11768 results for "cancer rna-seq"

in 22.74 milliseconds.

• SE9581

  RNA-exome

  Dataset EGAD00001006837

• SE6136

  RNA-exome

  Dataset EGAD00001006833

• SE9037

  RNA-exome

  Dataset EGAD00001006832

• SF10064

  RNA exome

  Dataset EGAD00001006744

• SF4209

  RNA-exome

  Dataset EGAD00001006768

• SF10127

  RNA-exome

  Dataset EGAD00001006767

• SE8570

  RNA-exome

  Dataset EGAD00001006831

• SF11082

  RNA-exome

  Dataset EGAD00001006773

• SE4567

  RNA-exome

  Dataset EGAD00001006836

• SF11248

  RNA-exome

  Dataset EGAD00001006752

• SF6953

  RNA-exome

  Dataset EGAD00001006750

• SF9259

  RNA-exome

  Dataset EGAD00001006751

• Centre Leon Berard - DAC - seq data Neuroendocrine tumors - B Gibert

  Review committee for the neuroendocrine tumor genetic data set uploaded by Benjamin GIBERT (CRCL), Anthony FERRARI (CLB) and Thomas WALTER (HCL)

  Dac EGAC50000000218

• Single-cell G&T sequencing - Genomic data

  Single-cell G&T seq from an untreated PDX mouse with neuroblastoma (MYCN amplified).

  Dataset EGAD50000001513

• Chromatin accessibility in OCI-AML22 cells

  ATAC-seq performed in OCI-AML22 subpopulations sorted based on CD34 and CD38 status

  Dataset EGAD50000001631

• Longitudinal Studies of Patients with Familial Platelet Disorder with Associated Myeloid Malignancy (FPDMM)

  FPDMM (OMIM #601399) is a rare autosomal dominant disease characterized by defective megakaryocytic development, thrombocytopenia, platelet functional defects, and a life-long risk of developing hematological malignancies. FPDMM is caused by germline mutations in the RUNX1 gene, which encodes a master regulator of hematopoiesis. The overall lifetime risk of hematological malignancies in FPDMM patients is 35 - 45%. It is hypothesized that mutations in other genes cooperate with the germline RUNX1 mutations for leukemia development. However, it is not clear which genes need to be mutated for leukemia to develop and which biomarkers or clinical tests can be used to predict which patients will progress to leukemia. Likewise, there are no treatments and/or preventative measures that can reduce or stop disease progression except for stem cell transplantation, which is not available for all patients and may be associated with significant complications. To address these questions, we launched a longitudinal natural history study of patients associated with RUNX1 germline mutations at the NIH Clinical Center in early 2019 (Trial Registration ID: NCT03854318). The goals of this study are to: (1) perform comprehensive longitudinal phenotyping of FPDMM patients to document the full spectrum of both hematopoietic and non-hematopoietic manifestations of the disease; and (2) perform extensive genomic analyses of FPDMM patient samples to identify cooperating genomic variants, which may confer growth advantage that leads to clonal expansion of affected hematopoietic cells that may serve as the precursor to malignancies. The data available through dbGaP include exome sequencing and RNA-seq data from these patients. Diverse germline mutation types were identified in the RUNX1 gene, including splice site mutations and large deletions, in addition to nucleotide substitutions and small indels. Somatic mutations, especially in genes associated with clonal hematopoiesis of indeterminate potential (CHIP), were found in a substantial proportion of FPDMM patients who were leukemia-free. The findings suggest that the genomes in these patients are prone to somatic mutations and monitoring the dynamic changes of these somatic mutations prospectively may provide a way to predict progression to malignancies.

  Study phs003075

• Count Me In (CMI): The Angiosarcoma (ASC) Project (CMI-ASCproject)

