Thermo Fisher Scientific). Libraries were single-end sequenced on a HiSeq 4000 (Illumina). ChIP-seq reads were aligned to the human reference genome build hg19 with bowtie

  Dataset EGAD00001007582

• Dataset for WGS and RNA samples of acute myeloid leukemia with MNX1 expression

  This dataset contains 10 samples of WGS, ATAC, 4C and RNAseq samples of patients with acute myeloid leukemia. The sequencing was performed on Illumina HiSeq 4000, 2000 and HiSeq X using Illumina TruSeq Nano DNA and Agilent Strand Specific RNA Kits. The sequencing was always paired.

  Dataset EGAD50000001098

• Single-cell RNA-sequencing of CSF cells and PBMCs from individuals with neurological disorders

  We undertook single-cell RNA sequencing to establish gene expression and lymphocyte receptor sequences of CSF and peripheral blood leukocytes derived from patients with a variety of inflammatory, infectious and non-inflammatory neurological disorders. The aim of the study was the investigation of functional changes of the cells involved in neuroinflammatory responses.

  Dataset EGAD50000001173

• Single-cell transcriptome sequencing

  Raw reads from single-cell RNA-sequencing of peripheral blood of five TET2 mutation carriers as well as three non-carrier family members. Single-cells were captured into 10x barcoded gel beads and RNA-sequencing library preparation was done using Chromium Single Cell 3' v2 chemistry (10x Genomics, Pleasanton, CA, USA). Sequencing was performed as recommended with 98bp length of read 2 using HiSeq4000 sequencer.

  Dataset EGAD00001004778

• Transcriptome profiling of human lung fibroblasts in COPD

  Primary lung fibroblast were isolated from well-matched control donors (no COPD, n=3) and patients with COPD (GOLD stage I-IV, n=8). Total RNA of cultured human lung fibroblast were isolated at passage 3 and rRNA was depleted. 75 bp single-end reads were generated from RNA libraries on Illumina NextSeq 500.

  Dataset EGAD00001009405

• Single-cell RNA sequencing generated from 11 glioma patient samples

  This dataset includes paired-end fastq files from 6 IDH-mutant, 5 IDH-wild-type glioma patient samples of unmatched initial and recurrent timepoints profiled using single-cell RNA sequencing.

  Dataset EGAD00001007772

• Transcript read counts derived from RNA sequencing data collected for NABUCCO cohort 1 (NCT03387761)

  Data from NABUCCO cohort 1 (NCT03387761). This dataset includes the Transcript read counts derived from the RNA sequencing data. The samples are pre-treatment (n=18) and post-treatment (n=18), and not all samples are paired. The data processing pipeline can be found on the Methods section from the Nature Medicine paper (https://doi.org/10.1038/s41591-020-1085-z)

  Dataset EGAD00001006854

• FASTQ files of the cell-free RNA from the maternal blood

  cell-free RNA from the maternal plasma obtained at round the 12, 20, 28 and 36 week gestational age from the cases of preeclampsia combined with fetal growth restriction (n=39) and their matched controls (n=156). All samples are from the Pregnancy Outcome Prediction (POP) study.

  Dataset EGAD00001015416

• RNA expression profiling of melanoma patient-derived xenograft

  Patient-derived xenografts (n=96) were derived from metastatic melanoma patients. RNA expression profiling will be preformed to study 1. HLA-typing and 2. the effect of the tumour microenvironment on tumour growth This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002230

• Immune-Related Adverse Events after Immune Checkpoint Blockade-Based Therapy are Associated with Improved Survival in Advanced Sarcoma

