12304 results for "cancer rna-seq"

in 47.72 milliseconds.

• Exploiting immune cell receptor information to quantify index switching in single cell transcriptome sequencing experiment

  By offering high sequencing speed and ultra-high-throughput at a low price, Illumina next-generation sequencing platforms has been widely adopted in recent years. However, an experiment with multiplexed library could be at risk of molecular recombination, known as “index switching”, which causes certain amount of reads to be assigned to an incorrect library. It is reported that a new advance, exclusion amplification (ExAmp) in conjunction with the patterned flow cell technology introduced on HiSeq 3000/HiSeq 4000/HiSeq X sequencing systems, potentially suffers from a higher rate of index switching than conventional bridge amplification. We took advantage of the diverse but highly cell-unique expression of immune cell receptors to quantify index switching on single cell RNA-seq data that were sequenced on HiSeq 3500 and HiSeq 4000. By utilizing the unique T-cell receptor (TCR) expression, we could quantify the spread-of-signal from many different wells (n=51 from total three batches) due to index switching. We used TraCer to reconstruct full-length TCR from all samples, and then used Kallisto to quantify TCR gene expression. We found index switching in all three batches of experiments investigated. The median percentage of incorrectly detected markers was estimated to be 4.2% (interquartile range (IQR): 2.0%-8.7%). We did not detect any consistent pattern of certain indices to be more prone for switching than others, suggesting that index switching is a stochastic process. We confirm that index switching is a problem that affects all samples run in multiplexed libraries on Illumina HiSeq 3500 and HiSeq 4000 platforms.

  Study EGAS00001002911

• Monocyte and macrophage lipid accumulation results in downregulated type-I interferon responses

  Macrophages are critical components of atherosclerotic lesions and their pro- and anti-inflammatory responses influence atherogenesis. Type-I interferons (IFNs) are cytokines that play an essential role in antiviral responses and inflammatory activation and have been shown to promote atherosclerosis. Although the impact of type-I IFNs on macrophage foam cell formation is well-documented, the effect of lipid accumulation in monocytes and macrophages on type-I IFN responses remains unknown. Here we examined IFN stimulated (ISG) and non-ISG inflammatory gene expression in mouse and human macrophages that were loaded with acetylated LDL (acLDL), as a model for foam cell formation. We found that acLDL loading in mouse and human macrophages specifically suppressed expression of ISGs and IFN-β secretion, but not other pro-inflammatory genes. The downregulation of ISGs could be rescued by exogenous IFN-β supplementation. Activation of the cholesterol-sensing nuclear liver X receptor (LXR) recapitulated the cholesterol-initiated type-I IFN suppression. Additional analyses of murine in vitro and in vivo generated foam cells confirmed the suppressed IFN signalling pathways and suggest that this phenotype is mediated via downregulation of interferon regulatory factor binding at gene promoters. Finally, RNA-seq analysis of monocytes of familial hypercholesterolemia (FH) patients also showed type-I IFN suppression which was restored by lipid-lowering therapy and not present in monocytes of healthy donors. Taken together, we define type-I IFN suppression as an athero-protective characteristic of foamy macrophages. These data provide new insights into the mechanisms that control inflammatory responses in hyperlipidaemic settings and can support future therapeutic approaches focusing on reprogramming of macrophages to reduce atherosclerotic plaque progression and improve stability.

  Study EGAS00001005955

• Identification of the dismal subtype of B-ALL with dysregulation of CDX2 and UBTF

  PURPOSE: Advance in the knowledge of genomic basis of B-cell acute lymphoblastic leukemia (B-ALL) has changed the treatment approaches and diagnostic assays and improved their outcome. Despite the recent development of next generation sequencing approaches, there are still some cases that their drivers are unknown. We aimed to define and describe the characteristics of an additional B-ALL subtype by integrating genomic, transcriptomic, and epigenomic approach. METHODS: More than 2,000 B-ALL cases of RNA-seq data from from Eastern Cooperative Oncology Group (ECOG) and the American College of Radiology Imaging Network (ACRIN) (n=764), the Children's Oncology Group (COG), Munich Leukemia Laboratory (MLL) (n=280), and other several collaborators were analyzed to identify the new subtype. Whole genome sequence was performed to detect mutations, stractural variants, and copy number alterations. The tridimentional analysis of chromatin was performed with HiChIP. RESULTS: We identified the new high-risk B-ALL subtype “CDX2/UBTF” that exhibited unique gene expression profiles, which is enriched in female (77.3%), adolescents and young adults (52.6%), and relapse cohort (3.3%). CD10 negativity and IgM positivity are hallmarks of this subtype with NTRK3 expression that can be an effective marker at diagnostic approach and a potential targeted therapy with TRK and mulkinase inbitors, larotrectinib and entrectinib. Genetically, two different alterations define CDX2/UBTF. One is UBTF-ATXN7L3 fusions caused by deletion of 17q21.31, and the other is extopic CDX2 expression through the enhancer hijacking mechanism induced by deletion of 13q12.2. Other genomic features include gain of 1q and PAX5 rearrangement (PAX5-ZCCHC7), might induce upregulation of histone cluster genes and PAX5. CONCLUSION: We described the novel B-ALL subtype “CDX2/UBTF” that has unique clinical and genomic characteristics.

