14982 results for "cancer studies using rna-seq"

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

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• 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 KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

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

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

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

  Islet-derived-iPSC01 mRNA-Seq paired end data

  Dataset EGAD00001007164

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

  Islet-derived-MSC01 mRNA-Seq paired end data

  Dataset EGAD00001007160

• 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-MSC02 mRNA-Seq data generated from KEP study

  Islet-derived-MSC02 mRNA-Seq paired end data

  Dataset EGAD00001007161

• McGill EMC Release 4 for assay "mRNA-seq"

  McGill EMC Release 4 for assay "mRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001291

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

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

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

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

• 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 KNIH006 miRNA-seq data generated from KEP study

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• 4C-seq data of a primary AML with t(3;8)

  The viewpoints used in the 4C-seq data were either the EVI1 promoter or the MYC super-enhancer

  Dataset EGAD00001006818

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

  Islet-derived-MSC04 mRNA-Seq paired end data

  Dataset EGAD00001007163

• Reference epigenome IPS-Fibroblast02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007270

• Reference epigenome IPS-ENeuron02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007298

• BLUEPRINT ATAC-seq data for cells in the haematopoietic lineages, from adult and cord blood samples

  BLLUEPRINT ATAC-seq data for cells in the haematopoietic lineages, from adult and cord blood samples.

  Study EGAS00001001596

• ChIP-seq data of Hodgkin lymphoma cell line L-428

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37.

  Study EGAS00001003033

• H3K27ac ChIP-seq of human acute leukemias and healthy donors

  H3K27ac ChIP-seq of 79 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, 4 samples derived from CD34+ cord blood cells of healthy donors were included.

  Dataset EGAD00001011060

• Chromatin accessibility (ATAC-seq) of human acute leukemias and healthy donors

  ATAC-seq of 79 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). ATAC-seq of CD34+ HSPCs from 3 healthy donors is also included.

  Dataset EGAD00001011050

• Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer

  Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitors. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. Here we evaluate bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples. In CheckMate 026, TMB was retrospectively assessed in 312 patients with non-small cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne CDx™ assay. TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. Parameter variation can therefore impact TMB calculations, highlighting the need for standardization. Encouragingly, WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90) and differences could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms and centers is achievable.

  Study EGAS00001003661

• 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

• Bulk RNA sequencing data in "Three-dimensional human alveolar stem cell culture models reveal infection response to SARS-CoV-2"

  This dataset includes 14 bulk RNA sequencing data (28 fastq files) in the study entitled "Three-dimensional human alveolar stem cell culture models reveal infection response to SARS-CoV-2". RNA sequencing library was generated with Truseq stranded total RNA Gold kit.

  Dataset EGAD00001006241

• Circulating RNA profiles of healthy and preeclamptic pregnancies

  This prospective study investigated whether circulating RNA (C-RNA; cell free RNA present in the bloodstream) measurements could non-invasively monitor pregnancy status, with emphasis on detecting molecular markers associated with preterm preeclampsia. Whole blood samples were collected from pregnant women at the time of diagnosis with early-onset preeclampsia with severe features. Blood samples were also obtained normotypical control pregnancies and from patients diagnosed with late-onset preeclampsia for comparison. For all samples, C-RNA was extracted from 4 mL blood and whole transcriptome enrichment performed on cDNA libraries prior to sequencing to a target depth of 50M reads on an Illumina HiSeq 2000 platform. Throughout the course of this work, whole transcriptome RNA sequencing data was obtained from 289 C-RNA samples collected from 178 subjects. The complete dataset consists of samples from the following cohorts: Illumina Preeclampsia Cohort (iPEC): 40 pregnancies at the time of early-onset preeclampsia diagnosis and 73 gestational age-matched controls PEARL preeclampsia cohort: 12 early-onset and 12 late-onset preeclampsia patients PEARL healthy control cohort: 152 longitundinal samples collected from 41 healthy pregnancies

  Study phs002017

• 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

• MTLE small RNA-sequencing and RNA-sequencing Data Access Committee

  Dac EGAC00001001427

• SEGA RNA-sequencing and small RNA-sequencing Data Access Commitee

  Dac EGAC00001001467

• NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish

  The Amish Complex Disease Research Program includes a set of large community-based studies focused largely on cardiometabolic health carried out in the Old Order Amish (OOA) community of Lancaster, Pennsylvania (http://medschool.umaryland.edu/endocrinology/amish/research-program.asp). The OOA population of Lancaster County, PA immigrated to the Colonies from Western Europe in the early 1700's. There are now over 30,000 OOA individuals in the Lancaster area, nearly all of whom can trace their ancestry back 12-14 generations to approximately 700 founders. Investigators at the University of Maryland School of Medicine have been studying the genetic determinants of cardiometabolic health in this population since 1993. To date, over 7,000 Amish adults have participated in one or more of our studies. Due to their ancestral history, the OOA may be enriched for rare variants that arose in the population from a single founder (or small number of founders) and propagated through genetic drift. Many of these variants have large effect sizes and identifying them can lead to new biological insights about health and disease. The parent study for this WGS project provides one (of multiple) examples. In our parent study, we identified through a genome-wide association analysis a haplotype that was highly enriched in the OOA that is associated with very high LDL-cholesterol levels. At the present time, the identity of the causative SNP - and even the implicated gene - is not known because the associated haplotype contains numerous genes, none of which are obvious lipid candidate genes. A major goal of the WGS that will be obtained through the NHLBI TOPMed Consortium will be to identify functional variants that underlie some of the large effect associations observed in this unique population.

