Search Results - EGA European Genome-Phenome Archive

Exploration of coding and non-coding variants in cancer using GenomePaint.

GenomePaint (https://proteinpaint.stjude.org/genomepaint) is a dynamic visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data, featuring a novel design that captures the inter-relatedness between DNA variations and RNA expression. Regulatory non-coding variants can be inspected and discovered along with coding variants, and their functional impact further explored by examining 3D genome and/or ChIP-seq data generated from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and can display external data such as adult cancer datasets and user-provided custom tracks. We used GenomePaint to analyze multi-omics data from 3,652 pediatric cancers representing 16 histotypes, and demonstrate the visualization features through examples, including two that led to new insights into oncogenic mechanisms in pediatric cancer. The first is the discovery of a new class of pathogenic recurrent variants that cause aberrant splicing, disrupting the RING domain of CREBBP, a driver gene frequently mutated in relapsed pediatric leukemia. The second is the cis-activation of the MYC oncogene in a subset of B-lineage acute lymphoblastic leukemia (B-ALL) via duplication of the NOTCH1-MYC enhancer (N-ME), previously discovered only in T-lineage ALL. The regulatory impact of N-ME enhancer amplification was initially confirmed by allelic imbalance in published gene expression and ChIP-seq data and verified by additional Capture-C and fluorescence in situ hybridization data generated by follow-up experiments. These examples demonstrate the power of GenomePaint in enabling not only data visualization but also integrative genomic analysis that can lead to novel biological insight for follow-up experimental validation.

Study EGAS00001004669

Melanoma Brain Metastasis Single-Cell RNA Sequencing Atlas

Melanoma brain metastasis (MBM) frequently occurs in patients with advanced melanoma, yet our understanding of the underlying salient biology is rudimentary. Here, we performed single-cell/nucleus RNA-seq in 22 treatment-naive MBM (scRNA-seq n=5; snRNA-seq n=17) and 10 extracranial melanoma metastases (ECM; all snRNA-seq), and matched spatial single-cell transcriptomics (n=16) and T cell receptor (TCR)-seq (n=5). Key observations were validated in matched bulk RNA- and ATAC-seq of isogenic MBM/ECM cell culture models (n=24; including triplicates (labeled as *-A, -B and -C) of 4 MBM- and 4 ECM-derived patient cell lines with 2 matched pairs). This work provides comprehensive insights into MBM biology and serves as a foundational resource for further discovery and therapeutic exploration.

Study phs002944

Genetics of Mammographic Density in Ashkenazi Jews

Mammographic density (MD) is a strong risk factor for breast cancer and is also a highly heritable trait with ~60-70% of the variance due to genetic factors, based on twin studies. MD is also higher in families with a strong history of breast cancer. Genome-wide association studies (GWAS), which focus on common genetic variants, have identified several single nucleotide polymorphisms (SNPs) associated with MD. However, these SNPs explain a very small fraction of the variance of MD, suggesting many other genes are involved. Thus, the vast majority of the heritability of mammographic density remains unexplained and may be explained, at least in part, by rare variants. Ashkenazi Jewish women are a founder population; founder populations frequently have alleles affecting phenotypes which may be unique and/or extremely rare in other populations. Prior reports have identified an association between higher mammographic density and Ashkenazi Jewish ancestry. Therefore, we developed a study of mammographic density in AJ women. We combined datasets from several different cohorts including (a) the California Pacific Medical Center Research Institute (b) the Athena Breast Health Cohort and (c) the Marin Women's study. In each study, we identified women who reported AJ ancestry (determined by self-report or by genetic analysis). We identified mammograms by linking the women to the San Francisco Mammography Registry. We retrieved digital mammographic results and used software to infer volumetric density and percent volumetric density for each woman. We then performed genome-wide genotyping of the samples using the Illumina MEGA array.

Study phs001857

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

RNA-seq FASTQ files studied in Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

This data set contains 12 paired fastq files (RNA-seq) from one wild-type iPS cell, two HLA-KO iPS clones and three CD14+ monocytes differentiated from them. The RNA-seq libraries were generated with Illumina TruSeq Stranded Total RNA library prep kit. The libraries were sequenced with Illumina NovaSeq 6000 in paired-end mode.

Dataset EGAD50000001700

mapped Bam files from whole transcriptome RNA-seq

In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors). Data provided here consist of 71 mapped Bam files form whole transcriptome RNA-seq.

Dataset EGAD00001002718

unmapped Bam files from whole transcriptome RNA-seq

In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors). Data provided here consist of 71 unmapped Bam files form whole transcriptome RNA-seq.

Dataset EGAD00001002717

This dataset includes FASTQ for single-nucleus RNA-seq of normal controls, and multiple system atrophy (MSA) or Parkinson's disease (PD) patients.

The dataset includes more than 130k nuclei isolated using 10x Genomics Chromium Next GEM Single Cell 3' Reagent Kits v3.1. In total, 30 samples are included from 10 controls, 8 MSA (including one sample that was omitted during QC), or 12 PD patients. Samples were sequenced on Illumina Novaseq 6000 or NextSeq 550 instruments.

Dataset EGAD50000002041

Transcriptomic Characterization of Human Innate T Cells

We performed low input RNA-seq and single-cell RNA-seq on 7 lymphocyte populations with the objective of finding the common transcriptional programs shared among innate T cells. In brief, we drew blood from healthy donors and isolated CD4+ T cells, CD8+ T cells, Natural Killer cells, invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, and two γδ T cell populations: Vδ1 and Vδ2. We performed low input RNA-seq on samples with 1000 cells each, for 6 healthy individuals, in duplicate. We performed single-cell RNA-seq with DNA-barcoded hashing antibodies to identify the cell type of origin for each cell. We found a common transcriptional program across lymphocytes that is gradually turned on as lymphocytes have a more "innateness" state, promoting effector functions and sacrificing proliferative capacity. Additionally, we performed low input RNA-seq for Vδ3-expressing γδ T cells and δ/αβ T cells for one healthy individual in duplicate (1000 cells per sample). This revealed δ/αβ T cells are transcriptionally more adaptive-like and Vδ3 more innate-like.