  Study EGAS00001005863

• Melanoma Genome Sequencing Project

  Melanoma is the most prevalent cause of skin cancer morbidity and mortality. In order to characterize the full range of somatic mutations that may drive the growth of melanoma, we are sequencing tumor and normal DNA from a set of roughly 150 melanomas. For the majority of samples (approximately 90% of the cases), mutations in protein coding genes will be assessed in the exonic DNA of tumor-normal pairs using hybrid capture and paired-end DNA sequencing. For a few of DNA samples (approximately 10% of the cases), the entire genomes will be analyzed to assess the possible contribution of complex structural rearrangements contributing to oncogenesis. The sequencing data are supplemented by copy number profiling on the same tumors using high-density SNP arrays. Integration of these approaches will enable the unbiased and comprehensive characterization of both known and novel recurrent DNA alterations that arise in melanoma.Versions 1-4 contain whole exome and bulk RNA sequencing data for patients with melanoma.Version 5 - We compared somatic genomic profiles from matched pre-treatment and post-resistance tumor biopsies in patients with metastatic melanoma who exhibited heterogeneous responses to immune checkpoint inhibitors (ICIs). This version includes whole exome sequencing (WES) data from 14 patients.

  Study phs000452

• The PEMDAC phase 2 study of pembrolizumab and entinostat in patients with metastatic uveal melanoma

  Patients with metastatic uveal melanoma (UM) have an abysmal prognosis. Preclinical studies have suggested that epigenetic therapy could enhance immunogenicity of cancer cells. Here we test if epigenetic therapy would enhance PD-1 immunotherapy in patients with metastatic UM. We report the results of the PEMDAC phase 2 clinical trial (n=29; NCT02697630) where the HDAC inhibitor entinostat was combined with the PD-1 inhibitor pembrolizumab in patients with metastatic UM. The primary endpoint was objective response rate (ORR), and was met with an ORR of 14%. The clinical benefit rate at 18 weeks was 28%, median progression free survival was 2.1 months and the median overall survival was 13.4 months. Toxicities were manageable, and there were no treatment-related deaths. Extensive genomics studies were performed using DNA/RNA and single cell sequencing and flow cytometry. Objective responses and prolonged survival were seen in three patients with BAP1 wildtype tumors, and in one patient with an iris melanoma that exhibited a UV signature. Longer survival also correlated with low baseline ctDNA levels or LDH. In conclusion, HDAC inhibition and anti-PD1 immunotherapy results in durable responses in a subset of patients with metastatic UM. Further exploration of combined immunotherapy and epigenetic therapy in metastatic UM is warranted.

  Study EGAS00001005478

• Ewing and Ewing-like-sarcoma tumoroid biobank reveals distinct drug sensitivities: translocation makes the difference

  Ewing sarcoma (ES) and Ewing-like sarcomas (ELS) are highly aggressive tumors found in soft tissues and bone of mostly children and young adults. Despite being different in many aspects, including genetics, possible cell-of-origin, and pathology, patients with any of these entities all receive the same therapeutic regimen. Currently, there is a lack of preclinical cell models that can predict drug response of these entities in vitro. Here, we describe a pediatric ES and ELS tumor organoid (tumoroid) biobank containing tumoroids with different translocations, including EWSR1::FLI1, EWSR1::ERG, CIC::DUX4, and BCOR-rearrangements. Using histology, whole genome sequencing and RNA sequencing, we demonstrate that these tumoroids retain key properties of their matching patient tumors. In addition, we traced tumor subclones in the tumoroids across patient-matched longitudinal samples, which showed that cellular heterogeneity was maintained. We subsequently performed drug screening on the tumoroid models, which unveiled entity-specific drug sensitivity to various cytotoxic compounds and targeted compounds, including MCL-1 inhibitors for CIC::DUX4 sarcomas. Taken together, this newly established ES and ELS patient-derived tumoroid biobank generates a unique source of material for future basic cancer research and drug-screening.

