11973 results for "cancer rna-seq"

in 206.82 milliseconds.

• Inference_of_B_cell_clonality_and_function_from_single_cell_RNA_seq_data

  cDNA libraries for RNA-seq will be prepared for sequencing. cDNA will be created following the Smart-Seq2 protocol, and a part of the cDNA from each cell will be used for preparation of RNA-seq libraries using the Nextera XT kit. The other part of the cDNA will be used as template for verification of the computationally recovered immunoglobulin sequences from the RNA-seq dataset by using a targeted PCR approach to generate a multiplexed sequencing library

  Study EGAS00001002963

• Comprehensive genomic characterization of early stage bladder cancer - Total RNA-seq data

  This study looks at the non muscle invasive bladder cancer subtypes based on totRNAseq data of 414 bladder cancer samples collected in different European centers within the UROMOL project. All samples were sequenced using paired reads on Illumina platforms. The reads are mapped to hg38.

  Study EGAS50000000512

• SF11977 scRNA-Seq Primary GBM IDHR132H Wildtype Female

  SF11977 single cell RNA-seq IDHR132H Wild-type GBM Female, 61 Single Cell RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005391

• SF12090 snRNA-Seq IDHR132H Wild-type Primary GBM Male

  Single Nuclei RNA-Seq Primary IDHR132H Wild-type GBM. Male, 61. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005412

• SF11964 scRNA-Seq Low Grade Glioma IDHR132H mutant Male

  Single cell RNA-Seq Low Grade Astrocytoma IDHR132H mutant. Male, 64. Single Cell RNA seq from primary astrocytoma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005396

• SF10022 snRNA-Seq Primary High-grade Glioma

  SF10022 single nuclei RNA-Seq Primary High-grade glioma. gender Male Age 65. Single Nuclei RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference

  Dataset EGAD00001005409

• Regulatory Changes in Glioblastoma Brain Tumors and Xenografts Wave 1

  Non-coding elements in our genomes that play critical roles in complex disease are frequently marked by highly unstable RNA species. Sequencing nascent RNAs attached to an actively transcribing RNA polymerase complex can identify unstable RNAs, including those templated from gene-distal enhancers (eRNAs). However, nascent RNA sequencing techniques remain challenging to apply in some cell lines and especially to intact tissues, limiting broad applications in fields such as cancer genomics and personalized medicine. Here we report the development of chromatin run-on and sequencing (ChRO-seq), a novel run-on technology that maps the location of RNA polymerase using virtually any frozen tissue sample, including samples with degraded RNA that are intractable to conventional RNA-seq. We used ChRO-seq to develop the first maps of nascent transcription in 23 human glioblastoma (GBM) brain tumors and patient derived xenografts. Remarkably, >90,000 distal enhancers discovered using the signature of eRNA biogenesis within primary GBMs closely resemble those found in the normal human brain, and diverge substantially from GBM cell models. Despite extensive overall similarity, 12% of enhancers in each GBM distinguish normal and malignant brain tissue. These enhancers drive regulatory programs similar to the developing nervous system and are enriched for transcription factor binding sites that specify a stem-like cell fate. These results demonstrate that GBMs largely retain the enhancer landscape associated with their tissue of origin, but selectively adopt regulatory programs that are responsible for driving stem-like cell properties. We also identified enhancers and their associated transcription factors that regulate genes characteristic of each known GBM subtype, and discovered a core group of transcription factors that control the expression of genes associated with clinical outcomes. This study uncovers new insights into the molecular etiology of GBM and introduces ChRO-seq which can now be used to map regulatory programs contributing to a variety of complex diseases.

  Study phs001646

• ovarian cancer sample RNA-seq

  Tissue samples were collected from 18 ovarian cancer patients. After mechanical and enzymatic dissociation, a part of these tissues underwent growth factoromics analysis. For four cases, single-cell sequencing analysis was conducted under conditions with or without estradiol. Additionally, the remains of the tissues were frozen and subsequently subjected to bulk RNA-sequencing on 17 of these samples. Sixteen cases were diagnosed as HGSOC, while two samples were clear cell or mucinous type (Extended Data Table 1). All cases were grade 3, and none had received neoadjuvant chemotherapy. - Group C1, Epithelial Ovarian Cancer(EOC) patients with estrogen responsiveness : 6 samples - Group C2, Epithelial Ovarian Cancer(EOC) patients with estrogen non-responsiveness : 10 samples - non-classified 1-sample

  Dataset EGAD50000000613

• The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

  Acute lymphoblastic leukaemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1 and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard of care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard of care diagnostics. KMT2A-rearrangements (MLL), whilst usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard of care testing in nine patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the commonest IKZF1 deletion from RNA-seq data and validated this using a RQ-PCR assay. We applied an expression classifier to identify Ph-like B-ALL patients. T-ALL proved a rich source of novel gene fusions which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.

