11770 results for "cancer rna-seq"

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• SF11979 scRNA-Seq Primary GBM IDHR132H Wild-type Female

  Single Cell Prmary high grade glioma IDHR132H Wild-type Female 76 Single Cell RNA seq from high grade primary glioma sample. NovaSeq6000 was used for RNA seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005429

• 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

• Defining and Overcoming Intrinsic T Cell Dysfunction to Enable Pediatric Immunotherapy

  T-cells from 71 children and young adults with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy were analyzed. These T-cells represent the pre-manufacture T-cells that were subsequently used for CAR T manufacture. Bulk RNA-Seq was performed on sorted T-cell subsets for all patients, in addition to CITE-Seq and single-cell ATAC-Seq on a subset of 6 patients. The primary clinical outcome was duration of B-cell aplasia, a surrogate measure of functional CAR T-cell persistence. From these data, molecular signatures associated with long and short-term CAR T-cell persistence were identified. This data submission includes bulk RNA-Seq, CITE-Seq, and single-cell ATAC-Seq data from this study.

  Study phs002323

• 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

• Identification of Recurrent SMO and BRAF Mutations in Ameloblastomas

  RNA-seq data from two cases of ameloblastoma was analyzed for candidate gene fusions and point mutations. Recurrent point mutations identified in the RNA-seq data, as well as mutations identified with limited panel targeted deep sequencing, were validated as somatic. Expanding to a larger cohort of 28, one of two activating mutations was found in 80% of cases. No recurrent gene fusions events were identified.

  Study phs000739

• Transcriptome analysis of iPSC-derived hepatocytes from Wilson's Disease patients and healthy controls

  To identify the differences in global gene expression patterns in WD-specific hepatocytes, we performed RNA-seq analysis. Six WD-specific hepatocytes and 6 healthy-donor (HD) hepatocytes were compared in total. The data suggested that our RNA-seq data were partially validated in the context of ATP7B deficiency and that these overlapped genes might be regulated by ATP7B deficiency among mammalian species in common.

  Study JGAS000382

• Discriminating Th17.1 cell driven sarcoidosis-like inflammation from relapse after anti-BCMA CAR T cells in multiple myeloma

  We present a case study on sarcoidosis-like flare-up after Idecabtagen Vicleucel (Ide-cel), a BCMA targeting CAR T cell therapy, and identified a Th17.1 driven autoimmune mechanism as the biological underpinning of this phenomenon using single-cell RNA-seq analysis. Furthermore, single-cell RNA-seq allowed to discriminate between immune-mediated changes and true relapse after CAR T cell treatment.

  Study EGAS00001006133

• Transcriptomic subgroups in soft tissue tumors correlate with morphologic subtype, genomic features, and outcome

  We assessed gene expression (GGE) profiles and gene fusions as detected by transcriptome sequencing (RNA-seq) of 704 soft tissue tumors (STT) representing 56 different tumor types. Comparison with follow-up data identified transcriptional subclusters among sarcomas with complex genomes and among adipocytic tumors that correlated with metastasis-free survival and local recurrences, respectively. Thus, RNA-seq analysis of STT provides clinically important information.

  Study EGAS50000001472

• CLL RNA-seq

  This dataset contains Illumina stranded RNA-seq from of 19 chronic lymphocytic leukemia (CLL) patients with or without mutation in SF3B1, as well as 6 B cell samples from healthy individuals. RNA was extracted from CLL cells.

  Dataset EGAD50000000078

• RNA Seq Data

  Human melanoma samples were collected pre, on, and progression on BRAF inhibitor therapy. RNA was extracted and run on RNA-seq. This has provided insights into different categories of BRAF inhibitor resistance mechanisms.

  Dataset EGAD00001001306