  Study EGAS00001007941

• Longitudinal evaluation of serum microRNAs as biomarkers for neuroblastoma burden and therapeutic p53 reactivation

  Accurate assessment of treatment response and residual disease is indispensable for the evaluation of cancer treatment efficacy. However, performing tissue biopsies for longitudinal follow-up poses a major challenge in the management of solid tumours like neuroblastoma. In the present study, we evaluated whether circulating miRNAs are suitable to monitor neuroblastoma tumour burden and whether treatment-induced changes of miRNA abundance in the tumour are detectable in serum. We performed small RNA sequencing on longitudinally collected serum samples from mice carrying orthotopic neuroblastoma xenografts that were exposed to treatment with idasanutlin or temsirolimus. We identified 57 serum miRNAs to be differentially expressed upon xenograft tumour manifestation, out of which 21 were also found specifically expressed in the serum of human high-risk neuroblastoma patients. The murine serum levels of these 57 miRNAs correlated with tumour tissue expression and tumour volume, suggesting potential utility for monitoring tumour burden. In addition, we describe serum miRNAs that dynamically respond to p53 activation following treatment of engrafted mice with idasanutlin. We identified idasanutlin-induced serum miRNA expression changes upon one day and 11 days of treatment. By limiting to miRNAs with a tumour-related induction, we put forward hsa-miR-34a-5p as a potential pharmacodynamic biomarker of p53 activation in serum.

  Study EGAS00001006678

• Stratifying and Targeting Pediatric Medulloblastoma through Genomics

  In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

  Study EGAS00001000273

• Genetic architecture of microRNA expression and its link to complex diseases in the Japanese population

  We mapped microRNA expression quantitative trait loci by quantifying microRNA expression using small RNA sequencing.

  Study JGAS000504

• Transcriptomic landscape of C1498 cells

  We characterized the mutations and gene fusion events (at RNA level) of C1498 cells by whole transcriptome sequencing analysis.

  Study EGAS50000001674

• Star-based RNA count files

  To ellucidate gene activity in mRCC, RNA Sequencing data was generated as part of the EuroTarget study. Star aligner 2.7 was used to generate count files

  Dataset EGAD50000002296

• Dataset of single cell RNA sequencing of peripheral blood HSPC

  Here, we conducted peripheral blood CD34+ cells single-cell RNA sequencing of patients with sickle cell anemia (n=4) and healthy donors (n=3)

  Dataset EGAD50000001522

• small RNA sequencing for 6 patients

  small RNA sequencing data for 3 patients with type 2D diabetes, and 3 patients with non diabetes. Single end sequencing was performed with read length of 100.

  Dataset EGAD50000001259

• RNA sequencing of SCLC tumor/normal sample pairs and cell lines

  RNA sequencing of SCLC tumor/normal sample pairs and cell lines

  Dataset EGAD00001000223

• GermCellTumour

  DNA and RNA sequencing across a number of different germ cell tumours, included mixed components within the same tumour.

  Study EGAS00001003457

• Transcriptome - MBD4-deficient AML

  Transcriptome sequencing for WEHI-AML-1 and WEHI-AML-2. RNA libraries were generated using the Illumina TruSeq RNA Sample Preparation Kit v2 and sequenced on an Illumina HiSeq2500.

  Dataset EGAD00001003569

• RNA sequencing analysis of lymphoblastoid cell lines with wildtype or defective Epstein-Barr virus

  Lymphoblastoid cell lines established using either wildtype or BALF5-deficient Epstein-Barr virus were analyzed by RNA sequencing.

  Dataset EGAD00001004297

• Multi-omics profiling of PSCCE

  We performed whole-exome sequencing, RNA sequencing and immunohistochemistry profiling of primary small cell carcinomas of the esophagus.

  Study EGAS00001004889

• RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases

  RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases. This dataset includes a total of 29 samples.

  Dataset EGAD00001005765

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

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 1.

  Dataset EGAD00001007697

• Single-cell RNA and TCR sequencing of PBMC from patients with uveal melanoma

  Single-cell RNA and TCR sequencing of PBMC from patients with uveal melanoma.

  Dataset EGAD00001007913

• Patient TSO500 RNA

  Original patient tumours from which PDX models were derived. TruSight Oncology RNA panel for 2 samples, sequenced over 4 lanes each, gave 8 pairs of fastq files

  Dataset EGAD00001009659

• Single-cell RNA sequencing

  Single-cell RNA sequencing on 10 antrum and 4 body gastric biopsies

  Dataset EGAD00001010166

• PrevANZ RNAseq dataset

  Whole blood samples collected at baseline and week 12 in PAXgene Blood RNA tubes. RNA sequencing on the Illumina NovaSeq 6000 System generated 150 base pair length paired end reads.

  Dataset EGAD00001011069

• Single-cell RNA sequencing of CML patients

  This dataset contains 15 single-cell RNA sequencing samples from 6 CML patients before and after TKI-cessation. The raw data is available as fastq files.