  Study EGAS00001004212

• RNU2-2 splicing signature RNA-Seq

  This dataset is composed of RNA-Seq samples: 49 controls which are probands without snRNA rare variants and 19 affected probands with either monoallelic or biallelic variants in RNU2-2. RNA was extracted from short-term lymphocytes cultures. RNA-Seq libraries were obtained by hybrid capture of fragmented cDNA with RNA probes from exome v8 (Agilent)

  Dataset EGAD50000002045

• glioblastoma and glioblastoma stem cells (GSCs), RNA-seq and WGS

  Characterization of glioblastoma tumors and glioblastoma stem cells (GSCs) lines via RNA-seq and/or WGS profiling.

  Study EGAS00001004395

• Small-RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  Single-end 75 basepair small-RNA sequencing of 90 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. Samples were prepared using the NEXTFLEX® Small RNA-Seq Kit v3 (Bioo Scientific ). Samples were sequenced at the molecular genomics core (Peter MacCallum Cancer Centre) using 50bp single end sequencing on the Illumina NextSeq 500 (Illumina, USA). This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000501

• Whole Genome Sequencing of HCC

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome (WGS) and whole exome sequencing (WGS), integrated with DNA methylation and RNA sequencing (RNA-seq) data. The present series corresponds to 24 whole genome sequencing (12 Tumoral/Non-tumoral pairs). Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Study EGAS00001002888

• H3K27ac ChIP-seq and RNA-seq of lung neuroendocrine tumors

  H3K27ac ChIP-seq and RNA-seq of lung neuroendocrine tumors

  Dataset EGAD50000000083

• Bulk RNA-seq

  Bulk RNA-seq

  Dataset EGAD50000000135

• RNA-seq

  RNA-seq of PDXs

  Dataset EGAD00001001862

• Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome

  To evaluate the potential of an integrated clinical test to detect diverse classes of somatic and germline mutations relevant to pediatric oncology, we performed three-platform whole-genome (WGS), whole exome (WES) and transcriptome (RNA-Seq) sequencing of tumors and normal tissue from 78 pediatric cancer patients in a CLIA-certified, CAP-accredited laboratory. Our analysis pipeline achieves high accuracy by cross-validating variants between sequencing types, thereby removing the need for confirmatory testing, and facilitates comprehensive reporting in a clinically-relevant timeframe. Three-platform sequencing has a positive predictive value of 97-99%, 99% and 91% for somatic SNVs, indels and structural variations, respectively, based on independent experimental verification of 15,225 variants. We report 240 pathogenic variants across all cases, including 84 of 86 known from previous diagnostic testing (98% sensitivity). Combined WES and RNA-Seq, the current standard for precision oncology, achieved only 78% sensitivity. These results emphasize the critical need for incorporating WGS in pediatric oncology testing.

  Study EGAS00001002217

• Evaluation of triple negative breast cancer with heterogeneous immune infiltration

  This dataset contains the raw RNA-seq data (FASTQ files) of the samples used in the study title: "Evaluation of triple negative breast cancer with heterogeneous immune infiltration". The dataset is composed of 3 patients with 6 samples per patient (3 with high TILs and 3 with low TILs).

  Dataset EGAD00001010280

• RNA-seq data from iCAF (sorted) and myCAF cluster 0 (spread) CAF-S1 fibroblasts maintained in culture

  RNA-seq data from iCAF (sorted) and myCAF cluster 0 (spread) CAF-S1 fibroblasts maintained in culture

  Study EGAS00001004031

• SF11979 scRNA-Seq Primary GBM IDHR132H Wild-type Female

  Single Cell-RNA Seq IDHR132H Wild-type Primary GBM Female, 76. Single Cell RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through cellranger count (10xGenomics.)

  Dataset EGAD00001005390

• SF11136 scRNA-Seq Primary astrocytoma IDH mutant

  Single cell RNA-Seq Primary diffuse astrocytoma G2. IDH mutant, ATRX mutant. Gender Male Age 34. Single Cell RNA seq from primary astrocytoma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference through Cellranger count (10xGenomics.)

  Dataset EGAD00001005394

• RNA-Seq dataset for Genomic rearrangements in Pediatric Cancer

  RNA-Seq data for systematic gene fusion detection in Pediatric Cancer

  Dataset EGAD00001008152

• Esophageal Adenocarcinoma Organoid Genomics

  Single cell RNA-seq (scRNA-seq) of esophageal adenocarcinoma organoids to benchmark variant calling from 10X Genomics scRNA-seq data. Five EAC organoids were subjected to scRNA-seq and two included matched exome data.

  Study EGAS00001005224

• RNA-seq from human embryonic tissues (pancreas_2, tongue_2)

  Additional unpublished RNA-seq data generated in conjunction with our mulit-tissue ChiP-seq project.

