9172 results for "cancer rna-seq"

in 21.41 milliseconds.

• RNA-seq CLL

  RNA-seq of peripheral blood samples from CLL patients.

  Dataset EGAD00001001675

• Un-mapped RNA-Seq data

  Un-mapped whole transcriptome RNA-Seq data from 476 human samples of early stage urothelial carcinoma.

  Dataset EGAD00001001940

• Transcriptome profiling of 25 prostate tumor samples by RNA-Seq

  Transcriptome profiling of 25 prostate tumor samples by RNA-Seq

  Dataset EGAD00001003344

• WGS and RNA-Seq data from a GBM patient PT-MD9088

  WGS and RNA-Seq data from a GBM patient PT-MD9088

  Dataset EGAD00001004254

• WGS and RNA-Seq data from a GBM patient PT-CA2271

  WGS and RNA-Seq data from a GBM patient PT-CA2271

  Dataset EGAD00001004230

• WGS and RNA-Seq data from a GBM patient PT-JE6375

  WGS and RNA-Seq data from a GBM patient PT-JE6375

  Dataset EGAD00001004246

• WGS and RNA-Seq data from a GBM patient PT-GC1519

  WGS and RNA-Seq data from a GBM patient PT-GC1519

  Dataset EGAD00001004239

• WGS and RNA-Seq data from a GBM patient PT-HO0394

  WGS and RNA-Seq data from a GBM patient PT-HO0394

  Dataset EGAD00001004243

• WGS and RNA-Seq data from a GBM patient PT-RD1291

  WGS and RNA-Seq data from a GBM patient PT-RD1291

  Dataset EGAD00001004256

• WGS and RNA-Seq data from a GBM patient PT-VO7089

  WGS and RNA-Seq data from a GBM patient PT-VO7089

  Dataset EGAD00001004263

• WGS and RNA-Seq data from a GBM patient PT-TM5196

  WGS and RNA-Seq data from a GBM patient PT-TM5196

  Dataset EGAD00001004262

• Glioblastoma stem cell lines RNA-seq

  RNA-seq data for three Glioblastoma stem cell (GSC) lines exposed to PRMT5 inhibitor and control samples.

  Dataset EGAD00001006095

• RNA-seq of Shprintzen-Goldberg syndrome dermal fibroblasts

  RNA-seq of dermal fibroblasts treated ± TGF-β from control and Shprintzen-Goldberg syndrome patients.

  Dataset EGAD00001006801

• IMpower133 processed RNA-seq data whole transcriptome

  This dataset contains log2(TPM + 1) for 271 tumor samples profiled by RNA-seq for the entire transcriptome.

  Dataset EGAD00001006927

• WGS and RNA-Seq data from a GBM patient PT-JR9883

  WGS and RNA-Seq data from a GBM patient PT-JR9883

  Dataset EGAD00001008521

• WGS and RNA-Seq data from a GBM patient PT-BM8772

  WGS and RNA-Seq data from a GBM patient PT-BM8772

  Dataset EGAD00001008516

• Single-cell RNA-seq of immune cells from Melanoma tumors (Li et al, 2018)

  Single-cell RNA-seq profiling of immune cells sorted from human Melanoma tumors (and several matching PBMC samples). Contains de-multiplexed FASTQ files per plate (MARS-seq amplification batch, total 204 samples) and also de-multiplexed FASTQ files of single-cell TCRb-seq.

  Dataset EGAD00001004497

• scRNA Seq of MCSP+ melanoma DCC and bulkRNA Seq of MelDCC lines

  Single cell RNA Seq of: 193 MCSP+ DCC isolated from SLNs of melanoma patients, 9 MCSP+ cells isolated from LNs of non-melanoma patients, 14 melanocytes from a healthy donor. Bulk RNA Seq of 10 samples from 4 DCC-PDX-derived cell lines. Sequencing on NovaSeq6000. Fastq files.

  Dataset EGAD00001009662

• Single cell RNA-seq and ATAC-seq of human fetal forebrain tissue, weeks 8 to 11.

  Single cell RNA-seq from 6 and single nuclei ATAC-seq from 3 human fetal tissue samples. Samples from 8 to 11 weeks. Includes a 8.5 weeks samples with matching both ATAC-seq and RNA-seq runs. Data was sequenced using 10X Genomics chromium technology, for scRNA-seq samples belong to v2 and v3.

