9295 results for "cancer rna-seq"

in 42.00 milliseconds.

• Ewing's sarcoma sequencing data

  This study shares DNA and RNA sequencing data for patients with Ewing's sarcoma

  Study EGAS00001005689

• Gene expression in brain (Schizophrenia) study

  The study included post-mortem brain tissue samples from 68 schizophrenia patients and 44 age and sex matched control subjects. Whole transcriptome poly-A selected paired-end RNA sequencing was performed on tissue from prefrontal cortex and orbitofrontal cortex. RNA expression differences were detected between case and control individuals, focusing both on single genes and pathways.

  Study EGAS00001004199

• An Organoid Biobank of Rare Human Neuroendocrine Neoplasms Enables Genotype-Phenotype Mapping

  We performed whole genome sequencing and RNA-sequencing of neuroendocrine neoplasm. The aim of this study was to provide genetic landscape of neuroendocrine neoplasm, to construct available animal models, and to engineer neuroendocrine neoplasm from normal colon epithelium. The reference genomes used are GRCh37 for whole genome sequencing and GRCh38 for RNA-sequencing.

  Study JGAS000237

• Genomic characterization of a patient with AGS and CdLS transcriptomic comparison of his monocytes against healthy and disease control samples

  In this study, we describe an extreme genetic phenotype of two simultaneous genetic lesions, leading to broad alterations in immunophenotype and transcriptome. Whole Exome Sequencing and RNA-Sequencing of CD14+ monocytes was performed on the case patient, and RNA-sequencing was also performed on disease (Systemic Lupus Erythematosus) and healthy controls.

  Study EGAS00001007829

• Nasopharyngeal RNASeq Comparing SARS-CoV-2+ Patients and SARS-CoV-2 Negative Control Subjects

  The goal of this analysis was to compare RNAs enriched in the upper respiratory tract of SARS-CoV-2 patients and SARS-CoV-2 negative controls. RNA-sequencing of RNA isolated from the viral transport medium associated with nasopharyngeal swab samples from SARS-CoV-2+ patients and SARS-COV-2 negative controls.

  Study phs002433

• scDNA-seq

  Single cell DNA-seq data (CLL gene panel - Tapestri single-cell DNA CLL panel, Mission Bio)

  Dataset EGAD00001008960

• Dataset to study clonal evolution in iAMP21 patient SJBALL021901 using scWGS-seq

  scWGS-seq of flow sorted blast and normal cells from SJBALL021901 with 71 high quality cells sequenced (67 blast and 4 normal)

  Dataset EGAD00001009755

• Bulk ATAC-Seq Illumina NextSeq 500 (10M reads)

  Human data for chromatin accessibility (ATAC-Seq) in eight B-cell precursors: HSC, CLP, pro-B, pre-B, Immature B, Transitional B, Naive B CD5-, Naive B CD5+ cells

  Dataset EGAD00001010908

• Bulk ATAC-Seq HiSeq2500 v4 reagents (100M reads)

  Human data for chromatin accessibility (ATAC-Seq) in eight B-cell precursors: HSC, CLP, pro-B, pre-B, Immature B, Transitional B, Naive B CD5-, Naive B CD5+ cells

  Dataset EGAD00001010909

• Genome-wide DNA-methylation assessment by MethylCap-seq and Infinium HumanMethylation450 BeadChips: an independent large-scale comparison

  DNA-methylation is an important epigenetic feature in health and disease. Two cost-efficient genome-scale methodologies to assess DNA-methylation are MethylCap-seq and Illumina's Infinium HumanMethylation450 BeadChips (HM450). However, objective information regarding the best-suited methodology for a specific research question is scant. Therefore, we performed a large-scale evaluation on a set of 70 brain tissue samples obtained from 65 glioblastoma and 5 non-tumoral brain tissues, using a gold standard free Bayesian modeling procedure. While conditional specificity was adequate for both approaches, conditional sensitivity was systematically higher for HM450. Also the genome-wide characteristics were compared, revealing that the HM450 probes assess less than 10% of the regions identified as methylated by MethylCap-seq. Hence the latter method may detect more potentially relevant DNA-methylation, defined by either functional location or previously reported differentially methylated candidate regions. Our results therefore indicate that – at least for the tissue under study - both methodologies are complementary, with a higher sensitivity for HM450, but a far larger genome-wide coverage for MethylCap-seq. Note that here only the relevant MethylCap-seq data is deposited, for the HM450 data we refer to GEO (GSE60274).

  Study EGAS00001001191