15059 results for "cancer studies using rna-seq"

in 27.06 milliseconds.

• Transcriptomic Profile of Whole Blood Cells from Elderly Subjects fed Probiotic Bacteria Lactobacillus rhamnosus GG ATCC 53103 (LGG) in a Phase I Open Label Study

  Open label study to evaluate the safety of Lactobacillus rhamnosus GG ATCC 53103 (LGG) in elderly subjects. Fifteen healthy elderly volunteers, ages 65-80 were enrolled in a study in which they received LGG capsules containing 1 x 1010 CFU, twice daily for 28 days and were followed through day 56. The study subjects completed a daily diary, a telephone call on study days 3, 7 and 14 and study visits at the Massachusetts General Hospital Clinical Research Center at baseline, day 28 and day 56. During each visit, the subject diary, interim history, potential adverse effects and concomitant medications were reviewed and vital signs and a physical examination were performed. Routine blood tests were obtained to monitor for safety during visits and nasopharyngeal and stool samples were collected for microbiome analysis. Volunteers interested in participating in the effect of LGG probiotic on the Human whole Blood Transcriptome substudy also had blood drawn for DNA and RNA extraction, after signing the substudy consent form. The main objective of the study was to assess the safety and tolerability of 2 x 1010 CFU LGG administered orally to elderly subjects for 28 days. Secondary objectives were to evaluate the richness and microbial diversity in nasopharyngeal and stool specimens using pyrosequencing, and to compare cytokine production in response to bacterial stimulation by following the kinetics of mRNA expression of pro and anti-inflammatory genes and different signaling pathways, in relation to changes in stool Bifidobacterium and Lactobacillus spp. The study was reviewed and approved by the Partners Human Research Committee (IRB # 2010P001695) and was registered at ClinicalTrials.gov (NCT01274598). The main results regarding the clinical signs and safety of intervention is published in PLoS One. 2014 Dec 1;9(12):e113456. doi: 10.1371/journal.pone.0113456. eCollection 2014. PMID: 25438151. RNA sequencing for transcriptome analysis was only done in 11 of the 15 subjects as RNA integrity and quantity had to be optimal for the three times where blood was collected in order for a subject to be included in the study. The information regarding the microbiome data associated with this study is presented in a separate paper, which can be found at dbGaP study accession, phs000896, and PMID: 25873374.

  Study phs000928

• A Phase I/II Trial of T Cell Receptor Gene Therapy Targeting HPV-16 E7 for HPV-Associated Cancers

  This is a phase I clinical trial testing the safety and efficacy of gene-engineered T cells expressing a T-cell receptor targeting an HLA-A*02:01-restricted epitope of HPV-16 E7 (E7 TCR T cells) in patients with metastatic HPV-16+ epithelial cancers. A total of 12 patients were treated. The maximum tested dose was tolerated. Side effects were most consistent with the expected toxicities of the lymphocyte-depleting conditioning regimen and high-dose aldesleukin. Six of the 12 patients demonstrated objective clinical responses. Translational studies of the infused T cell characteristics did not correlate strongly with clinical response. Genomic defects in the targeted peptide-HLA complex did demonstrate definitive mechanisms of resistance in some patients.

  Study phs002286

• Exome Sequencing of Schizophrenia Cases and Controls in the South African Xhosa Population

  The goal of this international collaborative project is to identify genes responsible for Schizophrenia in the Xhosa population of South Africa. The vast majority of the genetic basis for Schizophrenia has yet to be explained. We hypothesize that genes and pathways important to Schizophrenia will harbor different, severe disease-causing mutations in different affected individuals. Given the genetic diversity of African populations, we expect to find genes for Schizophrenia that have not yet emerged from studies of other populations. This project will also foster the development of gene discovery research for neuropsychiatric disorders in Africa. Our approach will identify genes important for the disorder in populations worldwide. These genes will stimulate future efforts to develop more effective treatment and prevention strategies.