  Immune activation after immune checkpoint blockade (ICB) can lead to a unique group of adverse events (AEs), which have been associated with improved outcome in many carcinomas. The association between immune-related AEs (irAEs) and outcome in patients with sarcoma is not well studied. This study has two components: (1) a retrospective clinical study of irAEs among 131 patients treated with ICB at Memorial Sloan Kettering Cancer Center and (2) an analysis of a subset of these patients who have whole transcriptome sequencing available for analysis. In the clinical study portion, we reviewed a cohort of patients with advanced sarcoma treated on prospective clinical trials utilizing ICB-based therapy between April 2017 and January 2020. The objectives were to pool the safety data from each trial, identify irAEs, and correlate them with best overall response by RECIST 1.1, progression-free survival (PFS) and overall survival (OS). Association of irAEs with response and survival was assessed using a Cox regression model. A total of 131 patients were included. Forty-two (32%) had at least one irAE of any grade and 16 (12%) had at least one Grade ≥ 3 irAE. On Cox analysis adjusting for clinical covariates that were significant in the univariate setting, the hazard ratio (HR) for an irAE (HR 0.662, 95% CI 0.421 - 1.041) approached, but did not reach statistical significance for PFS (P = 0.074), while patients had a significantly lower HR for OS (HR 0.443, 95% CI 0.246 – 0.798; P = 0.007) compared to those without or before an irAE. Tumor samples with available RNA sequencing data were stratified by presence of an irAE to identify patterns of differential gene expression. In total, 71 unique patients had RNA sequencing available for analysis. The transcriptomes of 31 of these patients have been previously uploaded to dbGaP (phs002852), leaving 36 unique transcriptomes that have not been previously publicly shared. Three patients did not agree for their sequencing to be publicly shared. Therefore, 33 unique patient samples will be uploaded to this dbGaP study. Overall, this study found that gene expression profiling on baseline tumor samples from patients who had an irAE had higher numbers of tumor-infiltrating dendritic cells, CD8+ T cells, and regulatory T cells as well as upregulation of immune and inflammatory pathways.

  Study phs003284

• Rna_expression

  RNA expression microarray

  Dataset EGAD00010000869

• Lipomatous tumors with 12q amplification

  RNA sequencing of 20 tumors.

  Dac EGAC50000000036

• RNA sequencing of AD, MCI and control ONS cells

  Study EGAS00001006594

• The Extracellular RNA Quality Control (exRNAQC) study (phase 2)

  Study EGAS00001006499

• The Extracellular RNA Quality Control (exRNAQC) study (phase 1)

  Study EGAS00001005263

• Complete sequencing dataset of study EGAS00001002795

  Dataset EGAD00001004572

• DAC IBMsnRNAseq

  This DAC manages the data access to our snRNA-seq and spatial transcriptomic data set on healthy and inflamed human skeletal muscle.

  Dac EGAC50000000220

• DLBCL cfDNA seq

  Sequencing dataset of 290 DLBCL and HGBL cfDNA samples of paired end 150bp sequencing runs.

  Dataset EGAD50000000602

• NABEC Exome-Seq

  Exome sequencing of North American Brain Expression Consortium (NABEC) subject.

  Dataset EGAD00001002886

• Centroblast Cohort

  miRNA-seq Cohort of 15 Benign Centroblasts

  Dataset EGAD00001001075

• RNA sequencing of primary B-cells infected with EBV virus

  Study EGAS00001007187

• Total RNA-Sequencing of patient-derived xenograft models of myxoid liposarcoma

  Study EGAS00001004901

• Bulk-tissue RNA-sequencing of anterior cingulate cortex samples derived from Lewy body disease patients

  Parkinson’s disease (PD), Parkinson’s disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular “window” of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.

  Study EGAS00001005305

• Bulk-tissue RNA-sequencing paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from post-mortem neuropathologically-confirmed control individuals.

  Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

  Study EGAS00001006380

• Renal_habitat_WXS

  This project aims at correlating genetic diversity (as well as transcriptomic, metabolic and immune diversity) observed in multiple targeted tumour biopsies and circulating tumour DNA with heterogeneity on multiparametric MRI. To this end, 20 patients with RCC (15 patients with localised/locally advanced disease and 5 with mRCC) will undergo multiparametric MRI including metabolic assessment by 13C-MRSI as well as measurements of tumour perfusion, oxygenation and water diffusion. Based on unique combinations of these imaging parameters, tumour habitats will be defined through cluster mapping. Intra-operative biopsy and immediate freezing of tumour cores from different imaging habitats will be performed. Multiregional ex-vivo tumour biopsies will be spatially registered to the MR data by using 3D printed tumour moulds generated from high-resolution volumetric sequences. These moulds allow slicing tumour specimens in identical planes to the MR acquisition and increase the accuracy of tissue sampling. The biopsies will then undergo molecular profiling such as metabolomic and RNA sequencing (including mRNA immune profiling), as well as DNA sequencing to assess driver and clonal mutations in different tumour habitats and guide design of appropriate cfDNA probes. The targeted cfDNA capture panel will be employed to determine the ability of cfDNA to capture the full genetic diversity observed in the primary tumour and to relate cfDNA to various imaging habitats in both renal and pancreatic cancer. Data integration will be undertaken between imaging habitats and molecular profiles for each region.

  Study EGAS00001003703

• Single_cell_characterization_of_T_cell_lymphoma_

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas.

  Study EGAS00001005750

• Exceptional Responders Initiative

  The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional response is defined as achievement of either a complete response or a partial response for at least 6 months duration in a trial or treatment where the overall response rate is < 10%. The hope is to discover underlying molecular features that can be further investigated and may eventually predict benefit from a given drug or class of drugs for a particular patient. This pilot project will successfully characterize approximately 100 cases of tumor tissue and, when available, case-matched germline DNA. All samples will undergo whole exome sequencing, and cases with sufficient nucleic acids will undergo additional analyses (e.g. whole genome sequencing, mRNA-sequencing, mi RNA sequencing, promoter methylation analysis, SNP etc). Each case will be annotated with demographic and clinical information, along with follow-up information minimally sufficient to correlate molecular profiles with response. Both retrospective and prospective collections will be considered. The project will also accept sequencing data and clinical data from patients who have had sequencing performed outside of this project. All data will be de-identified and placed in a controlled access database so other investigators may use them for additional insights. Clinically annotated tissue specimens meeting the criteria will be provided by groups participating in the Exceptional Cases Initiative to a Biospecimen Core Resource (BCR), which will perform quality control on the tissues, and will use a standard operating procedure to isolate nucleic acids. The nucleic acids will be shipped to a sequencing center to perform whole exome sequencing and analysis. These findings will be made available to the broader cancer research community in a controlled access database.

  Study phs001145

• Identification of Somatic Changes in Tumors from Fanconi Anemia Patients

  This study focused on the identification of genomic signatures present in tumors from Fanconi anemia patients. Fanconi anemia is a rare hereditary disease caused by defects in DNA interstrand crosslink repair. The primary patient phenotypes are bone marrow failure and cancer predisposition. We concentrated on squamous cell carcinomas and used whole genome Illumina sequencing (22 tumor samples and 12 normal samples) , whole exome sequencing (37 tumor samples and 33 normal samples), PacBio long-read whole-genome (nine tumor and four normal samples),10X linked-read whole-genome sequencing, 10x single-cell/nuclei 3’ RNA sequencing (three tumor samples), 10x Visium spatial transcriptomics (one tumor sample), EPIC 850K methylation array (six samples), and bulk RNAseq (six tumor samples) to identify somatic changes present in tumors. The study aimed to identify changes that drive tumorigenesis in Fanconi anemia, give insight into the early development and aggressive nature of these tumors, and suggest therapeutic avenues for these patients. Another goal was to compare the genomic changes identified in tumors from Fanconi anemia patients with sporadic head and neck cancers. This study demonstrated that the tumors from Fanconi anemia individuals are generally not driven by HPV. Instead, they acquire mutations in the p53 tumor suppressor. The main characteristic of the tumors is the presence of a high number of structural variants in the form of small deletions, unbalanced translocations, and fold-back inversions, which often form complex rearrangements. These rearrangements result in amplifications and deletions of a multitude of oncogenes and tumor suppressors, respectively. The study also shows that lack of the Fanconi anemia DNA repair pathway leads to an epithelial-to-mesenchymal transition in a mouse model and in human samples that may influence the behavior of tumors in Fanconi anemia. Finally, comparing genomic changes in Fanconi anemia and sporadic tumors of the head and neck, we propose that the structural variants present in sporadic cancer result in a functional deficiency of the Fanconi anemia DNA repair pathway.