  Dataset EGAD00001005115

• March 2017 data update (bam/fastq) (containing H3K27ac for CEMT_87, RNA-Seq for CEMT_141, CEMT_142, CEMT_145, CEMT_146, H3K27me3, H3K9me3, H3K4me3, H3K4me1, ChIP-Seq Input for CEMT_88, CEMT_90 and CEMT_91) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  March 2017 data update (bam/fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001003272

• RNA-Seq for SF3B1 splicing signature

  This dataset includes exome-captured RNA-Seq made with exome v8 (Agilent) - library preparation on a MAGNIS XT-HS2 starting from 100ng of RNA. RNA has been extracted from lymphocytes cultures from 2 probands with SF3B1 mutations and 14 controls. All 16 RNA-Seq were sequenced on a single HighOutput flowcell 2x75bp (NextSeq550). Raw fastq files have between 20 and 30M paired-reads.

  Dataset EGAD50000002121

• RNA-seq bam

  We established 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. Here are all available RNA Sequencing (RNA-seq) data from patient tumor, PDOX, and cell lines from our study.

  Study EGAS00001005161

• Study of Leukemia Stem Cells in B-ALL

  This study focused on the biologic properties of human B acute lymphoblastic leukemia (ALL) stem cells. We used human leukemia cell lines, primary patient specimens, and patient derived xenografts in experiments using ChIP-seq, bulk RNA sequencing, single cell RNA sequencing, and CITE-seq.

  Study phs002492

• Nrf2 transcript alterations

  WGS and RNA-seq data for Nrf2 study

  Study EGAS00001001740

• RNA-seq

  RNA-seq

  Dataset EGAD00001008995

• RNAseq from 6 organotypic co-cultures (OC cells bulk/OCSC with or without TME)

  The dataset contains samples of 6 organotypic co-cultures, assembled with patient-derived material from ovarian cancer (OC) patients. Tumor cells, both as bulk and as cancer stem cells-enriched (OCSC) populations, are cultured or not with in vitro peritoneal TME (for details see Battistini C et al, Tumor microenvironment-induced FOXM1 regulates ovarian cancer stemness, CDDis 2024). Dataset is composed by fastq file (paired end) type from bulk RNA-Seq.

  Dataset EGAD50000000523

• single cell RNA-seq of small cell lung cancer tumors

  This study investigated the transcriptomic profile of small cell lung cancer from 10 patients. Tumors were rapidly dissociated after the surgical procedure. Libraries were constructed using the VDJ NextGEM v1.1 10x Genomics Chromium kit. Samples were sequenced on a NextSeq 550 sequencer (Illumina).

  Study EGAS50000001400

• HuBMAP: High Resolution 3D Mapping of Cellular Heterogeneity Within Multiple Types of Mineralized Tissues

  The overall goal of the study was to generate 3D single cell maps of healthy human knee cartilage. Articular cartilage from the weightbearing and non-weightbearing regions of the femoral condyles in healthy knees from young donors (n=12) was analyzed by bulk RNA-sequencing (RNA-Seq), single nuclei RNA (snRNA-seq) and ATAC-sequencing and by spatial transcriptomic methods (GeoMx, MERFISH and Xenium). These multimodal data and additional single cell RNA-sequencing (scRNA-seq) data were integrated to generate a comprehensive molecular, genomic and function annotation of chondrocyte populations. Regulatory mechanisms were derived by linking motifs in expressed genes, transcription factors and chromatin organization.

  Study phs003721

• Integrative Analysis for Multi-Omics Data in Non-Small-Cell Lung Cancer

  We conducted epigenetic and transcriptomic mapping of primary non-small cell lung tumors and non-neoplastic tissues. 20 non-neoplastic lung tissues and 18 non-small cell lung tumors were obtained from Biorepository and Tissue Technology Shared Resource at Moores Cancer Center, UC San Diego. All tumor samples were in pathological stages I and II. 8 pairs of samples were "matched", meaning that the tumor and normal tissues came from the same patient. Microfluidic oscillatory washing–based chromatin immunoprecipitation followed by sequencing (MOWChIP-seq) was used to profile the binding of H3K4me1, H3K4me3, H3K9me3, H3K27ac and H3K27me3. SmartSeq-2 was used to map mRNA expression. We identified a large number of differentially modified regions for both epigenetic and transcriptomic markers between tumors and normal tissues. Deep learning model showed extensive transcription factor network rewiring. Raw sequencing data of the ChIP-seq and RNA-seq libraries are available through this dbGaP submission.

  Study phs003113

• Pooled Mutant KRAS-Targeted Long Peptide Vaccine Combined with Nivolumab and Ipilimumab for Patients with Resected MMR-p Colorectal and Pancreatic Cancer

  We developed INtraCEllular Reverse Transcription with Sorting and sequencing (INCERTS), an approach to combine molecular indexing of receptor repertoires within intact cells and fluorescence-activated cell sorting (FACS). We demonstrate that INCERTS enables efficient processing of millions of cells from pooled human peripheral blood mononuclear cell (PBMC) samples while retaining robust association between T cell receptor (TCR) sequences and cellular phenotypes. We used INCERTS to discover antigen-specific TCRs from patients with cancer immunized with a novel mutant KRAS peptide vaccine. After ex vivo stimulation, 28 uniquely barcoded samples were pooled prior to FACS into peptide-reactive and non-reactive CD4+ and CD8+ populations. Combining complementary patient-matched single-cell RNA sequencing (scRNA-seq) data enabled retrieval of full-length, paired TCR alpha and beta chain sequences for future validation of therapeutic utility. Data available through dbGaP include scRNA-seq with matched TCR-seq.