  Study phs000959

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): COVID-19 Experience Study (C19EX) Survey

  This was conducted virtually through the Achievement studies platform during the current COVID-19 pandemic. Participants were asked to complete a survey every day to capture information about whether they had experienced symptoms corresponding to respiratory, ILI, and COVID-19 infections within the previous 24 hour period, their experiences with the healthcare system, treatments, diagnostic testing, and their behaviors and potential risk factors. Participants were also asked to share their wearable activity tracker data. The purpose of this study was to better understand the geographic spread of diseases, impacts on daily life, and behavioral and physiological functioning in relation to recent self-reported respiratory disease, influenza-like-illness (ILI), and coronavirus disease (COVID-19) events.DOI: https://rapids.ll.mit.edu/10.57895/6m5z-je42

  Study phs002537

• Autism Genome Project (AGP) Consortium - Whole Genome Association Study of over 1,500 Parent-Offspring Trios - Stage I and II

  Autism spectrum disorders (ASDs) are highly heritable (~90%), yet the underlying genetic determinants are largely unknown. To understand the genetic and phenotypic heterogeneity in ASDs, we analyzed 2,611 strictly defined ASD families with over 1,000,000 single nucleotide polymorphisms (SNPs), and applied multiple analytical strategies to examine these families for SNPs and Copy Number Variation (CNVs) affecting risk for ASDs. Secondary analyses examined associations in more homogenous subgroups. Furthermore, the use of large control datasets permitted contrasting case and control samples and addressed the potential increased burden of rare CNVs in ASD. Our data have allowed us to discern key features of the ASD genomic architecture, find new susceptibility loci, and chart a course for future studies in ASDs.

  Study phs000267

• Establishment and genomic analysis of mixed phenotypic acute leukemia cell lines

  Among acute leukemias, mixed phenotype acute leukemias (MAPL; mixed phenotype acute leukemiae), which have both acute myeloid and acute lymphocytic leukemia traits, are generally considered resistant to chemotherapy, especially those with t(9;22) MPAL has a poorer prognosis than other forms of MPAL and is a challenge to overcome. MPAL accounts for less than 1% of all acute leukemias, and the rarity of this disease makes it difficult to conduct clinical studies to develop treatments for the disease. Therefore, we planned to establish cell lines based on acute leukemia cells (JMPAL-1) obtained from MPAL patients, and to analyze information such as gene mutations related to disease development based on these cell lines for therapeutic purposes.

  Study JGAS000721

• C-MACH reduced-representation bisulfite sequencing (RRBS)

  Umbilical cord blood (CB) has been commonly used as an epigenetic surrogate of fetuses. Here, we propose umbilical cord tissue (UC) as an alternative surrogate for epigenetic studies on fetuses. To explore the feasibility to examine UC epigenome by deep sequencing, we determined CpG methylation profiles of human postnatal UC by reduced representation bisulfite sequencing. We compared DNA methylomes between UC and CB. To investigate the effect of folate during pregnancy on fetal DNA methylation, we compared fetal UC DNA methylation between low and high levels of maternal plasma folate. Our study suggests that UC is useful as an alternative surrogate for studying environmental effects on DNA methylation in human fetuses, compensating CB by providing additional information about epigenetic regulation of genes.

  Study JGAS000171

• Integrated genomic analysis identifies recurrent mutations and evolution patterns driving the initiation and progression of follicular lymphoma.

  follicular lymphoma (FL), the most common indolent non-Hodgkin’s lymphoma, remains incurable. A well-recognized complication of FL is its transformation to a diffuse large B-cell lymphoma (DLBCL)-morphology in a subset of patients, for which the clinical outcomes are poor. Recent genetic profiling and single case studies of donor-derived FL following stem cell transplantation had alluded to the putative existence of a ‘long lived’ tumor-initiating progenitor cell compartment from which successive disease events occurred. To chronicle the genetic changes associated with progression to transformation, we conducted whole-genome sequencing (WGS) of FL-tFL pairs and matched germline (GL) samples from six patients, a total of 20 genomes.