  Dataset EGAD00001012842

• Single-cell RNA-seq analysis for COVID19 patients

  While SARS-CoV-2 infection causes mild respiratory disease in most individuals, a small group of patients develops severe COVID-19. Dysfunctional innate immune responses have been identified to contribute to differences in COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell epigenetics, transcriptomic, and genetics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified 41.3% of significantly up-regulated genes in hospitalized COVID-19 patients potentially induced by differential chromatin accessibility. Sub-clustering and motif-enrichment analyses of monocytes reveal disease condition-specific regulation by transcription factors, such as C/EBPs and SPI1, and their targets, including a long-noncoding RNA LUCAT1, which further regulates interferon responses and is associated with the need for oxygen supply of COVID-19 patients. The interaction between C/EBPs and LUCAT1 was validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in promoters/enhancers of COVID-19 patients. Integrating our data with publicly available expression quantitative trait loci and chromosomal interactions indicates that ASoC SNP rs6800484-C is associated with lower expression of CCR2, which may contributeto higher viral loads in lungs and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to the innate immune responses of different COVID-19 patients.

  Study EGAS00001006560

• DIGEST: Dietary Influences on Glucuronidation, a Cross-Sectional Study of Diet and Metabolism

  Plasma samples derived from a study population of control participants (DIGEST: Dietary Influences on Glucuronidation, a Cross-Sectional Study of Diet and Metabolism) were used to profile miRNA circulating in plasma. RNA was isolated from plasma samples using the Qiagen miRNeasy kit. Libraries were prepared using Illumina TruSeq Small RNA Library Prep Kit. Libraries were sequenced on a HiSeq. Data from this sequencing project will be available through dbGaP.

  Study phs003223

• Multi-omics characterization of KMT2A-rearranged infant acute lymphoblastic leukemia

  KMT2A gene rearranged (KMT2A-r) infant acute lymphoblastic leukemia (ALL) is the most refractory subgroup of childhood leukemia, whose molecular heterogeneity remains largely unknown. To better understand the molecular pathogenesis of KMT2A-r infant ALL, we comprehensively analyzed the RNA sequencing, methylation array, whole exome sequencing, single-cell RNA sequencing data of diagnostic samples of KMT2A-r infant ALL. We also performed ChIP sequencing of infant ALL-derived cell lines.

  Study JGAS000385

• Effect of inflammation on human hematopoietic stem cells in a xenograft model

  Transcriptional heterogeneity in cord blood HSCs was investigated by 10X single-cell Multiome (RNA+ATAC). To investigate the effect of inflammatory stress on human HSC, 10X single-cell Multiome profiling of cells from xenografted mice recovered from repeated inflammatory challenge was performed. To investigate transmission of inflammatory stress-related molecular programs from HSC to their progeny, 10X single-cell RNA sequencing was performed on committed progenitor and myeloid cells from the same mice.

  Study EGAS50000001624

• (ChIP-seq) Shifted assembly and function of mSWI/SNF family subcomplexes underlie targetable dependencies in endometriod carcinomas

  ChIP-seq data of 18 samples from the VOA1066 undifferentiated endometrial carcinoma cell-line with various pulldowns: BRG1 (abcam, ab110642, GR3255117-11), BRD9 (CST, E4Q3F, Lot 1), ARID1A (CST, D2A8U, Lot 4), SS18 (CST, D6I4Z, Lot 1), PBRM1 (CST, D3F70, Lot 3), GLTSCR1 (CST, E6I3A, Lot 1), H3K27ac (CST, C36B11, Lot 16), or none. Sample were either untreated or received doxycycline-induced ARID1A treatment. Samples were sequenced on Illumina NextSeq 500 using 37 bp single-end sequencing parameters. Raw fastq files, along with processed bigwig files containing normalized genomic coverage, and called narrow peaks, are included for each sample.

  Dataset EGAD50000001473

• DAC of the 300BCG ATAC-seq data

  To investigate human population variation of trained immunity, we immunized healthy individuals with Bacillus Calmette-Guérin (BCG). This live attenuated vaccine induces not only an adaptive immune response against tuberculosis, but also triggers innate immune activation and memory. We established personal chromatin accessibility maps over a time course of BCG vaccination in 323 individuals using ATAC-seq.

  Dac EGAC50000000056

• Raw scTCR-seq data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

  Raw 10X 5' v3 scTCR-seq data (TCR) for PBMC of 3 helthy donors and two AGLCD patient timepoints as well as of LPMC of 3 Crohns disease patients and one timepoint of the AGLCD patient.

  Dataset EGAD50000002191

• Raw scRNA-seq data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

  Raw 10X 5' v3 scRNA-seq data (GEX) for PBMC of 3 helthy donors and two AGLCD patient timepoints as well as of LPMC of 3 Crohns disease patients and one timepoint of the AGLCD patient.