  Study phs002652

• Automated machine-learning approach for next generation profiling of sequence alterations, mutation burden, microsatellite instability, and structural variants in human cancers

  Sequence and structural alterations together with tumor mutation burden (TMB) and microsatellite instability (MSI) have been identified as biomarkers for the determination of response to targeted and immune checkpoint inhibitor therapies. However, widespread clinical adoption of these biomarkers has historically been limited due to barriers such as evidence of clinical utility and reimbursement. We have developed 2.2 Mb targeted NGS system and an automated machine-learning analysis approach (PGDx elio™ tissue complete, ETC) that has been FDA cleared for examination of 500+ cancer-related genes and 68 mononucleotide repeats for identification of sequence and structural alterations, TMB, and MSI in solid cancers in a clinical setting. We designed and trained this approach using sequence data from 4,174 cancers and >124,000 in silico alterations and evaluated the methodology in >2,550 tumor or non-cancerous normal samples. Independent analyses of ETC sequence changes in 440 formalin fixed paraffin embedded (FFPE) tumor or cell line samples using MSK-IMPACT™, FoundationOne®, and ddPCR revealed a positive percent agreement (PPA) >97% with high sensitivity as low as 3% mutant allele fraction. We observed high concordance between panel-wide and whole-exome TMB for 307 pan-cancer FFPE tumors (Pearson r=0.95, p < 0.0001) using samples with ≥20% tumor cellularity. Comparison of the mutation context and repeat-based MSI approach in ETC with a multiplex MSI PCR assay in 223 samples revealed a PPA of 99% and negative predictive agreement (NPA) >99%. We confirmed the accuracy and precision of TMB and MSI measurements across three independent laboratories (CV of <5% and average PPA >99%, respectively). Finally, evaluation of amplifications and translocations against DNA and RNA-based approaches exhibited >98% NPA and PPA of 86% and 82% respectively. These results demonstrate high analytical performance for determination of sequence and structural changes, TMB, and MSI using a targeted NGS panel and provide a scalable and facile approach for evaluating these biomarkers in a clinical laboratory.

  Study EGAS00001005556

• scRNA-seq of glioblastoma cells alone or after co-culture with cerebral organoid cells

  Dataset EGAD00001005315

• ChIPseq data

  ChIPseq data for CLL patients

  Dataset EGAD00001008665

• Dataset of PPB

  Whole exome sequencing and miRNA-seq data of PPB.

  Dataset EGAD00001000822

• ChIPseq data

  ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

  Dataset EGAD00001003258

• Bulk RNAseq of cultured fibroblasts

  Fastq files from bulk RNAseq of fibroblasts after culture and facs sorting (N=9). Sorted FAP+ CAF cells RNAs were extracted using Qiagen miRNeasy Kit (Qiagen, #217004) according to the manufacturer's instructions. Verification of RNA integrity and quality was performed using the Agilent RNA 6000 nano Kit (Agilent Technologies, #5067-1511). cDNA libraries were prepared using the TruSeq Stranded mRNA Kit (Illumina, #20020594) followed by sequencing on NovaSeq (Illumina).

  Dataset EGAD50000000323

• RNAseq of preneoplasia lung adenocarcinoma

  RNA sequencing of 168 pulmonary samples including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=38), adenocarcinoma in situ (AIS, N=22), minimally invasive adenocarcinoma (MIA, N=19) and invasive lung adenocarcinoma (ADC, N=38) and adjacent lung tissues (Normal, N=62).

  Study EGAS50000000271