  Study phs003425

• Hybrid untargeted and targeted RNA sequencing facilitates genotype-phenotype associations at single-cell resolution

  Raw sequencing data for the manuscript "Hybrid untargeted and targeted RNA sequencing facilitates genotype-phenotype associations at single-cell resolution". It includes PacBio long-read scRNA-seq, illumina short-read scRNA-seq and Twist-based targeted scRNA-seq datasets for two adrenal samples.

  Study EGAS50000001537

• DNA and RNA Profiling Using Simultaneous Sequencing (Simul-seq)

  Paired DNA and RNA profiling is increasingly employed in genomics research to uncover molecular mechanisms of disease and to explore personal genotype and phenotype correlations. We developed a novel simultaneous DNA and RNA sequencing approach (Simul-seq) that enables comprehensive genomic and transcriptomic profiles from small quantities of cells or tissues. In this study, Simul-seq was performed on patient-derived fibroblast cells as well as an esophageal adenocarcinoma tumor sample and compared with standard DNA and RNA-sequencing approaches.

  Study phs001214

• Bulk and single-cell RNA sequencing of LCP1-mutated patients

  This dataset contains bulk RNA-seq and single-cell RNA-seq (scRNA-seq) of bone marrow obtained from 2 patients diagnosed with severe congenital neutropenia (SCN) attributed to LCP1 mutations. This is the first dataset of its kind. Moreover, the dataset also contains scRNA-seq of bone marrow derived from a healthy control.

  Dataset EGAD00001015698

• RNA-seq data for NRF2 study

  RNA-seq data for clinical samples

  Dataset EGAD00001002243

• RNA-seq of Ewing sarcoma tumors (ICGC)

  Ewing sarcoma (ES) is a primary bone tumor initiated by an EWSR1-ETS gene fusion. To characterize the transcriptome of Ewing sarcoma, RNA-seq was performed.

  Study EGAS00001003333

• Bulk and single-cell AML RNA-seq post ex vivo culture

  Bulk and single-cell RNA-seq performed on primary AML blasts cultured ex vivo for 3 days in standard or niche-like conditions.

  Study EGAS00001006265

• Verification of BCR reconstruction from single-cell RNA-seq using BraCeR

  This dataset was made to verify the computational reconstruction of B cell reseptors from single-cell RNA-seq using BraCeR. The dataset contains BCR-derived reads from single-cell RNA-seq from 13 cells using the Smart-seq2 protocol, as well as targeted BCR-sequencing data from the same cells.

  Dataset EGAD00001004199

• Childhood Cancer Data Initiative (CCDI): NCI-COG Pediatric MATCH Precision Medicine Clinical Trial

  The National Cancer Institute-Children's Oncology Group (NCI-COG) Pediatric Molecular Analysis for Therapy Choice (MATCH) trial provides a framework for biomarker-driven early phase trials of targeted therapies in pediatric and young adult cancer patients across the United States. Pediatric MATCH enrolled more than 1,300 patients with treatment-refractory cancers, offering access to molecular screening and investigational targeted therapies. To further advance this, matched diagnostic, refractory tumor, and normal samples from Pediatric MATCH cases were molecularly characterized, creating one of the largest comprehensive data sets available for childhood cancers. This analysis has the potential to provide novel insights into the genetics of refractory pediatric, adolescent, and young adult cancers, as well as inform potential therapeutic strategies. The following molecular characterization assays were performed: Tumor whole exome sequencing (WES) Tumor whole genome sequencing (WGS) Tumor RNA sequencing (RNA-seq) Blood whole exome sequencing (WES) Blood whole genome sequencing (WGS)

  Study phs002883

• Genomic profiling of subcutaneous patient derived xenograft models of solid childhood cancer

