7192 results for "({!join from=dataset_studies to=id v=$q1}) OR ("transcriptome" OR "RNA-seq" OR "rna sequencing" OR "rna-sequencing")"

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• RNA-sequencing data from metastatic Castration-Resistant Prostate Cancer (mCRPC)

  The goal of this study is to characterize the transcriptional landscape of advanced prostate cancer

  Study EGAS00001005954

• 17 scRNAseq samples

  17 samples were processed for single cell RNA sequencing (SORT-seq)

  Dataset EGAD00001007523

• Kidney tumour_DNA (2018-09-19)

  This is a bulk DNA and RNA sequencing study of human renal tumours . This dataset contains all the data available for this study on 2018-09-19.

  Dataset EGAD00001004346

• Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy

  Background: Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits including cardiovascular diseases.Results: Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 149 (97)* patients with dilated cardiomyopathy and 113 (108)* non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage and allele specific expression. Systematic annotation of genome wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome wide association signals in two independent cohorts.Conclusions: RNA transcription, splicing and allele specific expression are each important determinants of the DCM phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics. (*) numbers in parentheses represent samples that the publication is based on.

  Study EGAS00001002454

• BIH COVID-19 airway single-cell, long-read RNA-seq

  We performed single-cell, long-read RNA-seq on nine nasopharyngeal swabs from patients hospitalized at Charite to study splicing alterations induced by SARS-CoV-2 infection. Additionally, three samples from healthy controls were included.

  Dac EGAC50000000757

• Massachusetts General Hospital Cancer Center DAC for single cell RNA-seq data from small cell lung cancer primary tumors and CTCs

  single cell RNA-seq data from 10 small cell lung cancer primary tumors and circulating tumor cells of three small cell lung cancer patients

  Dac EGAC50000000808

• WGS and RNA-Seq data from a GBM patient PT-LC6372

  WGS and RNA-Seq data from a GBM patient PT-LC6372

  Dataset EGAD00001008522

• WGS and RNA-Seq data from a GBM patient PT-DS9789

  WGS and RNA-Seq data from a GBM patient PT-DS9789

  Dataset EGAD00001004233

• WGS and RNA-Seq data from a GBM patient PT-GJ3716

  WGS and RNA-Seq data from a GBM patient PT-GJ3716

  Dataset EGAD00001004240

• WGS and RNA-Seq data from a GBM patient PT-KM5291

  WGS and RNA-Seq data from a GBM patient PT-KM5291

  Dataset EGAD00001004249

• WGS and RNA-Seq data from a GBM patient PT-JW6420

  WGS and RNA-Seq data from a GBM patient PT-JW6420

  Dataset EGAD00001004248

• WGS and RNA-Seq data from a GBM patient PT-SK0976

  WGS and RNA-Seq data from a GBM patient PT-SK0976

  Dataset EGAD00001004260

• WGS and RNA-Seq data from a GBM patient PT-PD6881

  WGS and RNA-Seq data from a GBM patient PT-PD6881

  Dataset EGAD00001004255

• WGS and RNA-Seq data from a GBM patient PT-LC3356

  WGS and RNA-Seq data from a GBM patient PT-LC3356

  Dataset EGAD00001004250

• WGS and RNA-Seq data from a GBM patient PT-EV3071

  WGS and RNA-Seq data from a GBM patient PT-EV3071

  Dataset EGAD00001004234

• Prediction and quantification of splice events from RNA-seq data

  Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package SGSeq.

  Study EGAS00001001026

• Molecular characterization of T-PLL by Next-Generation Sequencing

  Patients with T-cell prolymphocytic leukemia (T-PLL) were profiled with multiple OMICS approaches based on Next-Generation Sequencing (NGS). In total, data from RNA-Seq, Whole-Exome Sequencing, Whole-Genome Sequencing and amplicon panel analyses in 134 samples are available. All samples were processed as paired-end libraries on Illumina sequencing machines. The data are available as paired FastQ files.

  Dataset EGAD00001003825

• RNAseq from 6 organotypic co-cultures (OC cells bulk/OCSC with or without TME)

  The dataset contains samples of 6 organotypic co-cultures, assembled with patient-derived material from ovarian cancer (OC) patients. Tumor cells, both as bulk and as cancer stem cells-enriched (OCSC) populations, are cultured or not with in vitro peritoneal TME (for details see Battistini C et al, Tumor microenvironment-induced FOXM1 regulates ovarian cancer stemness, CDDis 2024). Dataset is composed by fastq file (paired end) type from bulk RNA-Seq.

  Dataset EGAD50000000523

• Single-cell RNA-seq and TCR-seq of synovial tissue and peripheral blood CD4+ and CD8+ T cells from patients with immune-mediated refractory arthritis

  In this study, we investigated CD4+ and CD8+ T lymphocytes from the synovial tissue and peripheral blood of patients with treatment-resistant immune-mediated arthritis using paired single-cell RNA and TCR-sequencing.

  Study EGAS50000000528

• ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A

  ZFHX4 as a transcription factor specifically induced during the in vitro differentiation of iPSC-derived mDANs. Depletion of ZFHX4 during neuronal differentiation led to a significant reduction in the number of mDANs while its overexpression did not lead to any changes in mDAN count. Identification of ZFHX4 target genes using CUT&Tag profiling and RNA-seq upon ZFHX4 knock-down (KD) revealed its involvement in cell cycle regulation during mDAN differentiation

  Dataset EGAD50000001605

• Monosomy 7 delineates a primitive acute myeloid leukemia with adverse prognosis and responsiveness to epigenetic therapy

  The dataset comprises 27 single-cell multiome (ATAC + GEX) datasets generated from 13 patients with AML, collected across different stages of disease progression. In addition, matched long-read sequencing data from 5 samples were used to enable longitudinal analyses throughout the manuscript. Finally, bulk RNA-seq and ATAC-seq datasets from the cell line used in this study are also included.

  Dataset EGAD50000002359

• Processed RNA-seq data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the TPM and raw counts tables.

  Dataset EGAD00001006199

• RNA-Seq FASTQs for Tang F. et al. Chromatin accessibility profiles of castration-resistant prostate cancers reveal novel subtypes and therapeutic vulnerabilities

  This dataset contains the FASTQ files for a portion of the samples in Tang F. et al. “Chromatin accessibility profiles of castration-resistant prostate cancers reveal novel subtypes and therapeutic vulnerabilities” published in Science. It contains 51 samples sequenced with Illumina HiSeq 2500 or HiSeq 4000. The remaining samples can be found at dbGaP: phs000909.v1.p1

  Dataset EGAD00001008598

• Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer towards androgen-independence

  ChIP-seq for AR, FOXA1 and H3K27ac in primary prostate tumors before and after 3 months of neoadjuvant enzalutamide treatment. RNA-seq expression data of primary prostate tumors before and after 3 months of neoadjuvant enzalutamide treatment.

  Dataset EGAD00001008562

• Stromal and dendritic cells from lymph nodes: single-cell RNA-seq

  Although cross-species transcriptional analysis has been generated for DCs, transcriptomic conservation between mouse and human FRCs at single-cell resolution has been unclear. To test whether GREM1+ FRCs might also play a role in DC homeostasis in humans, we performed scRNA-seq of CD45–PDPN+ stromal cells, as well as CD45+CD11c+ immune cells from healthy human LNs of three human donors. Data was generated using the 10x platform.

  Dataset EGAD00001008731