  Dataset EGAD00001008653

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia - RNA

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009305

• Deciphering RBP - alternative splicing networks in ALS iPSC-MN: NOVA1 gain and loss of function systems

  We produced NOVA1 and EGFP over-expression RNA-seq datasets in three healthy control individuals using lentivirus-mediated gene transfer to model NOVA1 gain of function. Additionally, we produced a NOVA1 loss of function RNA-seq dataset by generating iPSC-MN with NOVA1 knock out produced by CRISPR/Cas9 in the CV-B iPSC line.

  Study EGAS00001005881

• H3K27ac/H3K27me3 landscape of medulloblastoma

  We profiled 111 patient medulloblastoma primary tumor samples by bulk RNA-seq (19 samples), 27ac (98 samples) / 27me3 (61 samples) ChIP-Seq, WGS (4 samples) and 27ac hichip (8 samples). Submitted data consists of data generated from previously unpublished tumors as well as complementary data for data sets already published for identical medulloblastoma tumors (ex: 27me3 ChIP-Seq and RNA-Seq data submitted for a tumor with publicly available WGS data). The raw fastqs and hg19 aligned RNA-Seq bams are provided.

  Dataset EGAD00001009709

• Indonesian RNA-seq dataset

  This dataset contains 4 batches of Indonesian RNA-seq data from Mentawai, New Guinea and Sumba islands. One RNA-seq batch was prepared without Globin depletion, and three batches were Globin depleted using the Illumina Globin-Zero Gold Kit. There are 179 runs in total, including 119 unique samples. Dataset includes multiple batch control samples.

  Dataset EGAD00001005053

• RNA-Seq for PTPN1 project (EGAS00001000554)

  Fastq files corresponding to RNA-Seq dataset for PTPN1 project (EGAS00001000554)

  Dataset EGAD00001001646

• Vcf RNA-Seq data

  Variants derived from mapped whole transcriptome RNA-Seq data from 476 human samples of early stage urothelial carcinoma.

  Dataset EGAD00001001941

• RNA-seq data from sorted CML and normal samples

  RNA-seq data from sorted populations from 10 CML samples and 4 normal bone marrow samples.

  Dataset EGAD00001004079

• WGS and RNA-Seq data from a GBM patient PT-KM5291

  WGS and RNA-Seq data from a GBM patient PT-KM5291

  Dataset EGAD00001004249

• WGS and RNA-Seq data from a GBM patient PT-DS9789

  WGS and RNA-Seq data from a GBM patient PT-DS9789

  Dataset EGAD00001004233

• WGS and RNA-Seq data from a GBM patient PT-EV3071

  WGS and RNA-Seq data from a GBM patient PT-EV3071

  Dataset EGAD00001004234

• WGS and RNA-Seq data from a GBM patient PT-GJ3716

  WGS and RNA-Seq data from a GBM patient PT-GJ3716

  Dataset EGAD00001004240

• WGS and RNA-Seq data from a GBM patient PT-SK0976

  WGS and RNA-Seq data from a GBM patient PT-SK0976

  Dataset EGAD00001004260

• Transcriptome of Ewing sarcoma tumors - RNA-seq (ICGC)

  Transcriptome of Ewing sarcoma tumors (ICGC project). Fastq files of 57 RNA-seq are available (2x101bp).

  Dataset EGAD00001004493

• RNA-seq of UTUC samples

  This dataset includes transcription profiling by RNA-seq of 3 upper urinary tract urothelial tumours (UTUC).

  Dataset EGAD00001008138

• WGS and RNA-Seq data from a GBM patient PT-WR7927

  WGS and RNA-Seq data from a GBM patient PT-WR7927

  Dataset EGAD00001008531

• WGS and RNA-Seq data from a GBM patient PT-WT4796

  WGS and RNA-Seq data from a GBM patient PT-WT4796

  Dataset EGAD00001008532

• WGS and RNA-Seq data from a GBM patient PT-RV2286

  WGS and RNA-Seq data from a GBM patient PT-RV2286

  Dataset EGAD00001008527

• WGS and RNA-Seq data from a GBM patient PT-GE7528

  WGS and RNA-Seq data from a GBM patient PT-GE7528

  Dataset EGAD00001008519

• WGS and RNA-Seq data from a GBM patient PT-GI2070

  WGS and RNA-Seq data from a GBM patient PT-GI2070

  Dataset EGAD00001008520

• WGS and RNA-Seq data from a GBM patient PT-LC6372

  WGS and RNA-Seq data from a GBM patient PT-LC6372

  Dataset EGAD00001008522

• WGS and RNA-Seq data from a GBM patient PT-ML9537

  WGS and RNA-Seq data from a GBM patient PT-ML9537

  Dataset EGAD00001008523

• 7 samples RNA-seq raw data

  Dataset comprising raw paired RNA-seq data in fastq.gz format for 7 samples of rosette forming brain tumors