  The pediatric cancer cohort in this study included 70 PDX models from 65 different individuals. This cohort included a total of 16 different pediatric solid tumor subtypes, including fourteen Wilms tumors, thirteen hepatoblastomas, thirteen osteosarcomas, ten germ cell tumors, four neuroblastomas, three clear cell sarcomas, two adrenal cortical carcinomas, two leydig cell tumors, two medulloblastomas, one embryonal rhabdomyosarcoma (ERMS), one Ewing sarcoma, one pleomorphic sarcoma, one adenocarcinoma, one glioblastoma, one mesothelioma and one ovarian tumor. Notably, we have five samples with multiple PDX models from same patient, including two cases with duplicates (564 and 564-Dup, 1796 and 1796-Dup), one case with two different metastasis (560-SM, 560-LM), one case with two blocks from same tumor (1939 and 1939-Dup), and one case with different primary tumor from same patient (2264 and 1932). We have a total of 353 sequencing data, including 82 RNA sequencing data (RNA-seq), 138 whole-exome sequencing (WES) and 135 low-pass whole-genome sequencing (WGS). For RNA-seq data, we have 61 PDXs and 21 PTs; for WES, we have 67 PDXs, 30 PTs and 40 matched normal germlines; for WGS, we have 64 PDXs, 30 PTs and 40 matched normal germlines. Of which, 19 PT-PDX paired RNA-seq, 28 paired PT-PDX paired WES and WGS were included.

  Dataset EGAD00001009863

• RNA-seq in insulinomas

  RNA-seq datasets in human insulinoma samples

  Dataset EGAD50000000462

• Transcriptome of insulinomas

  Characterization of the transcriptome of insulinomas by RNA-seq.

  Study EGAS50000000320

• RNA-Seq data for Juvenile Pilocytic Astrocytomas

  RNA-Seq data for 9 JPA samples.

  Dataset EGAD00001009043

• Single-Cell RNA-Sequencing of Human Prostatectomy Tissue

  The tissue microenvironment in prostate cancer is profoundly altered. How prostate cancer cells and their precursors mediate those changes is unclear, in part due to the inability to longitudinally study the disease evolution in human tissues. To overcome this limitation, we performed extensive single-cell RNA-sequencing (scRNA-seq) to assess the transcriptional profiles of the tissue microenvironment in prostate tissues from prostatectomies from men diagnosed with prostate cancer. For each subject, benign-enriched and tumor-enriched tissues were collected from the peripheral zone of the prostate. Our studies of human tissues revealed that cancer cell-intrinsic activation of MYC signaling was the top up-regulated pathway in human cancers, representing a common denominator across the well-known molecular and pathological heterogeneity of human prostate cancer. Numerous non-malignant cell states in the tumor microenvironment (TME), including non-cancerous epithelial, immune, and fibroblast cell compartments, were conserved across individuals, suggesting that these cell types may be a sequelae of the convergent MYC activation in the cancer cells.

  Study phs003480

• Identification of point mutations, expression perturbations, and gene fusions in T-cell acute lymphoblastic leukemia by RNA-seq

  RNA-seq is a promising technology to re-sequence protein-coding genes for the identification of single nucleotide variants, while simultaneously obtaining information on structural variations and gene expression perturbations. T-cell acute lymphoblastic leukemia (T-ALL) is caused by a combination of gene fusions leading to the over-expression of transcription factors reinforced by point mutations in oncogenes and tumor suppressor genes. We asked whether RNA-seq is suitable for the detection of driver mutations in these leukemias. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq.

  Study EGAS00001000536

• Epigenomic data of Human muscle stem cell

  To profile the epigenomic landscape of Human muscle stem cell, ChIP-seq of histone marks , WGBS and RNA-seq data were generated.

  Study EGAS00001006159

• RNA-seq dataset

  HTG EdgeSeq fastq files of bulk baseline tumors from IMbassdor250: a randomised phase 3 trial comparing atezolizumab with enzalutamide vs enzalutamide alone in patients with metastatic castration-resistant prostate cancer.

  Dataset EGAD00001007685

• RNAseq of 12q-amplified osteosarcomas

  RNA-seq of 12q-amplified osteosarcomas for detection of gene fusions and gene expression analysis. RNA-seq was used to look for novel gene fusions and promoter swapping events, and study the expression levels of identified 3' partners. Additionally, global gene expression patterns were used to separate cases into clusters.

  Study EGAS50000000494

• bulk RNA-seq data

  a total number of 99 bulk RNA-seq lymphoma samples

  Dataset EGAD50000000631

• WES, sWGS and RNA-seq of Asian breast cancer

  The dataset contains a full genomics characterization of 527 Asian breast tumours. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, shallow-whole genome sequencing at 0.1X for copy number analyses, and RNA-seq of tumour tissue at 40X coverage (>15 million reads). Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit; exome capture was performed in pools of 3 and subjected to paired end 75 sequencing on a HiSEQ4000 platform. RNA libraries were prepared using the TruSeq Stranded Total RNA HT kit with Ribo-Zero Gold as per manufacturer’s instructions and also subjected to paired end 75 sequencing on a HiSEQ4000 platform. Uploaded bam files have been mapped to the hs37d5 human genome and processed using the standard GATK pipelines. Paired clinical, demographic, genotyping, and overall survival data for these patients are available from the associated publications or by request.

  Dataset EGAD00001006399

• Paired RNA-Seq of fCAB treated and bisulfite treated VDH01, partly depleted for NSUN3

  Paired RNA-seq of bisulfite treated VDH01 samples control or depleted for NSUN3 (4 replicates each). The samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of fCAB treated vdh01 samples control or depleted for NSUN3(4 replicates each). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of fCAB treated VDH01 samples (3). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of bisulfite treated VDH01 samples (3). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq.

