11942 results for "cancer rna-seq"

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• Reference epigenome Pancreas-Islet08 miRNA-Seq data generated from KEP study

  Pancreas-Islet08 miRNA-Seq single end data

  Dataset EGAD00001007189

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• Reference epigenome Islet-derived_iPSC04 mRNA-Seq data generated from KEP study

  Islet-derived_iPSC04 mRNA-Seq paired end data

  Dataset EGAD00001007880

• Reference epigenome Islet-derived_MSC08 mRNA-Seq data generated from KEP study

  Islet-derived_MSC08 mRNA-Seq paired end data

  Dataset EGAD00001007879

• Reference epigenome Islet-derived_MSC06 miRNA-Seq data generated from KEP study

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• Histone marks

  EZH2, H3K4me3, H3K27ac and H3K27me3 ChIP-seq data consisting of fastq single-end reads from peripheral blood CLL cells

  Dataset EGAD00001004063

• Characterization of genetic intratumor heterogeneity in colorectal cancer and matching patient-derived spheroid cultures.

  Patient-derived in vitro cultures of colorectal cancer (CRC) may help guide treatment strategies prior to patient treatment. However, most previous studies have been performed on a single biopsy per tumor. The purpose of this study was to analyze multiple spatially distinct biopsies from CRCs and see how well intratumor heterogeneity (ITH) was recapitulated in matching patient-derived spheroids. Three to five biopsies were collected from six CRC tumors. Each biopsy was split in two; one half was used for spheroid culturing, the other half was used for direct DNA and RNA purification. For two of the patients, lymph node metastases were analyzed. Somatic mutations were called from whole exome sequencing data. Each tumor contained multiple mutations shared across all biopsies and spheroids, including major CRC drivers such as APC, KRAS, and TP53. At the same time, all tumors exhibited ITH on both mutation and copy number level. The concordance between biopsies and spheroids ranged between 40-70% for coding mutations. For three of the patients the biopsy and spheroid from matching areas clustered together, meaning that the spheroid resembled the area of origin more than the other areas. However, all biopsies and spheroids contained private mutations. Thereby, multiple cultures from spatially distinct sites of the tumor increase the insight into the genetic profile of the entire tumor. RNA sequencing based molecular subtyping was stable and seemed independent of genetic ITH. In conclusion, all examined CRC tumors contained genetic ITH on mutation and copy number level. Spheroid cultures partly reflected this ITH, meaning that multiple cultures from distinct sites of the tumor improved the representation of the different genetic subclones in the primary tumor. This should be taken in to account when establishing patient-derived models for drug screening.

  Study EGAS00001002684

• 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

• Exome and RNA sequencing of Greenlanders

  This study contains whole exome sequence of 27 Greenlanders and deep RNA sequencing of 17 Greenlanders

  Study EGAS00001002727

• Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA-sequencing the pros and cons

  RNA sequencing data of a collection of 6 pediatric ependymoma cases

  Study EGAS00001006535

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001006398