  Study EGAS00001000399

• Low_coverage_whole_genome_sequencing_of_samples_from_the__Cretan_Greek_isolate_collection_HELIC_MANOLIS

  Whole-genome sequencing at 1x of samples from the Cretan Greek isolate collection HELIC-MANOLIS. Genome-wide association studies of complex traits have been successful in identifying common variant associations, but a substantial heritability gap remains. The field of complex trait genetics is shifting towards the study of low frequency and rare variants, which are hypothesised to have larger effects. The study of these variants can be empowered by focusing on isolated populations, in which rare variants may have increased in frequency and linkage disequilibrium tends to be extended. This work focuses on an isolated population from Crete, Greece. Sequencing is very efficient in isolated populations, because variants found in a few samples will be shared by others in extended haplotype contexts, supporting accurate imputation.

  Study EGAS00001000392

• Exome sequencing of UK Birth Cohorts - Avon Longitudinal Study of Parents and Children

  Birth cohort studies involve repeated surveys of large numbers of individuals from birth and throughout their lives. They collect information useful for a wide range of life course research domains, and biological samples which can be used to derive data from an increasing collection of omic technologies. This rich source of longitudinal data, when combined with genomic data, offers the scientific community valuable insights from population genetics to rare disease associations. Avon Longitudinal Study of Parents and Children (ALSPAC)recruited 14,775 babies of predominantly White ethnicity in the Avon county of south-west England between 1991 and 1992. Born in Bradford (BiB) is similarly focused on a particular local area, the city of Bradford in the north of England, and recruited 13,858 babies between 2007 and 2011, of whom ~41% self-report as white British and ~59% as other ethnicities, predominantly Pakistani. Millennium Cohort Study (MCS) is a national cohort that recruited 18,827 children born between 2000 and 2002, intentionally over-sampling areas with high child poverty, large ethnic minority populations, and smaller UK nations (Wales, Scotland and Northern Ireland) Available here is a subset of exome-sequenced parents and children from these studies (CRAMS and post-QC VCFs) as detailed in https://doi.org/10.12688/wellcomeopenres.22697 [doi.org]. Phenotypic data is also available by submitting an application to the corresponding cohort: https://www.bristol.ac.uk/alspac/researchers/our-data/[bristol.ac.uk]

  Dataset EGAD00001015371

• Whole Genome Profiling to Detect Schizophrenia Methylation Markers

  Schizophrenia (SCZ) is a devastating psychiatric disorder. The aim of this study was to detect DNA methylation (5meC) profiles in whole blood associated with SCZ. The sample consisted of schizophrenia cases and controls selected from national population registers in Sweden. We employed MBD protein-based enrichment of the methylated DNA fraction, followed by single end sequencing (50 bp reads) on the SOLiD platform. To improve methylome-wide coverage we used an existing protocol variant, which increases the relative number of fragments from CpG poor regions. To analyze the data, we used an analysis pipeline described elsewhere7, that was designed specifically for MBD-seq and included alignment, quality control (QC) of reads and samples, coverage calculations for the 26,752,702 autosomal CpGs in the reference genome using a non-parametric estimate of the fragment size distribution, an in silico experiment to identify CpGs in loci with alignment problems, a 2-stage adaptive algorithm to combine data from correlated adjacent CpG sites in "blocks", principal component analysis (PCA), association testing, bioinformatics annotation and network analysis. After QC, 1459 subjects with on average 32.4 million high quality reads remained. Association testing was performed on 4.3 million high quality blocks after regressing out possible assay related technical artifacts, sex/age, and unmeasured confounders through the PCA components.

  Study phs000608

• Altered oligodendrocyte heterogeneity in Multiple sclerosis revealed by single nuclei RNA sequencing

  This study is currently hosted by the European Nucleotide Archive. To access the data contained within the Study please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Oligodendrocyte (OL) pathology is increasingly implicated in neurodegenerative diseases, as they are involved in metabolic support of axons and functional cross-talk with other brain cells. Rodent OLs are heterogeneous, with developmental and biological differences, but the extent of heterogeneity in the normal human brain and its contribution to any changes to disease remains unknown. Here we performed single nuclei RNA-sequencing (snRNA-seq) from white matter (WM) areas of post mortem human brain both in control (Ctr) and multiple sclerosis (MS) patients. We identified several sub-clusters of oligodendroglia in the Ctr human WM, some similar to those in mouse, and defined new markers for these cell states. Strikingly, some of these sub-clusters were under-represented in MS tissue, while others were more prevalent than in controls. We found a lack of OL precursor cells (OPCs) and an OL subcluster in an intermediate stage of differentiation in MS lesions and in normal appearing white matter (NAWM), suggesting either depletion by the disease or by a regenerative response. The differences in mature OL sub-clusters indicate different functional states of OLs in MS tissue and, as this is similar in NAWM to lesions, that MS is a more diffuse brain disease than the focal demyelinating lesions suggest. We were also able to identify new putative markers of different MS lesion subtypes. Our findings of an altered heterogeneity of oligodendroglia in MS may have an important contribution to our understanding of disease progression and may alter therapeutic approaches to MS.