  Dataset EGAD00001008742

• Lung Adenocarcinoma Promotion by Air Pollutants

  A complete understanding of how environmental carcinogenic exposures promote cancer formation is lacking. Over 70 years ago, tumour formation was proposed to occur in a two step process: an initiating step which induces mutations in normal tissue, followed by a promoter step which triggers cancer development. Recent evidence has revealed healthy human tissue contains a patchwork of clones harbouring oncogenic mutations.This led us to hypothesise that environmental particulate matter measuring PM2.5, known to be associated with lung cancer risk, might promote lung cancer by acting on pre-existing cells harbouring oncogenic mutations in normal lung tissue. Here we use a combination of WGS and RNA-seq of mouse tumours from pollution-exposed mice to examine the impact of particulate matter on mutagenesis and gene expression respectively.

  Study EGAS00001006951

• Sputum RNA-Seq from Asthmatic Patients for Microbes and Genes

  RNA-seq files and other data associated with an investigation of the relationship between microbes, genes and immune cells present in the sputum of humans with asthma.

  Study phs002224

• Single cell RNA sequencing of colorectal cancer patients (KUL3/KUL5)

  Libraries for scRNA-seq were generated using the Chromium Single Cell 3′ or 5′ library and Gel Bead & Multiplex Kit from 10x Genomics. Aimed to profile 5000 cells per library (if sufficient cells were retained during dissociation).

  Study EGAS00001006049

• RNA-seq of human astroblastomas

  RNA-seq for fusion gene discovery of human astroblastomas

  Dataset EGAD00001009664

• Additional RNA-seq, ChIP-seq, and ATAC-seq files for PCGP SJERG

  This dataset contains RNA-seq, ATAC-seq, and ChIP-seq samples from the SJERG cohort. We applied ChIP-Seq for Dux4 on two B-cell ALL cell-lines(REH, Nalm6) along with INPUT. ATAC-Seq on two B-cell ALL cell-lines(REH, Nalm6) and xenograft of a B-cell ALL patient(ERG000016).

  Dataset EGAD00001002654

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. FInally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Study EGAS00001004064

• Control samples, breast cancer clinical samples and matched patient-derived tumour xenografts (PDTXs) to develop and test a computational approach to discriminate human and mouse sequences in PDTXs

  Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. In this study we generated Whole Exome Sequencing (WES) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA.

  Study EGAS00001002445

• Transcriptomic profiling of lymphedema

  RNA-seq of skin from human subjects with and without lymphedema

  Study EGAS00001004516

• CD4 T-Cell RNA-Seq

  CD4 T-Cell RNA-Seq

  Dataset EGAD00001002687

• RNA-seq

  RNA-seq data

  Dataset EGAD00001008959

• LGG Epilepsy Cohort RNA-Seq

  LGG Epilepsy Cohort RNA-Seq

  Dataset EGAD00001001666

• bulk Exome-seq of the 5 HCC patients which also have the single cell T cell RNA-seq data

  bulk Exome-seq data of the 5 HCC patinets. Single cell RNA seq data of these patients was under the accession number EGAD00001003337

  Dataset EGAD00001004149

• Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases. Cancer Discovery doi: 10.1158/2159-8290. PMID: 30787016 PMCID: PMC6497554

  The BAM files for WES and RNA seq used in the article "Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases." on cancer Discovery 2019. PMID: 30787016 PMCID: PMC6497554. Authors : Grant M Fischer, ..., Michael A Davies.

  Dataset EGAD00001005046

• RNA-Seq of response to Tozinameran vaccination in individuals with KTx/dialysis or healthy controls.

  Low-input RNA-Seq (~200 cells per sample) of CD4+ T cells in patients with kidney transplants, dialysis, or healthy controls after the second dose of Tozinameran COVID-19 vaccine. RNA-Seq was performed using the Smart-Seq v4 ultra-low input protocol. The dataset includes 4 healthy control samples, 3 kidney transplant recipients, and 4 patients undergoing dialysis.

  Dataset EGAD00001007689

• Low-input RNA-seq libraries from FFPE samples using TaKaRa SMARTer kit

  Using the TaKaRa SMARTer Stranded Total RNA-Seq Kit v2, RNA-seq libraries were generated from 5 ng of RNA extracted from FFPE tissue. A low-input workflow was applied with 5 PCR cycles for the first amplification and 16 for the second. Library quality was assessed by Bioanalyzer and Qubit, and libraries were sequenced (2×150 bp) on the Illumina NextSeq 550 platform.