  Dataset EGAD50000002190

• ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze March 2013 transcriptome sequencing

  Dataset EGAD00001000356

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_LPS_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002922

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=24hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=24hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002921

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=1hr from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=1hr from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002919

• BLUEPRINT September 2016, ATAC-seq for germinal center B cell from tonsil, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for germinal center B cell from tonsil, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002917

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=6days from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=6days from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002914

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=1hr from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_LPS_T=1hr from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002913

• BLUEPRINT September 2016, ATAC-seq for germinal center B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for germinal center B cell from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002911

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002910

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=24hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=24hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002909

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=1hr from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=1hr from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002906

• BLUEPRINT September 2016, ATAC-seq for unswitched memory B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 3 sample(s) for unswitched memory B cell from venous blood, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002905

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002904

• BLUEPRINT September 2016, ATAC-seq for naive B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 3 sample(s) for naive B cell from venous blood, on Genome GRCh38. 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002902

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for monocyte RPMI_LPS_T=24hrs_RPMI_T=5days from venous blood, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002901

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_LPS_T=24hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_LPS_T=24hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002900

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002899

• BLUEPRINT: ChIP-seq for monocytes & neutrophils

  ChIP-seq for monocytes and neutrophils

  Dataset EGAD00001000676

• smRNA-seq of human post-mortem brain data of frontal lobe (Tuebingen part)

  This dataset contains smRNA-seq data from post-mortem human brain tissue of the frontal lobe of patients with FTD and healthy controls. The smRNA-sequencing was done in two parts, this dataset depicts the data generated at the DZNE Tübingen.

  Dataset EGAD00001006846

• ChIP-sequencing in human monocyte differentiation

  This dataset consists of ChIP-seq data from human monocytes, monocyte-derived dendritic cells as well as monocyte-derived cells that were subjected to siRNA treatment targeting CTCF or RAD21. ChIP-sequencing was done for H3K27, RAD21 and CTCF. In total, the data set includes 120 samples.

  Dataset EGAD00001007954

• Multiomic snRNAseq/snATACseq of human implantation sites, placenta and decidua

  This dataset is part of a study that aims to provide a spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy. This dataset contains 10x multiome snRNA-seq/snATAC-seq from human implantation sites, decidual and placental samples from 8-9 PCW.

  Dataset EGAD00001010038

• 4C-seq in endometrial healthy and tumor tissues

  4C-seq performed on 10 slices (30um thick) of fresh frozen endometrial tissues. These tissue include 2 healthy tissues and 4 tumor tisseus (post-menopausal patients) in replicate. The library has been performed using DpnII (primary) and NlaIII (secondary) restriction enzymes. Raw single-end fastq.gz files are provided.

  Dataset EGAD00001010897

• PhIP-seq of Japanese subjects with SLE

  Bacteriophage Immunoprecipitation Sequencing (PhIP-Seq) datasets of three eHHV-6B-positive SLE patients (three technical replicates), six eHHV-6B-negative SLE patients (three technical replicates), and a negative control lacking sera or plasma (12 technical replicates; Illumina NextSeq) and sequencing data of HHV-6 peptide phage library (two technical replicates; Illumina NextSeq).

  Dataset EGAD00001015429

• Bulk RNA-sequencing data from 9 cHL cell lines

  The dataset contains raw data of 27 bulk RNA-sequencing data from 9 cHL cell lines. Paired-end fastq with polyA capture are available for each cell line.

  Dataset EGAD50000001271

• RNA-sequencing (Illumina HiSeq 4000) of tumor-stage mycosis fungoides

  This dataset contains RNA-sequencing data of tumors from 8 patients with mycosis fungoides. The data was generated using the Illumina HiSeq 4000 platform.

  Dataset EGAD00001003983

• RNA sequencing data of child-mother pairs, maternal smoking.

  RNA sequencing data of whole blood samples from smoking and non-smoking mothers and their children at gestation/birth and follow-up years.

  Dataset EGAD00001002011

• Expression profiling of pediatric brain tumors Embryonal Tumor with Multilayered Rosettes (ETMR)

  RNA was prepared using the IlluminaTruSeq RNA sample preparation kit for poly-adenylated mRNA with an average of 97.64 million reads per sample respectively

  Dataset EGAD00001004803

• Hospital for Sick Children Infant Glioma RNA Sequencing

  Whole transcriptome (n=4) and targetted RNA sequencing (n=15) .bam files of infantile glioma samples reported on at the Hospital for Sick Children

  Dataset EGAD00001005092

• RNA-Sequencing data of Human Acute Myleloid Leukemia Stem Cells

  RNA-Sequencing of 27 functionally validated LSC and blast fractions from 9 AML patients. Three healthy hematopoietic stem and progenitor cells from age-matched controls.