  Count Me In - The Angiosarcoma Project: A Patient-Partnered Research Initiative to Accelerate Research in a Rare Sarcoma The Angiosarcoma Project (ASCproject) is a research study that directly engages angiosarcoma patients online and empowers them to accelerate cancer research by sharing medical information and clinical samples. Angiosarcoma is an exceedingly rare soft tissue sarcoma with an incidence of ~300 newly diagnosed cases per year in the United States. This low incidence has impeded large-scale research efforts in this disease that are strongly needed to catalyze research and improve clinical outcomes. The Angiosarcoma Project, launched in March 2017, has demonstrated the feasibility of directly engaging geographically dispersed patients to democratize research and establish a large patient cohort to characterize the genomic and clinical landscape of a rare disease. Angiosarcoma patients living in the US or Canada can access the study and consent online through an online portal (ASCproject.org). Enrolled patients are mailed saliva and blood draw kits, which can be used to extract germline DNA and cell-free DNA (cfDNA), respectively. The study team contacts participants' healthcare institutions to obtain medical records and a portion of archived tumor samples. All received tumor samples are subjected to centralized histological re-review by an expert pathologist to confirm a diagnosis of angiosarcoma. Whole exome sequencing is performed on tumor DNA, germline DNA, and cfDNA; transcriptome sequencing is performed on tumor RNA. De-identified data, including linked genomic, clinical, and patient-reported data, are shared via public databases on a recurring pre-publication basis. Participants are regularly provided updates on the study.

  Study phs001931

• Evaluation of EBUS-TBNA Aspirates from Advanced NSCLC for Comprehensive Sequencing Platforms Including Whole Genome Sequencing

  Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is often the only source of tumor tissue from patients with advanced, inoperable lung cancer. EBUS-TBNA aspirates are used for the diagnosis, staging, and genomic testing to inform therapy options. Here we extracted DNA and RNA from 220 EBUS-TBNA aspirates to evaluate their suitability for whole genome (WGS), whole exome (WES), and comprehensive panel sequencing. For a subset of 40 cases, the same nucleic acid extraction was sequenced using WGS, WES, and the TruSight Oncology 500 assay. Genomic features were compared between sequencing platforms and compared with those reported by clinical testing. A total of 204 aspirates (92.7%) had sufficient DNA (100 ng) for comprehensive panel sequencing, and 109 aspirates (49.5%) had sufficient material for WGS. Comprehensive sequencing platforms detected all seven clinically reported tier 1 actionable mutations, an additional three (7%) tier 1 mutations, six (15%) tier 2–3 mutations, and biomarkers of potential immunotherapy benefit (tumor mutation burden and microsatellite instability). As expected, WGS was more suited for the detection and discovery of emerging novel biomarkers of treatment response. WGS could be performed in half of all EBUS-TBNA aspirates, which points to the enormous potential of EBUS-TBNA as source material for large, well-curated discovery-based studies for novel and more effective predictors of treatment response. Comprehensive panel sequencing is possible in the vast majority of fresh EBUS-TBNA aspirates and enhances the detection of actionable mutations over current clinical testing.

  Study EGAS00001007708

• Methylation biomarker study of magnesium deficiency and colorectal cancer

  Five US studies using the Mg tolerance test, the "gold standard" test of Mg status, indicated that >50% participants had Mg deficiency. In our ongoing US trial, we have found a similar result. In growing recognition of the importance of Mg in human health, very recently, Mg was selected by the US Federal Dietary Reference Intake (DRI) Committee to update the DRI. In US and other populations at high risk of Mg deficiency, high Mg intake has been linked to reduced risk of colorectal neoplasia, insulin resistance, metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD). Conversely, in populations not at high risk of Mg deficiency, high Mg intake has been related to increased risk of total mortality (e.g. total cancer, colorectal cancer, and CVD), particularly when calcium intake is low. These observations suggest that the associations between high Mg intake and disease risks may completely differ by the underlying Mg status. Due to major limitations, the Mg tolerance test is not used in conventional clinical practice and rarely used in research. Instead, serum Mg is used for clinical diagnosis. However, serum Mg performs very poorly at identifying those with Mg deficiency. There is a great need to develop implementable, sensitive, and specific biomarkers which can be easily used for identifying people with Mg deficiency. Without such a marker, it is impossible to develop effective strategies that minimize adverse effects by targeting those who, as a result of Mg deficiency, are at risk of common diseases. It is known that DNA methylation changes are inducible by environmental exposures, including nutrients, and reversible when the exposure disappears. There are two major types of cytosine modifications in DNA, 5-hydroxymethylcytosine (5-hmC) and 5-methylcytosine (5-mC). 5-mC is often associated with suppressed gene expression. 5-hmC, generated by the oxidation of 5-mC, is specifically enriched in expressed genes and plays a critical role in activating and/or maintaining gene expression. However, current epigenomic profiling platforms cannot distinguish 5-hmC from 5-mC. Just recently, our collaborator, Dr. Chuan He (HHMI Investigator), established a novel and state of the art technique, TAB-Seq and TAB-Array protocol, which can separate 5-hmC from 5-mC in the genome. Our pilot studies indicate that Mg intake or treatment significantly affects methylation capacity, 5-mC and 5-hmC biomarkers. Our findings suggest blood leukocyte 5-hmC/5-mC biomarkers may serve as more sensitive biomarkers to identify Mg deficiency than serum Mg. Thus, a comprehensive epigenome-wide association study (EWAS) may help to identify the most sensitive 5-hmC/ 5- mC biomarkers for Mg deficiency. We propose to identify 5-hmC/5-mC biomarkers for Mg deficiency by a 4- phase EWAS study in the "Personalized Prevention of Colorectal Cancer Trial [PPCCT, R01CA149633; PI, Dai and Yu]" with a total of 240 participants. Mg tolerance test will be used as the gold standard. Finally, using newly identified biomarkers, we will evaluate if 12-week Mg treatment reduces TRPM7 expression, essential in Mg homeostasis and colorectal carcinogenesis, in rectal tissues only among those with Mg deficiency.