  Dataset EGAD50000001552

• The Role of ZEB2 during Human Neural Crest Cell Formation

  This study examined the role of ZEB2 during human neural crest cell formation. Human neural crest cells were differentiated from WiCell H1 human embryonic stem cells (WA01). Genome-wide transcriptional analysis (RNA-seq) was performed at day-3 and day-5 of human neural crest cell formation following siRNA knockdown of ZEB2 and using an N-terminal ZEB2 mutant H1 human embryonic stem cell line generated using CRISPR genome editing. RNA-seq was also performed on edited and unedited H1 human embryonic stem cells. ATAC-seq analysis was performed at day-3 and day-5 human neural crest differentiated from the N-terminal ZEB2 mutant embryonic stem cell line. Together, these data reveal the regulatory role of ZEB2 in the human neural crest gene regulatory network. We provide raw sequencing files for all RNA-seq and ATAC-seq samples.

  Study phs002701

• RNA-seq

  RNA sequencing files that were used in our study. The cohort was composed of 39 cases of RNA sequencing data. RNA-seq library was prepared with rRNA depletion method, and the sequence was performed using DNBSEQ sequencer.

  Dataset EGAD50000000971

• RNA-seq Phase Ib of olaparib and capivasertib

  RNA-seq Phase Ib of olaparib and capivasertib

  Dataset EGAD00001006840

• RNA-seq from melanoma biopsies

  RNA-seq data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009059

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 75 bp)

  Dataset EGAD00001001057

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 250 bp)

  Dataset EGAD00001001922

• RNA-seq iNKT, T and C1R cells

  RNA-seq (Ribodepleted Directional -75 PE- Hiseq 4000) data of purified and expanded iNKt and T cells from normal donor, and RNA-seq (poly-A 100-PE Hiseq 2500) data from C1R cell line. Data set consist of 3 pairs of fastq files, one pair per sample

  Dataset EGAD00001004331

• Analyses of RNAseq data from NEN patient derived tumor organoids (PDTOs) and matched parental tumors

  The dataset includes RNA-seq expression R data, RNA-seq gene counts matrix, and RNA-seq gene FPKM matrix from 21 NEN tumors or metastases and matched PDTOs. The sequencing was done with either a Nextseq 2000 or a NovaSeq 6000 instrument.

  Dataset EGAD00001009994

• Short-read (RNA-seq)

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Dataset EGAD00001006596

• Complex-I stratification of Parkinson's disease in the prefrontal cortex - bulk RNA seq

  Dataset corresponding to the bulk RNA seq dataset from prefrontal cortex for a total of N=98 samples. Tissue samples correspond to patients with idiopathic Parkison's disease (PD) with varying levels of Complex-I activity (N=79) and neurologically healthy controls (N=19). RNA seq was carried out using ribosomal depletion. A subset of these samples was additionally sequenced using single nucleus RNA seq and is available in the same Study as an additional Dataset.

  Dataset EGAD50000000433

• WES and RNA-seq data of 11 cancer patients with multiple cancer types

  This dataset includes WES and RNAseq from 11 patients with metastatic melanoma, lung, kidney, and stomach cancers, enrolled in phase I clinical trials of TIL ACT (NCT03475134 & NCT04643574). WES was performed on matched cancer and healthy samples, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 2500/4000 and Illumina NextSeq 550 systems. Paired-end reads are provided in fastq format.

  Dataset EGAD00001010169

• RNA seq reads

  RNA seq reads constituting of FASTQ paired end reads from 5 FHD/FHDL patients

  Dataset EGAD00001003790

• Paired-end RNA-seq analysis of GBM, additional patient.

  Dataset contains one paired-end RNA-seq sample.

  Dataset EGAD00001011990

• A Personalized Neoantigen Vaccine Generates Anti-Tumor Immunity in High-Risk Renal Cell Carcinoma

  Personalized cancer vaccines (PCVs) can generate circulating immune responses against predicted neoantigens. However, whether such responses can target cancer driver mutations, lead to immune recognition of a patient's tumor, and result in clinical activity are largely unknown, particularly in patients with lower mutation burden tumors. We conducted a phase I trial testing a neoantigen-targeting PCV in patients with high-risk, fully resected clear cell renal cell carcinoma (RCC; stage III or IV), with or without ipilimumab administered adjacent to the vaccine. At a median follow-up of 40.2 months from surgery, none of the 9 initial participants experienced a recurrence of RCC. No dose-limiting toxicities were observed. All patients generated T cell immune responses against PCV antigens, including RCC driver mutations in VHL, PBRM1, BAP1, KDM5C, and PIK3CA. Following vaccination, there was a durable expansion of peripheral T cell clones, and T cell reactivity against autologous tumor was detected in 7 of the 9 patients. Our results demonstrate that neoantigen-targeting PCVs in high-risk RCC are highly immunogenic, capable of targeting key driver mutations, and induce anti-tumor immunity. These observations, in conjunction with the absence of recurrence in all 9 vaccinated patients, highlights the promise of PCV as effective adjuvant therapy in RCC. We are submitting WES data from whole blood samples and tumor FFPE samples, and RNA-Seq data from tumor FFPE samples. We are also submitting RNA-Seq data from purified tumor samples. scRNA-seq data will be submitted for skin samples (vaccine site biopsies at Weeks 0, 4, and 13), tumor samples (including resected metastatic tumor samples for two patients), and normal kidney samples. Bulk TCR-seq data will be submitted for blood samples.

