8224 results for "canc*"

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• RNA-seq data from the tumor samples of a head and neck cancer patient

  This dataset contains paired-end RNA sequencing data (2x50 bp) from the three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006654

• UROMOL 2020 - RNA-seq data

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 535 patients. Sequencing of total RNA was performed using ScriptSeq-v2 RNA-Seq Library Preparation Kit (Illumina) and KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 500 ng for both kits. The dataset is composed of 1,596 fastq files.

  Dataset EGAD00001006656

• 41 CRC Exome bam + 4 CRC paired fastq from EGAS00001002477 study

  This dataset contain 45 pairs of colorectal tumor and adjacent normal tissue. Four of them are from a previous study EGAS00001002477. For each sample a BAM was generated by aligning to GRCh37. Those colorectal cancer all have the MSI phenotype.

  Dataset EGAD00001006666

• 133 CRC RNA-seq fastq

  This dataset contain 133 pairs of colorectal tumor and adjacent normal tissue. For each sample paired RNA-seq fastq were generated using an Illuma Myseq-2000. Those colorectal cancer comprise 101 MSI and 32 MSS tumors.

  Dataset EGAD00001006667

• 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

• HiC files for Zhang GenomePaint paper

  HiC files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006678

• WGS files for Zhang GenomePaint paper

  WGS files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006679

• RNASeq files for GenomePaint paper

  RNASeq files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006680

• Whole Exome Sequencing of High grade T1 non-muscle invasive bladder cancer

  The dataset consists of a multisample VCF (version 4.1) and the corresponding annotated MAF file, containing the somatic point mutations found from exome sequencing across the high-grade T1 bladder cancer cohort (HGT1, n=61 samples). The VCF file is in accordance with the HTS format specifications (https://samtools.github.io/hts-specs/). The dataset comprises also a CSV file with clinical data.

  Dataset EGAD00001006346

• Sequencing data for oesophageal and related samples - Katz-Summercorn, Jammula et al (WGS)

  Data supporting: “Multi-omic cross-sectional cohort study of pre-malignant Barrett’s esophagus reveals structural variation and retrotransposon activity occur early in cancer evolution.” Katz-Summercorn, Jammula et al. WGS (BAM files)

  Dataset EGAD00001006349