4728 results for "RNA AND sequencing OR transcriptome"

in 10.76 milliseconds.

• CPM table for POPLAR (GO28953)

  This dataset contains counts per million for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008631

• Log2 normalized TPM matrix for POPLAR (GO28753)

  This dataset contains log2(TPM + 1) for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008390

• Log2 normalized TPM matrix for OAK (GO28915)

  This dataset contains log2(TPM + 1) for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).

  Dataset EGAD00001008391

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the feature of age-associated helper T (ThA) cells and its contribution to autoimmune diseases, various peripheral blood immune cell subsets from 136 systemic lupus erythematosus (SLE) and 89 healthy controls (HC) were collected (Naive_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, ThA, Naive_CD8, Naive_B, SM_B, USM_B, DN_B, Plasmablast, NK, CD16p_Mono, CL_Mono, Neu, mDC, pDC, TEMRA_CD8, CM_CD8, EM_CD8, NC_Mono, Int_Mono, LDG). RNA-seq was performed with each cell subset samples.

  Study JGAS000627

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the regulation of gene expression in immune cell subsets and its contribution to autoimmune diseases, 24 peripheral blood immune cell subsets from 50 Systemic Sclerosis and 48 Healthy Controls were collected (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono, Int_Mono, NC_Mono, mDC, pDC). RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000296

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  Various peripheral blood immune cell subsets from 89 healthy volunteers and 136 systemic lupus erhythematosus (SLE) donors were collected (Naive_CD4, Mem_CD4, Th1, Th2, Th17, Tfh, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Naive_CD8, EM_CD8, CM_CD8, TEMRA_CD8, NK, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, Neu, LDG). 22 SLE donors were analyzed longitudinally. RNA-seq was performed with each immune cell subset samples. After gene expression quantification samples were filtered.

  Study JGAS000486

• Single-cell and spatial atlas of steatotic liver disease-related hepatocellular carcinoma

  This study consists of the spatial transcriptomics data generated by : 1. Visium CytAssist Spatial Gene Expression for FFPE (10x) for steatotic liver disease-associated hepatocellular carcinoma (SLD-HCC) (n=7) and non-SLD-HCC (n=5) 2. CosMxTM Human Universal Cell Characterization RNA Panel (1000-plex); NanoString, USA for n=4 SLD-HCC and n=4 non-SLD-HCC. The goal of this data is to compare SLD-HCC vs non-SLD-HCC as well as response to immunotherapy

  Study EGAS50000001034

• BAM files of total RNA-Seq data of POPS control samples (GRCh37)

  Placental biopsies (n = 64 female placentas, n = 67 male placentas) were selected from healthy pregnancies from the POPs cohort. A quality control process was also applied for the RNA-Seq datasets: reads were trimmed with Trim Galore!, which uses cutadapt internally and were mapped to the same version of human genome reference (hg19). TopHat2, a splice-aware mapper built on top of Bowtie2 short-read aligner, was used in the mapping process in which so-called two-pass (or two-scan) alignment protocol was applied to rescue unmapped reads from the initial mapping step. In the second mapping, previously unmapped reads were re-aligned to the exon-intron junctions detected in the first-mapping by TopHat2 and were combined across all 131 placenta samples. The initial and second mapped reads were merged by samtools

  Dataset EGAD00001003462

• RNA-Seq dataset for Genomic rearrangements in Pediatric Cancer

  RNA-Seq data for systematic gene fusion detection in Pediatric Cancer

  Dataset EGAD00001008152

• Whole exome and transcriptome analysis of UV-exposed epidermis and carcinoma in situ reveals early drivers of carcinogenesis

  Squamous cell carcinoma in situ (SCCIS) is a prevalent precancerous lesion that can progress to cutaneous squamous cell carcinoma (cSCC). Although SCCIS is common, its pathogenesis remains poorly understood. To better understand SCCIS development, we performed laser-captured microdissection of human SCCIS and adjacent epidermis to isolate genomic DNA and RNA for next generation sequencing. Whole exome sequencing (WES) identified UV-signature mutations in multiple genes including Notch 1-3 (GeneID:4851, GeneID:4853, GeneID:4854) in the epidermis and SCCIS and oncogenic TP53 (GeneID:7157) mutations in SCCIS. Novel gene families, including SCHLAFEN genes, contained UV/oxidative-signature disruptive (UVD) epidermal mutations that manifested positive selection in SCCIS. The frequency and distribution of NOTCH and TP53 mutations indicate that NOTCH mutations may precede TP53 mutations. RNA sequencing identified 1166 differentially expressed genes; the top 5 enriched GO biological processes included: 1) immune response, 2) epidermal development, 3) protein phosphorylation, 4) regulation of catalytic activity, and 5) cytoskeletal regulation. The NEURL1 ubiquitin ligase, which targets Notch ligands for degradation, was upregulated in SCCIS. Neuralized 1 protein was found to be elevated in SCCIS suggesting that increased levels could represent a novel mechanism for downregulating Notch during UV-induced carcinogenesis. The data from DNA and RNA sequencing of epidermis and SCCIS provide insights regarding SCCIS formation.

  Study phs002019