  Study EGAS00001003412

• RNA-sequencing

  RNA-sequencing for a cohort of B-ALL samples : 5 Down Syndrome (DS), 16 Hyperdiploid (HeH), 6 iAMP21, 9 other. RNA-sequencing for B-cell progenitors from 3 healthy. It also contains RNA-sequencing datasets of Patient-Derived Xenografts (X) developed from the B-ALL samples : 4 Down Syndrome (DS), 4 Hyperdiploid (HeH), 1 iAMP21, 3 other.

  Dataset EGAD00001005426

• Implementation, Adoption, and Utility of Family History in Diverse Care Settings

  The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

  Study phs001641

• Quantitative analysis of a novel DNA hypermethylation panel for lung cancer diagnosis using bronchial specimens

  Non-diagnostic findings in Transbronchial Lung Biopsy (TBLB) and Endobronchial Ultrasound-guided Transbronchial Lung Biopsy (EBUS-TBLB) are not uncommon. A challenge is to improve lung cancer detection by TBLB/EBUS-TBLB. In order to improve the diagnostic yield of bronchoscopy, we used 850K methylation chip to identify methylation sites that distinguish malignant from benign lung nodules.We found that the combination of HOXA7, SHOX2,and SCT methylation analysis has the best diagnostic yield in 123 bronchial washing (sensitivity: 74·1%; AUC: 0·851) and 172 brushing samples (sensitivity: 86·1%; AUC: 0·915). We developed a kit comprising these three genes and validated the kit in 329 unique bronchial washing samples, 397 unique brushing samples, and 179 unique patients with both washing and brushing samples. The diagnostic accuracy of the panel alone in lung cancer diagnosis was 86·9%, 91·2% and 95% in bronchial washing, brushing, and washing+brushing samples, respectively. The panel's sensitivity in combination with cytology, rapid on-site evaluation (ROSE), and histology in lung cancer diagnosis was 90·8% and 95.8% in bronchial washing and brushing samples, respectively, and 100% in washing+brushing samples. Quantitative analysis of the three-gene panel improves lung cancer diagnosis by bronchoscopy.

  Study EGAS00001007089

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• Genetic Mechanisms of Disease Lab DAC

  Data access for raw and processed H3K27ac ChIP-seq and ATAC-seq data from "Effect of ETS2 modulation on chromatin accessibility and enhancer activity in human macrophages".

  Dac EGAC50000000087

• Reference epigenome SMC01 h3k9me3 ChIP-Seq data generated from KEP study

  SMC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007312

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

  SMC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007344

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

  ADMSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007386

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

  SMC09 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007354

• Reference epigenome SMC02 h3k36me3 ChIP-Seq data generated from KEP study

  SMC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007315

• Reference epigenome SMC04 h3k9me3 ChIP-Seq data generated from KEP study

  SMC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007330

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

  ADMSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007387

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

  ADMSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007356

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

  ADMSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007361

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

  ADMSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007363

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

  ADMSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007385

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

  ADMSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007398

• Reference epigenome SMC04 h3k4me1 ChIP-Seq data generated from KEP study

  SMC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007328

• Reference epigenome SMC04 h3k36me3 ChIP-Seq data generated from KEP study

  SMC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007327

• Reference epigenome SMC03 h3k9me3 ChIP-Seq data generated from KEP study

  SMC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007324

• Reference epigenome SMC04 h3k4me3 ChIP-Seq data generated from KEP study

  SMC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007329

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

  ADMSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007364

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

  ADMSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007382

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

  ADMSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007365

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

  ADMSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007362

• 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 SMC02 h3k4me1 ChIP-Seq data generated from KEP study