  Dataset EGAD00001006112

• Single-cell RNA-sequencing reveals that glioblastoma recapitulates a normal neurodevetlopmental hierarchy

  10xGenomics single-cell RNA sequencing of glioblastoma patient tumor

  Dataset EGAD00001006206

• long RNA data

  long RNA data for 27 CLL samples (8 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005968

• RNA sequencing data of pretreatment tumor biopsies of patients treated in the OpACIN-neo trial

  The dataset includes RNA sequencing on pre-treatment tumor biopsies of lymph node metastases (n=65)

  Dataset EGAD00001006731

• Single-cell RNA sequencing of 43 BALF samples from CAPA and COVID-19 patients

  Single-cell RNA sequencing of 43 bronchoalveolar lavage fluid (BALF) samples from 15 CAPA and 22 COVID-19 mechanically ventilated patients.

  Dataset EGAD00001011342

• Whole-genome and transcriptome versus panel sequencing in precision oncology: A translational-clinical comparison

  Precision oncology offers new cancer treatment options. While the use of whole-genome (WGS) and transcriptome (TS) sequencing remains often confined to specific clinical programs, molecular analysis with gene panels is more common clinical practice. Methods: Twenty patients with rare or advanced tumors sequenced by WGS/TS within the DKFZ/NCT/DKTK-MASTER program from 2015 to 2020 (MASTER 1) were additionally sequenced with the TSO500 DNA and TST170 RNA Assay by Illumina. Original Molecular Tumor Board (MTB) therapy recommendations (TR) were juxtaposed with TR from NGS panel data analysis (comparison 1). To mitigate temporal differences between the two sequencings, WGS/TS was bioinformatically reanalyzed (MASTER 2), and new TR were again compared (comparison 2). Results: In comparison 1, 47.1% of the MASTER 1’s TR and 62.8% of the panel’s TR were identical, as were 45.9% of the TR of MASTER 2 and 54.9% of the panel in comparison 2. In both MASTER 1 and MASTER 2, 26.5% and 36.1% of TR, respectively, relied on Biomarkers (BM) which were not covered by the panel. Eight of the ten MTB-derived therapy implementations were also supported in the panel, the two remaining ones being based on BM absent from the panel. Conclusions: About half of the TR in WGS/TS and panel sequencing were identical. Around a third of TR issued by WGS/TS were based on BM which are not covered by the panel, leading to therapy implementations. The presented data underscores the clinical value of comprehensive molecular analyses in individuals with advanced cancers.

  Study EGAS50000000431

• Rheumatoid Arthritis Finger Stick RNA Sequence Data

  This is a longitudinal study of 355 blood samples collected weekly from 5 patients with Rheumatoid Arthritis with varying disease activity over the course of one to five years. Blood samples were self collected from finger sticks and mailed from home. RNA was extracted, purified and sequenced.

  Study phs003179

• RNA sequencing of BCP-LBL and EM B-ALL patients samples

  In order to perform comprehensive transcriptomics and gene fusion analyses of a cohort of BCP-LBL and extramedullary (EM) manifestations of B-ALL patients, we performed RNA sequencing of 16 tissue samples from BCP-LBL and EM B-ALL patients. Because the material was available as FFPE, and had a relative low quality, we used a capture-based approach, where NGS libraries obtained from total RNA were captured in 4-plex, using a whole exome capture panel.

  Dataset EGAD50000002047

• RNA-sequencing of 82 pleural mesothelioma samples

  The dataset contains RNA-sequencing data (bam files) for 82 pleural mesothelioma samples. All the samples were sequenced on an Illumina NovaSeq 600 sequencer in paired-end mode, generating 100 nt length reads, to obtain an average of 60 million clusters for RNA. Demultiplexing was performed using Illumina bcl2fastq2. Fastq quality was assessed using fastQC (v. 0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and low-quality reads were discarded. Sequence reads were aligned to Human reference genome (UCSC genome assembly GRCh38/hg38) using STAR (v. 2.7.0b).

  Dataset EGAD50000002131

• RNA Sequencing of Control and Myotonic Dystrophy Type 1 Cells During Myogenic Differentiation

  To investigate gene expression dynamics during myogenic differentiation in myotonic dystrophy type 1, RNA was collected from immortalized control, patient-derived, and CRISPR/Cas9-corrected myoblasts at proliferation (day -2) and at days 1 and 5 of differentiation. High-quality RNA was isolated and sequenced using poly(A)-enriched or rRNA-depleted protocols, yielding 100 bp paired-end reads with high coverage. A subset of samples at day 1 underwent deeper sequencing to enhance transcriptome resolution.