  Study phs002037

• Single-cell RNA sequencing of a PBMC collected from a male with 45,X/48,XYYY karyotype

  Dac EGAC00001002373

• Strand-specific RNA Sequencing of paired initial and recurrent gliomas

  Dataset EGAD00001001613

• Acute Myeloid Leukemia samples - peripheral blood

  ATAC-seq performed in peripheral blood samples of patients affected by Acute Myeloid Leukemia (AML)

  Dataset EGAD50000001642

• Mixing Sample Low Grade Glioma Gender Male, Age , 34,44

  Mixed Sample of scRNA-Seq primary low grade glioma. Genders: Male, Age: 34, 44.

  Dataset EGAD00001006018

• Paired-end Whole Exome-seq analysis of GBM, additional patient.

  Dataset contains one paired-end Whole Exome sequencing sample. One normal blood sample is also included.

  Dataset EGAD00001011989

• Pancreatic Cancer Sequencing Initiative OICR

  Pancreatic Cancer Sequencing Initiative for the International Cancer Genome Consortium at the Ontario Institute for Cancer Research.

  Study EGAS00001000395

• Lineage Plasticity and Immune Cell Heterogeneity Are Coordinately Dysregulated Through Changes in FOXA1 Expression in Bladder Cancers with Squamous Differentiation

  We performed whole exome sequencing (WES) of paired, macrodissected regions of urothelial carcinoma (UC), not otherwise specified (NOS-UC) and squamous differentiation (SqD) from 21 bladder tumors in which distinct and separable NOS-UC and SqD regions were present (total 42 exomes, in addition to matched normal exomes). Mean tumor mutational burden (TMB) was higher in the SqD versus the NOS-UC regions. Consistent with the higher TMB, the SqD regions also had a higher neoantigen burden than the patient-matched NOS-UC regions. The SqD regions also exhibited higher karyotypic complexity with a higher median ploidy relative to the paired NOS-UC regions. Phylogenetic analysis of the WES data confirmed that the morphologically distinct regions of all 21 tumors arose from a shared precursor with a median of 68 (range 16 – 852) non-synonymous mutations shared between the NOS-UC and SqD regions, and the overall data were indicative of early branched evolution. As whole exome sequencing did not identify a shared mutational alteration or genomic signature unique to the SqD or NOS-UC components, we therefore performed expression profiling of separable SqD and NOS-UC regions of 12 bladder tumors from the WES cohort with RNA quality sufficient for whole transcriptome RNA-sequencing (RNAseq, total of 24 regions). Based on centroid clustering analysis of expression data, all 12 regions of SqD were basal-squamous subtype based on the TCGA molecular classification schema. Notably, 8 of the NOS-UC regions were also basal-squamous subtype, with only four pairs exhibiting discordant transcriptional subtypes (three NOS-UC regions were luminal-infiltrated, one luminal-papillary). To better quantify differences in the transcriptional profiles of the paired NOS-UC and SqD regions, we used single sample gene set enrichment analysis (ssGSEA) to assess for enrichment of luminal or basal-squamous gene expression. ssGSEA analysis revealed that the SqD regions expressed higher levels of basal-squamous genes based on the BASE47 gene set and UPK/KRT gene sets; whereas the matched NOS-UC regions expressed higher levels of luminal genes. These differences in the levels of expression of basal and luminal genes were observed even in cases in which both the NOS-UC and SqD regions were basal-squamous subtype based the TCGA molecular classification schema. Exomes from eight patients in this study have previously been deposited in dbGaP: accession number phs001783 as part of the study "Exome Recapture and Sequencing of Prospectively Characterized Clinical Specimens From Cancer Patients".