  Dataset EGAD00001009265

• RNA sequencing of tumor samples from patients with Waldenstrom macroglobulinemia

  RNA was extracted from flow-sorted CD19+. RNA-Seq was performed on 32 samples of 30 patients (2 replicates per samples). RNA-Seq libraries were subjected to non-stranded paired-end (2 x 75 bp) sequencing on HiSeq 2500 (Illumina). The files are in FASTQ format.

  Dataset EGAD00001005324

• RNA_Seq_OMELib__Cord_blood_

  The transcriptional landscape of haematopoietic cells will be characterized by RNA Seq following amplification of RNA/cDNA and preparation of libraries by PTA(primary template-directed amplification). Samples have been sourced from the Cambridge Biobank.

  Study EGAS00001007454

• Pathogenesis and Immunity in Endemic Burkitt Lymphoma

  This genomic landscape of Burkitt lymphoma represents a multimodal sequencing of tumors and control tissues and individuals to better understand the etiology, and molecular pathogenesis of Burkitt lymphoma including the roles of the associated Plasmodium falciparum malaria and EBV infections. Comprehensive sequencing set includes genomic, transcriptomic, and epigenomic datasets in concert with variable clinical phenotypes and outcome information such as anatomical presentation site, in-hospital survival rates, and EBV genome type. This deposit includes additional small RNA-seq data from endemic BL (eBL) tumors and RNA-sequencing data from sorted NK cell subsets from the peripheral blood of eBL patients. The additional small RNA-seq data are from tumor specimens from 17 eBL cases, of the previously deposited polyA RNA-seq data from 28 eBL cases (phs1282.V1). The additional RNAseq data from Fluorescence-activated cell sorting (FACS) of NK cell subsets, isolated from the peripheral blood of 7 eBL cases. The additional RNAseq data are from eBL tumor samples of 5 eBL cases, BL cell lines established from these tumor samples and also NSG mouse tumors created by implanting the established BL cell lines into NSG mice. The dataset also includes RNAseq data of the NSG mouse tumors after treatment with Rituximab (RIT) and Phosphate buffered saline (PBS). The fifth batch of data includes mRNA-seq from different passages of a single eBL cell line that have variable EBV phenotypes (low passage with lytic EBV and high passage with latent EBV). This batch also includes lytic and latent lymphoblastoid cell line (LCL) clones from a single blood donor generated through infection with the strain of EBV isolated from the aforementioned eBL cell line. For initial transcriptome analysis see: Kaymaz et al. "Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type-specific Difference." Molecular Cancer Research, 2017, PMID: 28465297. For initial microRNA analysis see: Oduor et al. "Human and Epstein-Barr Virus miRNA Profiling as Predictive Biomarkers for Endemic Burkitt Lymphoma." Frontiers in Microbiology 8, 2017, PMID: 28400759. For initial NK subsets RNA expression analysis see: Forconi et al. "A new hope for CD56negCD16pos NK cells as unconventional cytotoxic mediators: an adaptation to chronic diseases." Frontiers in Cellular and Infection Microbiology, 2020, PMID: 32373555.For initial eBL cell line and mouse avatar RNA expression analysis see: Saikumar et al. "Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies." Life Science Alliance, 2023, PMID: 36878637. For general overview of cohort study see: Buckle et al. "Factors influencing survival among Kenyan children diagnosed with endemic Burkitt lymphoma between 2003 and 2011: A historical cohort study." Int J Cancer. 15:1231, 2016, PMID: 27136063.

