9296 results for "cancer rna-seq"

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• RNA-seq from islet differentiation model

  Whole transcriptome RNA sequencing (RNA-seq) of human induced pluripotent stem cell lines from three independent donors at seven islet developmental stages: definitive endoderm (DE), primitive gut tube (GT), posterior foregut (PF), pancreatic endoderm (PE), endocrine progenitors (EP), endocrine-like cells (EN), and beta-like cells (BLC).

  Dataset EGAD00001003807

• Meso RNA-seq data (SSA cell line study)

  RNA-seq data for mesothelioma cell lines after spliceostatin (SSA) or control (DMSO) treatment.

  Dataset EGAD00001001914

• Single Cell RNA Seq GBM

  Single Cell RNA sequencing for 4 high grade glioma samples. NovaSeq6000 was used for snRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005369

• Single Cell RNA Seq LGG

  Single Cell RNA sequencing for 5 low grade glioma samples. NovaSeq6000 was used for scRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005368

• Human tumor bulk RNA-seq

  We profiled 9 high-grade gliomas patient tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001008348

• WGS and RNA-Seq data from a GBM patient PT-SS3647

  WGS and RNA-Seq data from a GBM patient PT-SS3647

  Dataset EGAD00001008530

• RNA-seq

  Stranded RNA-seq libraries were performed for 150 ng of mRNA using the TruSeq library kit (Illumina, San Diego, CA, USA). Libraries were sequenced on a NextSeq 2000 (Illumina) in a 2x50bp length.

  Dataset EGAD00001010841

• Systematic comparative analysis of single-nucleotide variants detection methods from single-cell RNA sequencing data

  Systematic interrogation of single nucleotide variations (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single cell level. While SNV detection from abundant single cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Here, we performed a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, using both simulation and scRNA-seq datasets, and identified multiple elements influencing their performance. Our study provided the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

  Study EGAS00001003883

• RNA-seq following miR-130a overexpression (OE) in human CD34+cord blood cells

  We performed RNA-seq on CD34+ human cord blood (CB) cells transduced with Control or miR-130a OE lentiviruses to identify differentially expressed genes. Sorted CD34+ cells from 3 independent cord blood pools were transduced with lentiviruses containing mOrange (mO) reporter gene. mO+ cells were sorted 3 days post-transduction and RNA was harvested for library preparation and sequencing.

  Study EGAS00001005868

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. One possibility for this inefficiency is an incomplete understanding of the molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to develop a foundational dataset for identifying candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Study EGAS00001003667