  Dataset EGAD50000001647

• RNA sequencing data of pediatric ETV6::RUNX1 acute lymphoblastic leukemia (set 2)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of four ETV6::RUNX1 patients (ALLT-388, ALLT-392, ALLT-394, ALLK-114) and from a second relapse sample of one ETV6::RUNX1 patient (ALLK-104). TruSeq Stranded Total RNA Library Prep (Illumina) kit was used in library preparation, and Novogene's internal method was utilized in rRNA removal. The diagnostic and relapse tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002589

• RNA sequencing data of pediatric B-other acute lymphoblastic leukemia (set 4)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of 10 B-other ALL patients (ALLT-389, ALLT-391, ALLT-393, ALLK-101, ALLK-109, ALLK-110, ALLK-113, ALLK-115, ALLK-123, ALLK-124). TruSeq Stranded Total RNA Library Prep (Illumina) kit was used in library preparation, and Novogene's internal method was utilized in rRNA removal. The diagnostic tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002599

• Single-cell bam files and RNA sequencing of viral RNA stocks

  244 infected single-cell alveolar bam files, 48 empty well bam files, and 52 RNA sequencing of amplicons (4 SARS-CoV-2 variants with 12 batches and 4 viral variants pool samples). 244 alveolar single cells were captured over 12 experimental batches and experimental condition is written in metadata uploaded as "infected_cells_final_revision.csv". on github (https://github.com/twkim-0510/SARS-CoV-2_viral_competition). Each bam file name corresponds to the sample_name column of the metadata.

  Dataset EGAD00001009711

• Single-cell RNA-sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumouroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015708

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015714

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015718

• scRNA-seq

  Dac EGAC00001000963

• Multi-omics characterisation of immune cells in Long Covid

  We generated 10X Multiomics (paired RNA+ATAC) for Long Covid patients with up to 14 months follow-up.

  Study EGAS50000000142

• Strand-specific RNA Sequencing of initial and recurrent gliomas

  Four RNA-sequencing datasets from two patients with initial low-grade glioma and copy number alteration at IDH1 upon recurrence. Data is provided as bam files.

  Dataset EGAD00001003764

• RNA Sequencing of mCRC xenografts under cetuximab treatment.

  RNA sequencing on cetuximab treated, untreated and release samples of one metastatic colorectal xenograft. 3 cetuximab treated samples, 3 placebo and 3 release - paired fastq.

  Dataset EGAD00001005185

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

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 2 (interaction study)

  Dataset EGAD00001009724

• Whole exome and RNA sequencing of 5 samples of patient-derived xenograft (PDX).

  Whole exome and RNA sequencing of 5 samples of patient-derived xenograft (PDX). Available files are raw sequencing fastq files.

  Dataset EGAD00001010102

• Human serum small non-coding RNA sequencing

  Human small non-coding RNA sequencing of serum from sons of PCOS mothers (n=9) and sons of control mothers (n=9), see publication for details.

  Dataset EGAD00001010165

• RNA sequencing of AML with 3q26 rearrangements

  RNA sequencing of 44 AML cases with diverse 3q26 rearrangements. This dataset is complementary to EGAD00001000726 (which only contains t(3;3)/inv(3)) and to EGAD00001006123.

  Dataset EGAD00001015629

• scRNA-seq DAC

  Dac EGAC00001001559

• National Cancer Institute (NCI) Biospecimen Pre-analytical Variables (BPV) Analysis

