11973 results for "cancer rna-seq"

in 16.89 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