15028 results for "cancer studies using rna-seq"

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• Matched Pair Cancer Cell line Whole Genomes

  Matched Pair Cancer Cell line Whole Genomes

  Dataset EGAD00001000145

• ICGC prostate cancer miRNA sequencing

  ICGC prostate cancer miRNA sequencing

  Dataset EGAD00001000304

• Esophageal cancer

  Genome-wide NanoRCS of cfDNA from plasma of Esophageal cancer patients

  Dataset EGAD50000000221

• Pilot data of PDiamond Lung Cancer Data

  RNAseq pilot data of PDiamond Korean lung cancer data

  Dataset EGAD50000000843

• Genomic analysis of Japanese gastric cancer

  fastq of 345 Japanese gastric cancer

  Dataset EGAD00001004051

• Copy number profile of ctDNA in ovarian cancer patients

  This study analyzes the copy number profiles from circulating tumor DNA (ctDNA) in plasma samples withdrawn from ovarian cancer patients, in a subset of samples from the MITO16A clinical trial.

  Study EGAS00001008226

• Whole exome and transcriptome sequencing of biliary tract cancer

  To uncover the full repertoire of genetic and transcriptomic landscape of biliary tract cancer (BTC), we molecularly characterized BTC based on genetic and transcriptomic alteration profiles.

  Study EGAS00001000950

• Sequencing of Plasma DNA from breast cancer patients

  Dataset contains plasma DNA whole genome sequencing on 4 breast cancer patients. It also includes matched germline and tumour whole genome sequencing data. Two benign cancer patients were also sequenced and their plasma DNA and matched germline whole genome sequencing data are included in the dataset. Samples were sequenced on Illumina HiSeqX Ten.

  Dataset EGAD00001006869

• Mapping the Human Connectome - Structure, Function, and Heritability: Healthy Young Adults (Age 22-35 Years) Including Twins and their Non-Twin Siblings

  The Human Connectome Project (HCP) has acquired vast amounts of data about the pattern of long-distance connections (wiring) in the brains of large numbers of healthy participants aged 22-35 using cutting-edge MRI and MEG neuroimaging, and extensive behavioral testing. Types of MR data collected included diffusion MRI (dMRI), resting-state functional MRI (r-fMRI), and structural MRI at both 3T and 7T, and task-evoked functional MRI (t-fMRI) at 3T. Following an extensive period of development and optimization of data acquisition and analysis methods, at Washington University in St. Louis we studied 1,206 participants comprising twins and their non-twin siblings. A subset of 184 twins was scanned again at the University of Minnesota using a 7T scanner. A different subset of 95 twins was studied at St. Louis University using combined resting state and/or task-activated MEG. We were able to collect genetic data on 1142 of our 1206 participants, including 149 pairs of genetically-confirmed monozygotic twins (298 participants) and 94 pairs of genetically-confirmed dizygotic twins (188 participants). Overall, there are 457 different families in the study, as determined by genetic analysis. Our rich set of imaging, behavioral and genetic data - available through the Human Connectome database - will enable many types of analysis of brain circuitry, its relationship to behavior, and the contributions of genetic and environmental factors to brain circuits.

  Study phs001364

• Single cell transcriptomics in expanded Tregs of APS-1 patients

  Aim of study: Exploring single cell transcriptomics in expanded Tregs of with autoimmune polyendocrine syndrome type 1 (APS-1) patients (global gene expression, immune panel, TCR). N= 9 APS-1 patients and 9 healthy controls Protocol 1. Regulatory T cells (Tregs) were isolated from EDTA-blood, activated with anti-CD3/CD28 and IL2, expanded in cell culture for 14 days and stored at -150℃ in AB serum or FBS + 5% DMSO until use. 2. Cells were thawed, run through a Live/Dead column and carefully counted. 10000 Tregs were used as input for the 10x protocol. 3. cDNA was generated and amplified using Single Cell VDJ 5’ Gel Beads, Chromium Next GEM Chip K Single cell Kit, Next GEM Single Cell 5’ GEM Kit v2 and 5’ Feature Barcode Kit (all from 10x genomics). 4. Gene expression (GEX) libraries were generated by using the Library Construction Kit from 10x genomics. 5. T-cell receptor (TCR) libraries were generated using the Single Cell Human TCR Amplification Kit and the Library Construction Kit from 10x genomics. 6. 10x Genomics Target Hybridization Kit and Human Immunology Panel was used with GEX libraries. Only 8 patient samples and 8 control samples were used for the immune panel. 7. All libraries have a unique sample index (Dual Index TT Set A). 8. All samples were sequenced on Illumina NovaSeq 6000 on a NovaSeq S2 flow cell.

  Study EGAS50000000181