14970 results for "cancer studies using rna-seq"

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• Human_Developmental_Cell_Atlas_HDCA___WGS

  Single cell + bulk genomics study for immune and hematopoietic organs during human fetal development

  Study EGAS00001002929

• Childhood_arthritis_DNA

  The aim of this study is to describe the transcriptome of single arthritic cells.

  Study EGAS00001002652

• pilot

  Pilot study for genome-wide sequencing of cell-free DNA from nipple aspirate fluid and plasma of breast cancer patient

  Dataset EGAD00001010836

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• Chromatin immunoprecipitation linked to next-generation whole genome sequencing (ChIP-Seq) for H3K36me3 in paediatric high grade glioma cell lines KKNS4 and SF188 with and without a G34V mutation in H3F3A

  Glioblastomas of children and young adults have a median survival of only 12-15months and are clinically and biologically distinct from histologically similar cancers in older adults1. They are defined by highly specific mutations in the gene encoding the histone H3.3 variant H3F3A2, occurring either at or close to key residues marked by methylation for regulation of transcription – K27 and G34. We performed chromatin immunoprecipitation linked to next-generation whole genome sequencing (ChIP-Seq) for H3K36me3 in order to test the hypothesis that, rather than total H3K36me3, the G34V mutation may instead result in differential binding of the trimethyl mark throughout the genome. Compared with H3F3A wild-type SF188 paediatric GBM cells, H3K36me3 was found to be significantly differentially bound in KNS42 cells at 5130 distinct regions of the genome corresponding to 156 genes. Concurrent whole genome DNA sequencing demonstrated that this was not confounded by copy number alterations. The cerebral hemispheric-specific G34 mutation drives a distinct expression signature through differential genomic binding of the K36 trimethylation mark (H3K36me3). The transcriptional program induced recapitulates that of the developing forebrain, and involves numerous markers of stem cell maintenance, cell fate decisions and self-renewal. Critically, H3F3A G34 mutations cause profound upregulation of MYCN, a potent oncogene which is causative of glioblastomas when expressed in the correct developmental context. This driving aberration is selectively targetable in this patient population by inhibiting kinases responsible for stabilisation of the protein.

  Study EGAS00001001437

• Whole exome sequencing of gallbladder cancer

  The dataset includes the whole exome sequencing data from32 pairs of gallbladder caner tissues and patient-matched normal tissues.

  Dataset EGAD00001000901

• SAFIR02_Oncoscan

  Genomics to select patients with metastatic breast cancer for targeted therapy (microarray_oncoscan)

  Dataset EGAD00010002241

• BAM/BAI dataset of Colorectal Cancer Synthetic genomes

  This dataset contains binary alignment map (BAM) data with their respective index (BAI) from four patients synthetically generated.

  Dataset EGAD50000000316

• Michigan-Georgetown Cancer DAC

  Genomics data from cancers and/or dysplasias for the collaborations between Maureen Sartor and Nisha D'Silva at the University of Michigan and Laura Rozek at Georgetown University

  Dac EGAC50000000397

• RNAseq for 208 patients with medulloblastoma cancer (MB_COMICS cohort)

  This dataset contains 208 paired fastq files sequenced with Illumina HiSeq 2500.

  Dataset EGAD50000000600