9295 results for "cancer rna-seq"

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• Reference epigenome ADMSC06 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC06 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007381

• Reference epigenome ADMSC06 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007382

• Reference epigenome ADMSC06 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007383

• Reference epigenome ADMSC06 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007384

• Reference epigenome ADMSC06 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007385

• Reference epigenome ADMSC06 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007386

• Reference epigenome ADMSC06 input ChIP-Seq data generated from KEP study

  ADMSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007387

• Reference epigenome ADMSC07 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC07 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007388

• Reference epigenome ADMSC07 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC07 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007389

• Reference epigenome ADMSC07 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007390

• Reference epigenome ADMSC07 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007392

• Reference epigenome ADMSC07 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007393

• Reference epigenome ADMSC07 input ChIP-Seq data generated from KEP study

  ADMSC07 input ChIP-Seq paired end data

  Dataset EGAD00001007394

• Reference epigenome ADMSC08 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC08 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007395

• Reference epigenome ADMSC08 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007396

• Reference epigenome ADMSC08 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007397

• Reference epigenome ADMSC08 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007398

• Reference epigenome ADMSC08 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007399

• Reference epigenome ADMSC08 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007400

• Reference epigenome ADMSC08 input ChIP-Seq data generated from KEP study

  ADMSC08 input ChIP-Seq paired end data

  Dataset EGAD00001007401

• ATAC-seq dataset

  This dataset contains ATAC-seq data performed in MM.1S cell line in ETOH (control) or Dexamethasone condition (Treatment)

  Dataset EGAD00001011135

• Pathogenic Neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

  Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. an in-house developed tool (QURNAS) was used to calculate the enrichment score (ERS) for each splicing event. RNA enrichment of NF1 and SPRED1 was done using SPET (NUGEN - NF1 only) and using SureSelect (Agilent - NF1 and SPRED1).

  Study EGAS00001005530

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• Identification_of_cardiovascular_biomarkers_through_an_integrative_omics_approach

  Recent GWAS studies have made extensive use of large eQTL data sets to functionallyannotate index SNPs. With a large number of association signals located outside codingregions there has been an intense search among sequence variants affecting geneexpression at the transcriptional level. However, little progress has been made in mappingregulatory variants that affect protein levels at the translational or post-translational level. It isnow possible to undertake a protein QTL scan for focused sets of e.g. oxidized proteins bymass spectrometry. We have established a collaboration with a longitudinal, family-basedstudy in France, the Stanislas cohort, which comprises circa 1000 nuclear families (4,295individuals) and has follow up data for 10 years (three visits). We have undertaken a pilotstudy in a focus set of 257 subjects from 79 families with the aim to integrate GWAS,transcriptomic and DNA methylation data with proteomic data on a set of 100 proteinsmeasured in PBMCs. We have already generated GWAS data using Illumina's core-exomechip as well as DNA methylation profiles with the 450K array. We propose to use RNA seq togenerate transcriptomic data of the corresponding PBMCs.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000711

• An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors

  We undertook an empirical approach to dissect the tumor microenvironment by developing a bulk tumor dissection algorithm, DisHet, and leveraging RNA-Seq data of tumorgrafts (patient-derived tumors implanted in mice), in which only the tumor cell component expands. We found that approximately 65% of previously defined immune signature genes are not abundantly expressed in the Renal Cell Carcinoma (RCC) microenvironment, and we identified more than two times as many novel immune/stromal transcripts. By using refined immune/stroma-specific genes and genomics, electronic medical record data and imaging data of 1084 RCC patients, we discovered a highly-inflamed pan-RCC subtype enriched for Treg cells, NK cells, Th1 cells, neutrophils, macrophages, B cells, and CD8+ T cells. This inflamed subtype (IS) is enriched for aggressive RCCs, including BAP1-deficient clear-cell and type 2 papillary tumors. Interestingly, IS is correlated with systemic manifestations of inflammation in patients such as thrombocytosis and anemia, whose pathogenesis were poorly defined and have been predictors of poor prognosis in RCC. Indeed, we discovered that IS is a strong predictor of poor survival. Lastly, our analyses show that tumor cells may drive stromal immune response. Overall, these data provide a missing link between the tumor cells, the tumor microenvironment, and systemic factors.

  Study EGAS00001002786