9931 results for "cancer rna-seq"

in 65.47 milliseconds.

• Reference epigenome SMC07 h3k36me3 ChIP-Seq data generated from KEP study

  SMC07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007339

• Reference epigenome SMC07 h3k4me1 ChIP-Seq data generated from KEP study

  SMC07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007340

• Reference epigenome SMC07 h3k4me3 ChIP-Seq data generated from KEP study

  SMC07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007341

• Reference epigenome SMC07 h3k9me3 ChIP-Seq data generated from KEP study

  SMC07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007342

• Reference epigenome SMC07 input ChIP-Seq data generated from KEP study

  SMC07 input ChIP-Seq paired end data

  Dataset EGAD00001007343

• Reference epigenome SMC08 h3k27ac ChIP-Seq data generated from KEP study

  SMC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007344

• Reference epigenome SMC08 h3k36me3 ChIP-Seq data generated from KEP study

  SMC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007345

• Reference epigenome SMC08 h3k4me1 ChIP-Seq data generated from KEP study

  SMC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007346

• Reference epigenome SMC08 h3k4me3 ChIP-Seq data generated from KEP study

  SMC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007347

• Reference epigenome SMC08 h3k9me3 ChIP-Seq data generated from KEP study

  SMC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007348

• Reference epigenome SMC08 input ChIP-Seq data generated from KEP study

  SMC08 input ChIP-Seq paired end data

  Dataset EGAD00001007349

• Reference epigenome SMC09 h3k27ac ChIP-Seq data generated from KEP study

  SMC09 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007350

• Reference epigenome SMC09 h3k36me3 ChIP-Seq data generated from KEP study

  SMC09 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007351

• Reference epigenome SMC09 h3k4me1 ChIP-Seq data generated from KEP study

  SMC09 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007352

• Reference epigenome SMC09 h3k4me3 ChIP-Seq data generated from KEP study

  SMC09 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007353

• Reference epigenome SMC09 h3k9me3 ChIP-Seq data generated from KEP study

  SMC09 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007354

• Reference epigenome SMC09 input ChIP-Seq data generated from KEP study

  SMC09 input ChIP-Seq paired end data

  Dataset EGAD00001007355

• Reference epigenome ADMSC01 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007356

• Reference epigenome ADMSC01 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007357

• Reference epigenome ADMSC01 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007358

• Reference epigenome ADMSC01 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007359

• Reference epigenome ADMSC01 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007360

• Reference epigenome ADMSC01 input ChIP-Seq data generated from KEP study

  ADMSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007361

• Reference epigenome ADMSC02 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007362

• Reference epigenome ADMSC02 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007363

• Reference epigenome ADMSC02 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007364

• Reference epigenome ADMSC02 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007365

• Reference epigenome ADMSC02 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007366

• Reference epigenome ADMSC02 input ChIP-Seq data generated from KEP study

  ADMSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007367

• Reference epigenome ADMSC03 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007368

• Reference epigenome ADMSC03 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007369

• Reference epigenome ADMSC03 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007370

• Reference epigenome ADMSC03 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007371

• Reference epigenome ADMSC03 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007372

• Reference epigenome ADMSC03 input ChIP-Seq data generated from KEP study

  ADMSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007373

• Reference epigenome ADMSC05 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC05 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007374

• Reference epigenome ADMSC05 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007376

• Reference epigenome ADMSC05 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007377

• Reference epigenome ADMSC05 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007378

• Reference epigenome ADMSC05 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007379