Study phs002007

All available datasets of DEEP

All available datasets of DEEP. There are several Reference Epigenomes available. For metadata check out www.deep.dkfz.de One reference epigenome represents a set of 6 ChIP-Seqs, WGBS, RNA-Seq, smallRNA-Seq and sometimes DNase1 and/or NOME-Seq.

Study EGAS00001001608

StemNet - in vitro differentiation of human hepatocytes

Integrative epigenomic and transcriptomic characterization of hepatocyte-like cells differentiated in vitro from human induced pluripotent stem cells in comparison with primary human hepatocytes. This study comprises single cell RNA-seq, bulk mRNA-seq, ATAC-seq and RRBS.

Study EGAS00001004201

Human adipocyte bulk RNA-seq dataset

The dataset contains raw RNA-seq data of human adipocytes from 13 individuals.

Dataset EGAD00001008335

RNA-Seq for academic use only

RNA-Seq transcriptome data is only for academic use.

Dataset EGAD00001009675

RNA-seq for IgA BMPC project

7 RNA-seq samples in total: CD19n_IgAn (x2) , CD19n_IgAp (x2) , CD19p_IgAn (x1), CD19p_IgAp (x2)

Dataset EGAD00001006862

Extracellular RNA Profiling of Serum, Plasma, and Urine of Healthy Subjects

This study was conducted to generate a reference dataset of extracellular RNA (exRNA) from standard human biofluids. Small RNA-seq data was obtained from plasma, serum, and urine of healthy subjects.

Study phs003054

Bone Microarchitecture

Osteoporosis affects more than 28 million people in the United States and the lifetime risk for osteoporosis-related morbidity is higher than a woman's combined risk for breast cancer, endometrial cancer and ovarian cancer. Health care expenditures for osteoporotic patients in this country are currently nearly 13 billion dollars per annum and are predicted to increase markedly in the next decades due the aging of the population; therefore, it is important to understand the factors that contribute to bone strength and fracture risk. With the advent of skeletal imaging modalities such as high resolution peripheral quantitative computed tomography (HR-pQCT), it is now possible to study the genetics of more highly refined peripheral skeletal microarchitecture phenotypes. Because these refined phenotypes have not been measured in many cohort studies, this project was a collaborative effort to include almost all the existing data on HR-pQCT of the radius and tibia, and genetics from around the world. Cohorts involved in the discovery included: 1) Framingham Osteoporosis Study, 2) Mayo Clinic cohort; 3) Geneva Retirees cohort; 4) OFELY cohort from Lyon France; 5) STRAMBO cohort from Lyon France; 6) Swedish Male cohorts. Replication cohorts undergoing de novo genotyping include: 1) QUALYOR cohort from Lyon France; 2) CaMOS cohort from Canada. Other than the Framingham cohort and the Swedish male cohorts, all discovery cohorts underwent genome wide genotyping with the Affymetrix Axiom Biobank array that had common variants along with exome content based on the early findings from the Exome Sequencing Project. All genotype data from the discovery cohorts were then imputed using the Haplotype Reference Consortium reference panel. Variants for replication genotyping using the Kasp technology were selected based on novelty compared with previously identified loci using DXA phenotypes, minor allele frequency, underlying LD structure within a locus, and likelihood of a variant being functional as assessed using various algorithms for coding and non-coding variants.    

Study phs002102

RNA Seq OMELib

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.

Dataset EGAD00001015252

Single-cell RNA-seq of PBMC from 2 patients iGRAN-Low and 2 patients iGRAN-High

Single-cell RNA-seq of PBMC from 2 patients iGRAN-Low and 2 patients iGRAN-High [Publication title] CXCL8 secreted by immature granulocytes inhibits wildtype hematopoiesis in chronic myelomonocytic leukemia

Study EGAS50000000556

Longitudinal RNA-seq (whole blood) in a twin cohort

The dataset consists of 232 RNA-seq samples (whole blood) obtained from healthy female from the TwinsUK adult registry cohort. The samples were obtained at two time points separated on average by 22 months.

Study EGAS00001001763

RNA-seq data: Histone mutations in human Pre-Leukemic HSC and AML

RNA-seq data from TEX cells transduced with HIST1H3H WT, HIST1H3H K27M, HIST1H3F WT, HIST1H3F K27I and Luc2 control in triplicate. In addition, RNA-Seq was performed on untransduced TEX cells. Libraries [rRNA-depleted stranded (HMR)] were sequenced on an Illumina Hiseq 4000 platform to generate 100 bp paired-end reads.

Dataset EGAD00001004437

Single-cell RNA-seq cases

Single-cell RNA-seq cases (Tumor and adjacent tissue)

Dataset EGAD00001008835

Optimized large-scale longitudinal biorepository of gastroesophageal adenocarcinoma patient-derived organoids

To enable translation studies we created patient derived models to assess biomarkers, target validation, and patient response. To evaluate that the PDOs retain the genomic characteristics of the primary tumours we performed (WES) sequencing of 11 matched primary tumours and their matched organoid samples (total n=22). Libraries were sequenced using Illumina NovaSeq technology. Reads were aligned with BWA alignment to hg38. Files were processed following GATK best practices (version GATK 4.2.3.0). Variants were called using Mutect2. The dataset contains the alignmnent files (.bam) and the called mutations (.vcf).

Study EGAS50000001614

15058 results for "cancer studies using rna-seq"

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