12310 results for "cancer rna-seq"

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• Oncogenic FOXL2C134W has gain-of-function chromatin remodeling activity that reprograms glucocorticoid receptor occupancy to promote ovarian granulosa cell tumor growth

  This dataset contains bam files from the whole exome sequencing performed on granulosa cell tumors and 7 sequencing experiments performed on human-derived KGN granulosa cell tumor cells and isogenic cell lines. The latter include transcription factor ChIP-seq for Foxl2 as well as transcription factor ChIP-seq for Foxl2 and GR under dexamethasone treatment and untreated conditions, two ATAC-seq data sets, two RNA-seq data sets, and Histone marks ChIP-seq.

  Dataset EGAD50000000880

• Human embryonic stem cells dopaminergic neurons scRNA-seq

  The dataset includes 2 samples of Single cell RNA-seq (10X Genomics) from Human Embryonic stem cells. 2 samples with 2 replicates of timepoints 17 and 28 of dopaminergic neuron differentiation.

  Dataset EGAD50000001592

• Single nucleus and spatial transcriptomic characterization of prostate cancer versus normal controls

  10x Genomics Visium and Chromium platforms were used for spatial and single-nuclei RNA-sequencing, respectively. Cohort consisted of 8 prostate cancer patients (8 prostate cancer + 8 paired non-malignant control samples, fresh-frozen)

  Study EGAS50000001143

• RNA-Seq Profiles from the CheckMate-577 Clinical Trial

  The dataset contains RNAseq profiles of 386 patients from the CheckMate-577 (CA209-577) clinical trial whose ICF allows data deposition into a public repository. The Allprep DNA/RNA FFPE kit was used to simultaneously purify genomic DNA and total RNA from formalin-fixed, paraffin embedded (FFPE) tissue sections. RNA was analyzed using the Illumina TruSeq RNA Access method for library preparation, followed by sequencing on the Illumina NovaSeq platform with a 50bp paired-end strategy and a target read depth of 50M per sample. Fastq files are included.

  Dataset EGAD50000002394

• 10X single-cell Multiome (RNA+ATAC) of cord blood-derived HSPC

  10X genomics chromium single-cell ATAC+RNA (Multiome) was use to prepare single-nucleus RNA- and ATAC-seq libraries, sequenced on an Illumina NovaSeq 6000 platform. BAM files are provided. Samples from 4 donors were sequenced after sorting for primitive HSPCs in 2 libraries, each with cells from 1 male and 1 female CB.

  Dataset EGAD50000002327

• Multi-omic analysis of Down Syndrome in thyroid

  Background: Down syndrome (DS) is a chromosomal disorder characterized by trisomy of chromosome 21 and is associated with various health problems. Congenital thyroid dysfunction is a common endocrine abnormality in DS, but its exact cause remains unknown. This work aimed at studying the etiology of congenital thyroid dysfunction in DS. Methods: Thyroid tissue of fetuses with DS (n=4) and fetuses without a genetic/developmental abnormality (n=5) were analyzed using histopathological evaluation, RNA sequencing (RNA-seq), and DNA methylation (DNAm) profiling. Results: Histological examination showed abnormally developed DS thyroid tissue compared to non-DS/healthy tissue. RNA-seq analysis showed numerous differences in gene expression between DS and healthy thyroid tissue, including significant downregulation of thyroid genes FOXE1, IYD and DIO2. Gene set enrichment analysis showed disruption of vital cellular processes and functions in DS fetal thyroid tissue. Analysis of DNAm and expression quantitative trait methylation (eQTM) analysis identified functional and biologically relevant associations, that can be linked to plausible disrupted pathways in DS. Conclusions: Based on these observations, we conclude that congenital non-autoimmune thyroid dysfunction in DS is probably a DS-specific form of thyroid dysfunction, characterized by abnormal thyroid development, altered expression of genes that regulate thyroid development and hormone production, and altered expression and DNAm of genes pertaining to numerous vital pathways leading to significant metabolic disturbances. The disturbances as investigated by multi-omics analyses (RNA-seq, DNAm and eQTM analyses) are widely spread over the genome, and presumably caused by the direct and downstream effects of overexpressed chromosome 21 genes (dosage effect), and (epi)genomic disturbances.

  Study EGAS00001007677

• IgM heavy chain V(D)J library sequencing using varied PCR parameters

  When amplifying the rearranged V(D)J genes encoding antigen receptors during adaptive immune receptor repertoire sequencing (AIRR-seq), each cycle of the PCR can produce spurious “chimeric” hybrids of two or more different template sequences. To test the effects of PCR protocol variation on chimera formation in AIRR-seq, we produced IgM heavy chain V(D)J repertoire libraries under 8 conditions, varying the number of PCR cycles, indexing PCR template, input RNA, and extension time. Three donors were sequenced using all 8 conditions.

  Study EGAS50000001037

• Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

  Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

  Study EGAS00001003973

• Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

  Patients with metastatic urothelial cancer (n=29) were treated with atezolizumab (anti-PD-L1). All 29 patients had T-cell receptor sequencing (TCR-seq) of the pre-treatment blood; 24 also had TCR-seq on at least one post-treatment blood sample. 24 patients had TCR-seq of their pre-treatment tumors. TCR-seq data can be found at http://doi.org/10.5281/zenodo.546110. 26 patients also had RNAseq of their tumors and whole exome sequencing (WES) of their tumors and matched normal blood. WES results for one sample were excluded from our analysis after failing to meet coverage requirements.

  Study phs001743

• Single-Cell Sequencing Reveals Distinct Microenvironment Cell Types Associated with Response to High Dose Melphalan and Autologous Stem Cell Transplant in Multiple Myeloma

  The aim of this study is to understand the transcriptome of microenvironment cells in pre- and post- autologous stem cell transplantation (ASCT) samples between patients with complete response and partial response to transplant. Here, we studied immune cells from 40 multiple myeloma patients who have undergone ASCT. Samples were collected from 40 multiple myeloma patients before and after ASCT. Samples were sorted using magnetic activated cell sorting (MACS) and the non-tumor CD138- fraction underwent 3' single cell RNA-sequencing (scRNA-seq) or 5' scRNA-seq + 5' T cell receptor sequencing (TCRseq), to determine the scRNA transcriptome and TCR repertoire. This study includes 24 samples with 3' scRNA-seq, 56 samples with 5' scRNA-seq, and 56 samples with 5' TCRseq data.

  Study phs003219