• Reference epigenome ADMSC05 input ChIP-Seq data generated from KEP study

  ADMSC05 input ChIP-Seq paired end data

  Dataset EGAD00001007380

• 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

• Colorectal cancer organoids expressing BRAF (fusion) genes

  Fusion genes may be oncogenic drivers and potential targets for personalized therapies in a variety of cancer types. The BRAF gene is frequently involved in oncogenic fusions, with fusion frequencies of 0.2-3% throughout different cancers. BRAF can be fused to a wide variety of genes, however, BRAF fusions rarely occur in the exact same gene configuration, making assessment of the fusion relevance and decision for drug treatment challenging. Here, we devised a colorectal cancer organoid-based platform to functionally characterize diverse BRAF fusions, containing various partner genes (AGAP3, DLG1 and TRIM24). We compared these BRAF fusions with respect to cellular behaviour, downstream signaling activation and response to targeted therapies. We found that 5’ partner choice of BRAF affects cellular localization and intracellular signaling capacities of the fusion genes. The DLG1-BRAF fusion gene showed distinct localization to the plasma membrane and exhibited increased levels of MAPK pathway activation under unperturbed conditions. Furthermore, the different BRAF fusions showed varying sensitivities to the targeted inhibition of ERK and BRAF, with the DLG1-BRAF fusion being the most sensitive. Additionally, RNA-sequencing identified distinct subsets of genes affected by the DLG1-BRAF fusion gene. Importantly, all fusion genes conveyed resistance to the clinically relevant EGFR/HER2/HER4-inhibitor afatinib, suggesting that BRAF fusions should be screened alongside other MAPK pathway alterations to identify mCRC patients amenable to cetuximab treatment. In summary, we developed a platform to efficiently assess molecular and cellular effects of fusion genes, and revealed that differential drug responses and distinct gene expression profiles can be induced by different BRAF fusions .

  Study EGAS00001003558

• Transcriptomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

  Background; Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.MethodsMultiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping were performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).ResultsOverall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high ITH and were classified discordantly. Proximal tumors showed ITH related to differences in the microenvironment, while ITH within distal tumors were cancer-cell related. This subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05).ConclusionOur transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

  Study EGAS00001004668

• Biallelic tumor suppressor loss and DNA repair defects in de novo small cell prostate cancer

  Small cell prostatic carcinoma (SCPC) is an aggressive pathology that is managed similar to small cell lung cancer. SCPC can evolve from prostatic adenocarcinoma in response to androgen deprivation therapy but in rare cases is present at initial cancer diagnosis. The molecular etiology of de novo SCPC is incompletely understood due to the scarcity of tumor tissue and the brief life expectancy of patients. Through a retrospective search of our regional oncology pharmacy database, we identified 18 patients diagnosed with de novo SCPC between 2004 and 2017. Ten patients had pure SCPC pathology and the remainder had some admixed adenocarcinoma foci, but all were treated with first line platinum-based chemotherapy. Median overall survival was 28 months. We performed targeted DNA sequencing, whole exome sequencing and mRNA profiling on formalin-fixed paraffin embedded archival tumor tissue. We observed frequent biallelic deletion and/or mutation of tumor suppressors TP53, RB1 and PTEN, similar to treatment related SCPC. Indeed, at the RNA level pure de novo SCPC closely resembled treatment-related SCPC. However, five patients had biallelic loss of DNA repair genes including BRCA1, BRCA2, ATM and MSH2/6: potentially underlying the high genomic instability of this rare disease variant. Two patients with pure de novo SCPC harbored ETS gene rearrangements to androgen-driven promoters, consistent with evolution of de novo SCPC from an androgen-driven ancestor. Overall, our results reveal a highly aggressive molecular landscape underlies this unusual pathologic variant, and propose opportunities for targeted therapy strategies in a disease with few treatment options.

  Study EGAS00001003007

• Identification of driver oncogenes in scirrhous-type gastric cancer cell lines

  Scirrhous type gastric cancer (SGC) is one of the most intractable cancer subtypes in human, and its therapeutic targets have been rarely identified to date. Exploration of somatic mutations in the SGC genome with the next generation sequencers has been hampered by low tumor contents by the markedly increased fibrous tissues. Thus, SGC cell lines may be useful resources to search for novel oncogenes in. Here we have conducted whole exome sequencing and RNA sequencing on two SGC cell lines, OCUM-8 and OCUM-9. Interestingly, most of the mutations thus identified have not been reported. In OCUM-8 cells, a novel CD44-IGF1R fusion gene is discovered, the protein product of which ligates the amino-terminus of CD44 to the transmembrane and the tyrosine-kinase domains of IGF1R. Further, both of CD44 and IGF1R are markedly amplified in the OCUM-8 genome and abundantly expressed. CD44-IGF1R has a transforming ability, and the suppression of its kinase activity leads to rapid cell death of OCUM-8. On the other hand, OCUM-9 seems to possess multiple oncogenic events in its genome. The MET gene is highly amplified, and the treatment of OCUM-9 with crizotinib, a MET inhibitor, results in cell death. Further, a novel BORCS5-ETV6 fusion gene is identified in the OCUM-9 genome. BORCS5-ETV6 possesses oncogenic activity, and suppression of its message partially inhibits cell growth. Prevalence of these novel fusion genes among SGC awaits further investigation, but we validate the significance of cell lines as appropriate reagents for detailed genomic analyses of SGC.

