11932 results for "cancer rna-seq"

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• MESA colorectal cancer cfDNA EM-seq dataset

  This dataset contains the methylation sequencing data of 60 nonCancer and 70 colorectal cancer cfDNA samples. The methylation library is constructed by using NEBNext Enzymatic-seq Kit.

  Dataset EGAD00001009165

• Enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors

  Most pancreatic neuroendocrine tumors (PNETs) do not produce symptoms of hormonal excess and are hence considered “non-functional”. Their clinical behaviors vary widely, emphasizing the need for a robust classification with prognostic power. Using enhancer maps to infer regulatory programs, we find that the large majority of non-functional PNETs fall into two major sub-types that reflect alpha and beta endocrine cell ontogeny, respectively. Tumors of the different subtypes have similar clinical presentations and histology, but express distinct lineage-specifying transcription factors, ARX or PDX1. Here we provide the raw ChIP-seq and RNA-seq data of the PNET cohort in this study, as well as ChIP-seq data of ileal carcinoids.

  Study phs001910

• 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

• Pancreatic cancer RNA sequencing

  Study EGAS00001004706

• 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

• Time course acetalax bisacodyl treatment

  RNA-Seq raw data of PDX treated with acetalaxor bisacodyl for 4h00 to 24h00.

  Dataset EGAD50000000875

• PACA-CA RNASeq bam files

  Bam files for PACA-CA RNA Seq analysis, for DCC release 27

  Dataset EGAD00001003945

• Microvascular Permeability, Inflammation, and Lesion Physiology in Endometriosis: A Microphysiological Systems Approach

  The goal of the study is to identify novel molecular pathways involved in the etiology of endometriosis and to develop in vitro model systems to enable the study of the mechanisms regulating reproductive function. Using a physiomimetic modeling approach, primary tissue samples of the human endometrium derived from patients diagnosed with and without endometriosis and/or endometriotic diseases (e.g. adenomyosis) were used for secondary analysis using multi-omic systems biology analysis and to build tissue engineered models. We performed single cell RNA sequencing (scRNA-Seq, N=6)) to study the cellular and transcriptomic composition of the endometrial tissue microenvironment and proteomics (LC-MS/MS) to study protein-level signaling pathways and the matrisome of the native endometrium. Systems biology analysis of these datasets helped identified a putative dysregulated inflammatory signaling pathway in endometriosis patients compared to controls. Moreover, subsets from these scRNA-Seq and proteomic datasets were used to aid the design of a synthetic extracellular matrix hydrogel as an in vitro platform for the culture of endometrial cells. Tissue-engineered synthetic PEG hydrogel enables the simultaneous co-culture of endometrial epithelial organoids and stromal fibroblasts in a fully defined, 3D, and tunable environment. We demonstrated that the in vitro co-culture model recapitulates key phenotypic processes observed across the human menstrual cycle in response to sex hormone stimulation through both biochemical and transcriptomic (bulk RNA-sequencing) analysis. This model will enable future mechanistic studies that examine origins of endometriotic disease phenotypes. The transcriptomic data generated for this study including the scRNA-Seq data from primary human endometrial tissues and the bulk RNA-Seq datasets from the experimental co-culture models is available through dbGAP.

  Study phs003326

• Single cell RNAseq and TCRseq data from tumor and blood samples from 4 patients with muscle invasive bladder cancer

  For this dataset we performed single cell RNAseq paired with single cell TCR-seq on tumor and blood samples from 4 patients. This dataset contains 4 tumor samples as well as 4 blood samples. Each sample is made up of 2 sets of paired fastq files. The first pair contains reads corresponding to RNA transcripts (_Transcripts in file name), while the second pair contain reads corresponding to TCRs (_VDJ in file name). Sequenced on the Illumina NovaSeq6000 platform in a paired-end run using an SP flow cell (v1.5, 300 cycles).

  Dataset EGAD50000001381

• p200503_fn2

  RNA-Sequencing data of Fusobacterium nucleatum (Fna 71) treated CRC patient derived (CAF180, CAF181) and CAF05 cancer associated fibroblasts (CAFs).

  Study EGAS50000001121