  Study phs002357

• Pancreatic Cancer Sequencing Initiative

  Pancreatic Cancer Sequencing Initiative for the International Cancer Genome Consortium at the Ontario Institute for Cancer Research.

  Study EGAS00001000343

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• High-Risk Breast Cancer GWAS

  The GWAS includes High Risk Women from the following epidemiological studies of breast cancer, comprising a total of 3,719 cases and 3,642 controls (cases/controls: MEC, 0/200; ABCFR, 326/418; FCCC, 56/3; BCFR-UT, 66/32; CNIO-BC, 87/92; GESBC, 65/0; LIFE, 164/0; MARIE, 41/105; MAYO, 208/210; MNYR, 293/409; MSKCC, 310/0; NC-BCFR, 234/233; OFBCR, 553/560; POSH, 377/0; HBOC, 47/47; BBCS, 612/1333; UPENN, 280/0 This study was funded by a grant CA165038 to Christopher Haiman (University of Southern California) and John Hopper (University of Melbourne) from the National Cancer Institute, National Institute of Health. The contributing studies: Multiethnic Cohort (MEC). This study was supported by grant UM1 CA164973 from the National Cancer Institute, National Institute of Health. Ontario Familial Breast Cancer Registry, the Ontario site of the Breast Cancer Family Registry Cohort (OFBCR). This study was supported by grant UM1 CA164920 from the National Cancer Institute. Utah Breast Cancer Family Registry (BCFR-UT). This study was supported by grant UM1 CA164920 from the National Cancer Institute. New York site of the Breast Cancer Family Registry (MNYR). This study was supported by grant UM1 CA164920 from the National Cancer Institute. Northern California site of the Breast Cancer Family Registry (NC-BCFR). This study was supported by grant UM1 CA164920 from the National Cancer Institute. Australian Breast Cancer Family Registry (ABCFR). This study was supported by grant UM1 CA164920 from the National Cancer Institute. Breast Cancer Study (CNIO-BC). This study has been partially funded by The Spanish Network on Rare Diseases (CIBERER) and the Spanish National Genotyping Center (CEGEN). Genetic Epidemiologic Study of Breast Cancer (GESBC). The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and German Cancer Research Center (DKFZ). Mammary Carcinoma Risk Factor Study (MARIE). This study was supported by the Deutsche Krebshilfe e.V. [70-2892-BR I, 106332, 108253, 108419], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. Prospective study of Outcomes in Sporadic versus Hereditary breast cancer (POSH). Funding for the POSH study was provided by Cancer Research UK (grant refs A7572, A11699, C22524), the Breast Cancer Campaign (grant number: 2013MayPR044) and from 2003-2006 by a grant from The Wessex Cancer Trust. Prospective study of Outcomes in Sporadic versus Hereditary breast cancer (POSH). Funding for the POSH study was provided by Cancer Research UK (grant refs A7572, A11699, C22524), the Breast Cancer Campaign (grant number: 2013MayPR044) and from 2003-2006 by a grant from The Wessex Cancer Trust. Hereditary Breast and ovarian Cancer: Genetic and Molecular Studies (HBOC). This study was supported by National Cancer Institute grant CA58860 and The Lon V Smith Foundation: LVSF-44528. Mayo Clinic inherited breast and ovarian cancer study (MAYO). This study was supported by the Breast Cancer Research Foundation, NIH grants CA192393, CA176785, and an NIH CA116201 Specialized Program of Research Excellence (SPORE) in Breast Cancer. British Breast Cancer Study (BBCS); Mammographic oestrogens and growth factor study (MOG). The BBCS and the MOG study are funded by Cancer Research UK and Breakthrough Breast Cancer and acknowledge NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). Genotyping of non-BRCA1/2 mutation carriers (UPENN). The study is supported by the Basser Research Center at the University of Pennsylvania, Rooney Family Foundation, NIH grants CA176785 and CA192393, the Breast Cancer Research Foundation, the Susan G. Komen Foundation for the Cure and Macdonald Family Foundation. Clinical Significance of Germline BRCA Mutations (MSKCC). The study is supported by the Robert and Kate Niehaus Clinical Cancer Genetics Research Initiative, The Breast Cancer Research Foundation, and the Cancer Center Support Grant from the National Institute of Health, National Cancer Institute 5P30 CA08748-40. Women's Learning the Influence of Family and Environment (LIFE). This study was supported by grants CA17054 and CA74847 from the National Cancer Institute, National Institutes of Health, No. 4PB-0092 from the California Breast Cancer Research Program of the University of California. Philadelphia site of the Breast Cancer Family Registry at Fox Chase Cancer Center (FCCC). This study is supported by NIH grant CA164920.