  Study phs000956

• Exome Sequencing to Identify Medically Relevant Associations in Finnish Sub-Isolate Samples from the FINRISK Cohort

  We propose to undertake, at the McDonnell Genome Institute of Washington University, a collaborative exome sequencing project of ~10,000 samples from a large Finnish population cohort, FINRISK, a participating component of the Sequencing Initiative Suomi (SISu) consortium. Together these sequenced samples will provide a unique resource for identification of infrequent or rare variants with a large impact on cardiovascular and metabolic disorders as well as on a wide range of heritable, disease-related quantitative phenotypes. Our collaborative group has already shown, in exome sequencing studies of smaller Finnish samples, that numerous loss of function variants are dramatically more frequent in Finland compared to other European populations, and that such variants demonstrate strong associations to several disease-related traits. Finland is the largest population isolate in Europe and the enrichment of these variants reflects its rapid growth from a severe population bottleneck about 100 generations ago. Northern and Eastern parts of Finland experienced additional, more recent bottlenecks, and therefore the enrichment of particular high-impact variants in these regions is even more extreme (Stoll et al., 2013, PMID: 23912948). We therefore propose to focus exome sequencing efforts on about 10,000 members of FINRISK who are specifically drawn from these sub-isolate regions. As a very wide range of phenotypes are available from this cohort (including cardiovascular and metabolic disease outcomes obtained from national registries) we hypothesize that the proposed exome sequencing will identify numerous new disease-related associations. Within the National Biobanks of Finland (www.nationalbiobanks.fi) we have DNA and tissue samples from over 200,000 individuals of whom 50,000 have GWAS data; these samples provide an exceptional resource for both imputation from the sequencing studies proposed here, as well as for replication of associations.

  Study phs000756

• Sequencing of paediatric High Grade Gliomas and DIPG

  Paediatric high grade glioma (pHGG) and diffuse intrinsic pontine glioma (DIPG) are a phenotypically diverse collection of tumours which molecular profiling studies have refined into age- and location-based subgroups driven by unique genetic and epigenetic alterations. Despite this, the rarity of these tumours means individual studies are underpowered to investigate subgroup-specific changes and to identify low frequency events recurrently targeting specific pathways. We have collated genomic data from 143 unpublished cases up to 30 years of age along with those from 18 previously published datasets in an integrated analysis comprising 1067 unique patients across all anatomical compartments of the CNS, with histone mutation status available for 893 cases (n=67 H3.3G34R/V, n=316 H3.3K27M, n=68 H3.1/H3.2K27M). Distinct clinicopathological and molecular subgroups are defined by these histone mutations, with several novel co-segregating mutations identified through these aggregated analyses, including loss of FBXW7 in H3.3G34R/V, TOP3A rearrangements in H3.3K27M, and BCOR mutations in H3.1K27M. Histone wild-type subgroups can be further refined by the presence of key oncogenic events (PDGFRA or EGFR amplification/mutations) or methylation profiles which molecularly more closely resemble pleomorphic xanthoastrocytoma or other low grade gliomas. The burden of genomic aberrations decreases with age, highlighting the infant population as biologically and clinically distinct. Across the whole cohort, we identify novel previously unrecognised pathway dysregulation in small subsets of tumours (e.g. splicing, WNT, immune response) which make up the umbrella classification of paediatric diffusely infiltrating gliomas. The integrated dataset helps to further define the molecular diversity of the disease, opening up novel avenues for biological study and providing a basis for functionally defined future treatment stratification.

  Study EGAS00001002314

• ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  Dataset EGAD00001000278

• ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  Dataset EGAD00001000279

• ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  Dataset EGAD00001000355

• ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  Dataset EGAD00001000648

• EM-seq converted WGS for CSF-derived cfDNA from pediatric brain tumor patients

  EM-seq converted WGS for CSF-derived cfDNA from pediatric brain tumor patients

  Dataset EGAD50000001975

• Reference epigenome Pancreas-Islet07 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007462

• Reference epigenome Pancreas-Islet08 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007466

• Reference epigenome Pancreas-Islet04 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007211

• Reference epigenome Pancreas-Islet07 input ChIP-Seq data generated from KEP study

  Pancreas-Islet07 input ChIP-Seq paired end data

  Dataset EGAD00001007463

• Reference epigenome Kidney-Podocyte01 h3k27me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007229

• Reference epigenome ADMSC02_smRNA-Seq data generated from KEP study

  A ADMSC02_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003868

• Reference epigenome Pancreas-Islet02 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007198

• Reference epigenome Pancreas-Islet02 input ChIP-Seq data generated from KEP study

  Pancreas-Islet02 input ChIP-Seq paired end data

  Dataset EGAD00001007199

• Reference epigenome IPS-NPC01 input ChIP-Seq data generated from KEP study

  IPS-NPC01 input ChIP-Seq paired end data

  Dataset EGAD00001007283

• Reference epigenome IPS-ENeuron02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007302