  The Biospecimen Pre-analytical Variables (BPV) Program is a National Cancer Institute-sponsored study to systematically assess the effects of pre-analytical factors on the molecular profile of biospecimens. A robust biospecimen collection infrastructure was established to prospectively collect biospecimens using rigorous standard operating procedures to control for most variables while introducing experimental conditions to study specific biospecimen handling issues, including the cold ischemic time (delay to formalin fixation), time in formalin, freezing methods, and storage temperatures and durations. RNA and DNA from biospecimens collected under these conditions was analyzed on multiple molecular platforms. The potential effects of these pre-analytical conditions on protein integrity and detection of metabolites were also examined. Data from this study will be used to develop evidence-based biospecimen standard operating procedures and best practices for fit-for-purpose collection, processing, and storage of biospecimens. The BPV Cohort is utilized in the following dbGaP substudies. To view genotypes, analysis, expression data, other molecular data, and derived variables collected in these substudies, please click on the following sub-studies below or in the "Substudies" box located on the right hand side of this top-level study page phs001304 BPV Cohort. The substudy links will be active once they are released by dbGaP. Preanalytical Impacts on Global Metabolite Profiling - plasma (MassSpec by Metabolon) This study was to evaluate the impact of the storage temperature (s) (-80°C and LN2 vapor) and the length of storage on human plasma quality using LC-MS/MS (liquid-chromatography-mass spectrometry/mass spectrometry) based global metabolite profiling. The study includes 240 plasma samples collected from 40 donors. Investigate the effect of the delay to fixation on the proteome and phosphoproteome -FFPE (MassSpec by Caprion). The study is to do proteome and phosphoproteome analysis on Delay to fixation was carried out using FFPE tumor samples from colon and ovarian cancer patients comparing 2, 3, and 12hr delay to fixation to the 1hr time point. The study includes 100 samples 20 donors. Investigate the effect of storage conditions of tumor specimens on the proteome and phosphoproteome profiling- Frozen tissue and plasma (MassSpec by Caprion). The study was to evaluate the effects of storage conditions on tumor specimens. Plasma samples from 40 cancer patients stored at two different temperatures (-80°C and LN2) for a given period (0-2, 6-8, and 12-14 months) were evaluated. Frozen kidney tumor samples from 20 patients were compared for effects of different snap frozen (dry ice vs. LN2) and storage temperatures (-80°C and LN2). The study includes 100 tissue and 240 plasma samples from 60 donors. Preanalytical Impacts on Genomic Sequencing by Next Generation Sequencing (NGS) technology (mRNA/miRNA and WES by Expression Analysis). The goal of the study is to determine the effects of cold ischemic delay-to-fixation (4 time points) and formalin preservation (FFPE) on the nature and quality of genomic profiles using the matched freshly frozen sample as the gold standard, which including WES, RNAseq. The study includes 395 samples from 37 donors. Preanalytical Impacts on Copy Number Variation (CNV) Detection by aCGH technology (aCGH by Georgetown University). This study was to use aCGH to evaluate the effect of variation in cold ischemia time and time in formalin fixation on CNV in DNA extracted from kidney cancer specimens. The study includes 235 samples from 40 donors. Evaluation of frozen conditions on mRNA profiling by TaqMan assay (mRNA expression by Georgetown University). This study was to utilize gene expression profiling, using custom TaqMan arrays, to compare the molecular profiles of RNA from frozen tumor samples collected using two freezing methods (dry ice or LN2), two storage temperatures (-80°C or LN2 vapor), as well as Optimal Cutting Temperature (OCT) compound and non-OCT embedded. The study includes 100 samples from 20 donors. mRNA signature for stratification by cold ischemia time (mRNA expression by IBBL). The study was to determine the effects of cold ischemic time (delay-to-fixation) and formalin preservation (FFPE) on mRNA detection by Taqman assay using tumor tissue specimens from kidney, colon and ovarian cancer patients. There are160 samples from 40 donors. The Biospecimen PV cohort is utilized in the following dbGaP individual studies. To view molecular data, and derived variables collected in these individual studies, please click on the following individual studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001304 Biospecimen PV cohort. phs001634 CIT mRNA phs001635 CNV aCGH phs001636 Fixation Delay phs001637 Global Metabolite Profiling phs001638 mRNA TaqMan phs001639 NGS phs001640 Tumor Storage

  Study phs001304

• IBMsnRNAseq

  snRNA-seq and spatial transcriptomic data and analysis of healthy (CTRL) and inflamed (immune-mediated necrotizing myopathy (IMNM) and inclusion body myositis (IBM)) human quadriceps muscle. This data set includes 19 snRNA-seq (7 CTRL, 4 IMNM, 8 IBM) and 8 ST samples (3 CTRL, 2 IMNM, 3 IBM).

  Dataset EGAD50000000449

• ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  Dataset EGAD00001000355

• Human T-bet governs innate and innate-like adaptive IFN-g immunity against mycobacteria.

  We report a patient with mycobacterial disease due to inherited deficiency of the transcription factor T-bet. PBMCs from the patient was analyzed with scRNA-seq.

  Study EGAS00001004504

• Histone Acetylome-wide Association Study of Autism Spectrum Disorder

  H3K27ac ChIP-seq were performed on postmortem samples from autism spectrum disorder and matched control brains. Tissues were chosen from three brain regions: prefrontal cortex, temporal cortex and cerebellum.

  Study EGAS00001001943

• BLUEPRINT September 2016, ATAC-seq for monocyte RPMI_BG_T=24hrs_RPMI_T=5days_LPS_T=4hrs from venous blood, on Genome GRCh38

  ATAC-seq data for 1 sample(s) for monocyte RPMI_BG_T=24hrs_RPMI_T=5days_LPS_T=4hrs from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002915

• BLUEPRINT September 2016, ATAC-seq for class switched memory B cell from venous blood, on Genome GRCh38

  ATAC-seq data for 2 sample(s) for class switched memory B cell from venous blood, on Genome GRCh38. 2 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002908

• ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  Dataset EGAD00001000645

• ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  Dataset EGAD00001000650

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days B-glucan, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days B-glucan 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 EGAD00001002314

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days untreated, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days untreated 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 EGAD00001002458

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days LPS, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days LPS 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 EGAD00001002498

• BLUEPRINT September 2016, ATAC-seq for venous blood, on Genome GRCh38

  ATAC-seq data for 136 sample(s) from venous blood, on Genome GRCh38. 141 run(s), 139 experiment(s), 139 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002709

• Peripheral Blood Leukocytes 10x Genomics scRNA-seq

  This dataset contains 10x Genomics Single Cell 3’ Solution (version 2) scRNA-seq data from peripheral blood leukocytes of a single healthy donor. Data from 20939 cells were collected over 8 lanes and 2 sequencing runs.

  Dataset EGAD00001008590