  Study phs000929

• Whole genome MeDIP-seq of DNA in whole blood samples from 5yr old Swedish children (the ABIS study)

  Dataset EGAD00001001221

• Exome-seq data from study of juvenile myelomonocytic leukemia (JMML)

  Whole exome seqeuncing from primary human JMML samples

  Dataset EGAD00001003419

• Psoriasis PBMCs

  This DAC is for the purpose of controlling access to Psoriasis PBMC scRNA-seq data of the study Spermidine/spermine N1-acetyltransferase controls tissue-specific regulatory T cell function in chronic inflammation

  Dac EGAC50000000470

• Reconstructed BCRs

  Computationally reconstructed B-cell receptor sequences (using BraCeR) from scRNA-seq data for all cells passing quality control.

  Dataset EGAD00001008456

• DLBCL Discovery (FF) Cohort

  miRNA-seq Cohort of 92 Fresh Frozen Diffuse Large B-cell Lymphoma Patient Samples

  Dataset EGAD00001001074

• ATAC data for Glioblastoma (EGAS00001003953, H016)

  ATAC-seq data for 2 glioblastoma cell lines (LN229, ZH487), NT and SOX10KD.

  Dataset EGAD00001006544

• SF10207

  SF10207 snATAC Seq. Oligodendroglioma, Anaplastic (WHO gr. 3). Tumor Location:Frontal. Age:43. Sex: Male

  Dataset EGAD00001006319

• Breast Cancer - Very young women

  This project will focus on sequencing of breast cancer from young age cancer patients characterized by age at diagnosis under 35. Based on the incidence rate by Korean National Cancer Incidence Database, breast cancer is the most common solid tumors among young adults aged 15-34 excepting thyroid cancer (Cancer statistics in Korea (2012)). This project just started from the mid of 2015. National cancer center in Korea are collecting more than 100 tumor specimens from breast cancer patients aged 20-34. Genetic features of young patients will be investigated with somatic mutation pattern, structural variations, and canonical pathways.

  Study EGAS00001001908

• DAC for "The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia"

  Dac EGAC00001001501

• Solve-RD data - RNA Sequencing, idib-capella_group samples

  Dataset EGAD00001012114

• Establishment and characterization of a new Pseudomonas aeruginosa infection model using 2D airway organoids and dual RNA sequencing

  Study EGAS00001007463

• sncRNAseq human islets

  small non-coding RNA sequencing of 3 non diabetic donors and 3 donors with T2D.

  Dac EGAC50000000539

• Environmental Influences on Child Health Outcomes (ECHO) - PATHWAYS - Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE)

  Pregnant women are exposed daily to multiple chemical and non-chemical stressors, including air pollutants, phthalates, and psychosocial stress. The Developmental Origins of Health and Disease model (DOHaD) states that exposure to these stressors in pregnancy affects fetal development in a manner that impacts offspring health across the life course. Yet epidemiologic data in these areas is limited, particularly with U.S. samples. It is also poorly understood whether these exposures prenatally affect childhood neurodevelopment and airway health in an independent or combined manner, and likely moderators of effects, such as the sex of the child, are infrequently addressed. We propose to unify three diverse extant pregnancy cohorts: TIDES (N=717, 2010-12), GAPPS (N=1133, 2012-16), and CANDLE (N=1385, 2007-11), for a combined sample size of 3235 mother-child dyads in the PATHWAYS study. PATHWAYS will investigate how chemical (air pollutants and phthalates) and non-chemical (psychosocial stress) exposures during pregnancy are related to placental gene expression (transcriptome) and childhood neurodevelopment and airway health (at ages 4-6, 8-9, and 10-11 years). Each cohort has rich resources of prenatal data and banked specimens (urine, blood, and placenta) that will be harmonized for the PATHWAYS study and will contribute to the ECHO consortium. We will develop a national model with high spatiotemporal resolution of key air pollutants and assess urinary markers of maternal exposure. A composite measure will capture multilevel maternal psychosocial stress across pregnancy, and urinary phthalate and blood stress hormone [Corticotropin-releasing hormone (CRH)] levels in the second and third trimesters will provide individual assessment of those exposures in potential critical periods. We will characterize the placental transcriptome using RNA sequencing (RNA-Seq) and will assess neurodevelopment and airway health prospectively into middle childhood. PATHWAYS will examine how these prenatal exposures are related to the placental transcriptome and child health outcomes in main effect and interactive models, with emphasis on sex-specific associations. For both neurodevelopment and airway health, we propose to measure both phenotypic precursors of health outcomes (i.e. fluid cognition, lung function growth), which yield dimensional tests of proposed associations, as well as clinically meaningful and policy relevant outcomes (i.e. asthma, mental health). Our study is powered to assess interactive effects of chemical and non-chemical stressors and will be the first study to characterize how prenatal environmental exposures relate to placental transcriptome pathways in relation to childhood health outcomes. This represents a significant scientific advance in testing DOHaD hypotheses. Major contributions to the ECHO Consortium include: 1) the development of a state of the art national model of air pollution, 2) a large, diverse pregnancy cohort with extensive biorepositories and extant prenatal and postnatal biomarkers, placental transcriptome, psychosocial and environmental data, and 3) an experienced, interdisciplinary team that will contribute meaningfully to the ECHO program of work.The sequencing data in this submission pertains only to the 794 subjects from the CANDLE study for whom placental tissue transcriptomics was performed.