  Study JGAS000179

• 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

• GWAS data on human pancreatic islets from 191 donors - Lund

  Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

  Study EGAS00001004044

• Human Pancreatic Islet RNAseq - Lund

  Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

  Study EGAS00001004042

• Phenotyping data on human pancreatic islets from 191 donors - Lund

  Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

  Study EGAS00001004056

• ICR_RNASeq_pHGG

  RNA sequencing of paediatric high grade gliomas and diffuse intrinsic pontine gliomas. RNA was sequenced from fresh frozen surgical material or from primary cells cultured under stem cell conditions. RNA was subjected to Illumina whole transcriptome paired end sequencing. Data is provided as paired-end FASTQ files

  Dataset EGAD00001004116

• RNA sequencing data pre-treatment and post-treatment for NABUCCO cohort 1

  NABUCCO cohort 1 sequencing data. The dataset includes RNA sequencing pre-treatment on tumor samples (n=18) and RNA sequencing post-treatment on bladder tumor samples (n=18).

  Dataset EGAD00001006855

• 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

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• Reference epigenome IPS02_N_NPC_mRNA-Seq data generated from KEP study

  A IPS02_N_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003489

• Reference epigenome IPS03_N_ENeuron_mRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003490

• Reference epigenome IPS05_X_NPC_mRNA-Seq data generated from KEP study

  A IPS05_X_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003492

• Reference epigenome IPS06_X_ENeuron_mRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003493

• Reference epigenome IPS02_N_NPC_smRNA-Seq data generated from KEP study

  A IPS02_N_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003502

• Reference epigenome IPS03_N_ENeuron_smRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003503

• Reference epigenome IPS05_X_NPC_smRNA-Seq data generated from KEP study

  A IPS05_X_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003505

• Reference epigenome IPS06_X_ENeuron_smRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003506

• Reference epigenome KNIH002 mRNA-seq data generated from KEP study

  A KNIH002 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002168

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• Reference epigenome KNIH004 mRNA-seq data generated from KEP study

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

• Reference epigenome KNIH001 mRNA-seq data generated from KEP study

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome KNIH006 mRNA-seq data generated from KEP study

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• Reference epigenome KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH006 miRNA-seq data generated from KEP study

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• 4C-Seq datasets of EndoC-bh1

  2 Circular chromosome conformation capture (4C-Seq) datasets of the human beta cell line EndoC-bh1.

  Dataset EGAD00001005210

• Reference epigenome Islet-derived-MSC01 mRNA-Seq data generated from KEP study

  Islet-derived-MSC01 mRNA-Seq paired end data

  Dataset EGAD00001007160

• Reference epigenome Islet-derived-MSC02 mRNA-Seq data generated from KEP study

  Islet-derived-MSC02 mRNA-Seq paired end data

  Dataset EGAD00001007161

• Reference epigenome Islet-derived-MSC03 mRNA-Seq data generated from KEP study

  Islet-derived-MSC03 mRNA-Seq paired end data

  Dataset EGAD00001007162

• Reference epigenome Islet-derived-MSC04 mRNA-Seq data generated from KEP study

  Islet-derived-MSC04 mRNA-Seq paired end data

  Dataset EGAD00001007163

• Reference epigenome Islet-derived-iPSC01 mRNA-Seq data generated from KEP study

  Islet-derived-iPSC01 mRNA-Seq paired end data

  Dataset EGAD00001007164