  Study phs003710

• RNA-seq

  Bulk RNA-seq data of 8 RNA samples were generated. Libraries were prepared using the Stranded mRNA Library Prep, Ligation Kit (Illumina) following manufacturer’s recommendations and sequenced on a NextSeq 2000 (2x50 bp, Illumina).

  Dataset EGAD00001010308

• RNA-seq data for study 'Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk.'

  RNA-seq data from nasal and bronchial tissues in 649 subjects, many with lung cancer. Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to lifestyle risk in the form of cigarette smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, often many years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk are unclear. CT screening of current and former smokers has been shown to reduce lung cancer mortality by up to 26%. To examine whether clinical risk stratification can be improved upon by the addition of genetic data, and to explore the mechanisms of the persisting risk in former smokers, we have analyzed transcriptomic data from accessible airway tissues of 487 subjects. We developed a model to assess smoking associated gene expression changes and their reversibility after smoking is stopped, in both healthy subjects and clinic patients. We find persistent smoking associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune and interferon related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Our results provide initial evidence for germline-mediated personalised smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

  Dataset EGAD50000000333

• Developing Allelic Imbalance Analysis from Single-Nucleus RNA-Seq Data

  Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that, because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq) and introns are under lower purifying selection, and are thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. Overall, we provide an end-to-end experimental and computational approach for future studies.

  Study phs003749

• Epigenomic dataset of Human Hepatocellular Carcinoma for EpiHK

  This dataset is generated for EpiHK. It contains Histone PTM ChIP-seq, WGBS, RNA-seq of Human Hepatocellular Carcinoma and tumor adjacent normal tissue sample.

  Study EGAS50000000039

• Single-cell RNA-seq data of angioimmunoblastic T-cell lymphoma

  Single-cell RNA-seq data of angioimmunoblastic T-cell lymphoma

  Dataset EGAD00001011361

• IPF Core Biopsy Study RNA-Seq Fastqs

  RNA-Seq fastq files for 97 Idiopathic Pulmonary Fibrosis samples.

  Dataset EGAD00001007568

• Bulk RNA-seq osteosarcoma

  We profiled 23 patient osteosarcoma tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001011371

• RNA-Seq dataset

  10 RNA-Seq bam files including 4 cases with fusion and 6 controls without fusion.

  Dataset EGAD00001004360

• RNA-Seq and WXS from 6 glioblastoma patients

  RNA-Seq and WXS from 6 glioblastoma patients

  Dataset EGAD00001003293

• RNA-Seq files for SJOS study

  RNA-Seq files for SJOS study

  Dataset EGAD00001003154

• Glucose Binds and Activates NSUN2 to Promote Translation and Epidermal Differentiation

  This study was undertaken to determine the role of NSUN2 in epidermal homeostasis. Primary neonatal foreskin keratinocytes were obtained from discarded surgical samples at a California hospital. Samples were anonymous and not selected. NSUN2 was found to promote epidermal differentiation and alter translation using the methods of RNA-seq, CLIP-seq and Ribo-seq.

  Study phs003767

• Prognostic whole-genome and transcriptome signatures in colorectal cancers

  This dataset consists of RNA-seq CRAM raw-data files of 1063 primary colorectal cancer and 120 adjacent normal tissue samples. The expression profiles for these samples can be found at the ArrayExpress with accession number E-MTAB-12862.

  Dataset EGAD50000000169

• RNA-Seq Dataset of Organoid drug profiling identifies methotrexate as a therapy for SCCOHT, a rare pediatric cancer

  We generated and characterized tumoroids from small cell carcinoma of the ovary, hypercalcemic type. Furthermore, we identified a drug that is selective and effective against SCCOHT tumoroids.

  Dataset EGAD00001015441

• WGS, RNA-seq and methyl-seq data for multiple tumour clones from a single glioblastoma case.

  10 bams of WGS data from HiSeqXTen platform; 10 bams of RNA-seq data from HiSeq2500 platform; 7 bams of TruSeq Methyl Capture EPIC sequencing data from HiSeq4000 platform

  Dataset EGAD00001004773

• Integrated diagnosis based on transcriptome analysis in suspected pediatric sarcomas

  We performed RNA-sequencing in 47 patients with suspected pediatric sarcomas, to evaluate the potential utility of RNA-seq in the diagnostic process of pediatric sarcoma.

  Study JGAS000284

• Whole-genome landscape of adult T-cell leukemia/lymphoma

  Whole-genome sequencing (WGS) data of tumor-normal pairs from 139 ATL patients and RNA sequencing (RNA-seq) data of tumors from 28 ATL patients.

  Study EGAS00001005237