  Study phs001282

• WES and RNA-seq of triple-negative breast cancers from the MyBrCa cohort

  The dataset contains a genomics characterization of 35 triple-negative Asian breast tumours from the Malaysian Breast Cancer cohort. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, 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 NovaSeq 6000 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 NovaSeq 6000 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 EGAD00001009311

• SF11949 scRNA-Seq primary oligodendroglioma G3 IDH1 Mutant Male

  SF11949 Primary oligodendroglioma G3 IDH1 Mutant. Male, 40 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 EGAD00001005397

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

  Single Cell Prmary high grade glioma IDHR132H Wild-type 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.

  Dataset EGAD00001005429

• Human data for chromatin accessibility (ATAC-Seq and scATAC-Seq) and transcriptome (RNA-Seq and scRNA-Seq) in eight B-cell precursors, from HSC to Naive B cells

  We generated paired transcriptomic (RNA-Seq) and chromatin accessibility (ATAC-Seq) profiles from 8 immune cell populations representing the early lymphoid hematopoietic differentiation stages along the B cell lineage: HSC, CLP, pro-B, pre-B, Immature B, Transitional B, Naive B CD5-, Naive B CD5+. Cell populations were isolated from healthy adult human bone marrow precursors and peripheral blood (n=13) following a predefined sorting strategy. For each donor, a subset of different cell type populations was obtained. Additionally, to explore the continuous dynamic of RNA and ATAC, we generated a single-cell sample of human CD34+ B cell precursors.

  Study EGAS00001007296

• The Finland-United States Investigation of NIDDM Genetics (FUSION) Study - Islet Expression and Regulation by RNAseq and ATACseq

  This study evaluates gene expression and its regulation in human pancreatic islets, a tissue relevant in the study of genetic risk factors contributing to diabetes. We obtained islets from deceased donors and generated data from genome-wide SNP chip, bulk RNA-Seq, microRNA (miRNA)-Seq, whole genome sequence, DNA methylation (methyl)-Seq, transcription initiation profiles using cap analysis of gene expression (CAGE)-Seq, single cell RNA-seq, and single nuclei ATAC-seq. These data include ATAC-seq of two islet subjects, RNA-seq of 31 additional subjects, genome-wide chip genotypes, and imputed genotypes of the 33 subjects released with phs001188.v1. For genotyping, 500-1000 islet equivalents (IEQ) were cultured as in Gershengorn (Science, 2004, PMID: 15564314); genomic DNA isolated from islet cultures. For RNA analyses, 2500-5000 IEQ from each islet source were used for bulk or single-cell RNA isolation. Messenger RNA was isolated with trizol extraction and 12-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol. Bulk RNA sequencing was performed on HiSeq2000/HiSeq2500 sequencers using paired-end reads at the NIH Intramural Sequencing Center (NISC). miRNA libraries were prepared from total RNA from 68 samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. CAGE libraries were prepared from total RNA samples using the nAnT-iCAGE protocol at DNAFORM, Japan. CAGE libraries were sequenced at the NIH Intramural Sequencing Center (NISC) on the HiSeq2000 sequencer. Genotyping on the Illumina Omni2.5M array was performed at the NHGRI Genomics Core facility. Genotypes were imputed using the HRC.r1.1.2016 reference panel. In order to assess regions of open chromatin in islets, we performed bulk ATAC-seq on HiSeq2000 sequencers using paired-end reads at NISC. Single-nuclei ATAC-seq libraries were prepared using single-cell-combinatorial-indexing (sci-) ATAC-seq protocol and sequenced on Illumina NextSeq using paired-end reads. scRNA-seq libraries were generated using the 10X Genomics platform and sequenced on Illumina HiSeq3000 at the Genomics Technology Core of the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). Greater than 90% of the loci associated with T2D through genome-wide association studies occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Therefore, there is a critical need to understand the full spectrum of genetic variation and regulatory element usage in T2D-relevant tissues. To that end, this study contains whole genome sequence and whole genome bisulfite sequence, and/or Illumina MethylationEPIC Array data, providing a comprehensive survey of both individual genetic variation as well as DNA methylation across different tissues from multiple individuals. In addition, we carried out sequencing of single cell RNAs (two subjects) and single cell nuclei (one subject) to characterize gene expression and chromatin accessibility of islets.

  Study phs001188

• RNA Seq Data

  Human melanoma samples were collected pre, on, and progression on BRAF inhibitor therapy. RNA was extracted and run on RNA-seq. This has provided insights into different categories of BRAF inhibitor resistance mechanisms.

  Dataset EGAD00001001306