  SMC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007316

• Reference epigenome SMC05 h3k4me1 ChIP-Seq data generated from KEP study

  SMC05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007334

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

  Islet-derived-MSC03 miRNA-Seq single end data

  Dataset EGAD00001007183

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

  Islet-derived-iPSC01 miRNA-Seq single end data

  Dataset EGAD00001007185

• Reference epigenome Islet-derived-iPSC02 miRNA-Seq data generated from KEP study

  Islet-derived-iPSC02 miRNA-Seq single end data

  Dataset EGAD00001007186

• Reference epigenome SMC03 h3k27ac ChIP-Seq data generated from KEP study

  SMC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007320

• Reference epigenome SMC03 h3k36me3 ChIP-Seq data generated from KEP study

  SMC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007321

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

  Islet-derived-MSC02 miRNA-Seq single end data

  Dataset EGAD00001007182

• Reference epigenome SMC03 h3k4me3 ChIP-Seq data generated from KEP study

  SMC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007323

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

  SMC09 input ChIP-Seq paired end data

  Dataset EGAD00001007355

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

  SMC08 input ChIP-Seq paired end data

  Dataset EGAD00001007349

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

  Islet-derived-MSC01 miRNA-Seq single end data

  Dataset EGAD00001007181

• Reference epigenome SMC01 h3k4me1 ChIP-Seq data generated from KEP study

  SMC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007310

• Reference epigenome SMC02 h3k9me3 ChIP-Seq data generated from KEP study

  SMC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007318

• Reference epigenome SMC01 h3k27ac ChIP-Seq data generated from KEP study

  SMC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007308

• 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 SMC01 input ChIP-Seq data generated from KEP study

  SMC01 input ChIP-Seq paired end data

  Dataset EGAD00001007313

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

  ADMSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007397

• Reference epigenome SMC02 h3k4me3 ChIP-Seq data generated from KEP study

  SMC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007317

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

  ADMSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007396

• 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 SMC03 h3k4me1 ChIP-Seq data generated from KEP study

  SMC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007322

• Reference epigenome SMC04 h3k27ac ChIP-Seq data generated from KEP study

  SMC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007326

• Reference epigenome SMC05 h3k27ac ChIP-Seq data generated from KEP study

  SMC05 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007332

• Reference epigenome SMC05 h3k36me3 ChIP-Seq data generated from KEP study

  SMC05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007333

• Reference epigenome SMC05 input ChIP-Seq data generated from KEP study

  SMC05 input ChIP-Seq paired end data

  Dataset EGAD00001007337

• 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 h3k27ac ChIP-Seq data generated from KEP study

  SMC07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007338

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

  SMC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007345

• 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 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 KNIH009 miRNA-seq data generated from KEP study

  A KNIH009 miRNA-seq single end data for preadipocytes

  Dataset EGAD00001002768

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

  SMC09 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007350

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

  SMC09 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007352

• 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 ADMSC05 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007376

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

  ADMSC05 input ChIP-Seq paired end data

  Dataset EGAD00001007380

• 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 ADMSC08 input ChIP-Seq data generated from KEP study

  ADMSC08 input ChIP-Seq paired end data

  Dataset EGAD00001007401

• Reference epigenome SMC04 input ChIP-Seq data generated from KEP study

  SMC04 input ChIP-Seq paired end data

  Dataset EGAD00001007331

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

  SMC09 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007353

• Reference epigenome SMC01 h3k36me3 ChIP-Seq data generated from KEP study

  SMC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007309

• Reference epigenome SMC02 h3k27ac ChIP-Seq data generated from KEP study

  SMC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007314

• Reference epigenome SMC02 input ChIP-Seq data generated from KEP study

  SMC02 input ChIP-Seq paired end data

  Dataset EGAD00001007319

• Reference epigenome SMC05 h3k4me3 ChIP-Seq data generated from KEP study

  SMC05 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007335

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

  SMC09 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007351

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

  ADMSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007366

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

  ADMSC05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007377

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

  ADMSC06 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007381

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

  ADMSC06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007384