  Study phs003619

• Environmental Influences on Child Health Outcomes (ECHO) - PATHWAYS - Global Alliance to Prevent Prematurity and Stillbirth (GAPPS)

  Pregnant women are exposed daily to multiple chemical and non-chemical stressors, including air pollutants, phthalates, and psychosocial stress. The Developmental Origins of Health and Disease model (DOHaD) states that exposure to these stressors in pregnancy affects fetal development in a manner that impacts offspring health across the life course. Yet epidemiologic data in these areas is limited, particularly with U.S. samples. It is also poorly understood whether these exposures prenatally affect childhood neurodevelopment and airway health in an independent or combined manner, and likely moderators of effects, such as the sex of the child, are infrequently addressed. We propose to unify three diverse extant pregnancy cohorts: TIDES (N=717, 2010-12), GAPPS (N=1133, 2012-16), and CANDLE (N=1385, 2007-11), for a combined sample size of 3235 mother-child dyads in the PATHWAYS study. PATHWAYS will investigate how chemical (air pollutants and phthalates) and non-chemical (psychosocial stress) exposures during pregnancy are related to placental gene expression (transcriptome) and childhood neurodevelopment and airway health (at ages 4-6, 8-9, and 10-11 years). Each cohort has rich resources of prenatal data and banked specimens (urine, blood, and placenta) that will be harmonized for the PATHWAYS study and will contribute to the ECHO consortium. We will develop a national model with high spatiotemporal resolution of key air pollutants and assess urinary markers of maternal exposure. A composite measure will capture multilevel maternal psychosocial stress across pregnancy, and urinary phthalate and blood stress hormone [Corticotropin-Releasing Hormone (CRH)] levels in the second and third trimesters will provide individual assessment of those exposures in potential critical periods. We will characterize the placental transcriptome using RNA sequencing (RNA-Seq) and will assess neurodevelopment and airway health prospectively into middle childhood. PATHWAYS will examine how these prenatal exposures are related to the placental transcriptome and child health outcomes in main effect and interactive models, with emphasis on sex-specific associations. For both neurodevelopment and airway health, we propose to measure both phenotypic precursors of health outcomes (i.e., fluid cognition, lung function growth), which yield dimensional tests of proposed associations, as well as clinically meaningful and policy relevant outcomes (i.e., asthma, mental health). Our study is powered to assess interactive effects of chemical and non-chemical stressors and will be the first study to characterize how prenatal environmental exposures relate to placental transcriptome pathways in relation to childhood health outcomes. This represents a significant scientific advance in testing DOHaD hypotheses. Major contributions to the ECHO Consortium include: 1) the development of a state of the art national model of air pollution, 2) a large, diverse pregnancy cohort with extensive biorepositories and extant prenatal and postnatal biomarkers, placental transcriptome, psychosocial and environmental data, and 3) an experienced, interdisciplinary team that will contribute meaningfully to the ECHO program of work.The sequencing data in this submission pertain only to the 408 subjects from the GAPPS study for whom placental tissue transcriptomics was performed.

  Study phs003620

• National Cancer Institute (NCI) Study of Lung Cancer and Smoking Phenotypes in African-American Cases and Controls

  This is a two-stage case-control study designed to evaluate the association between common genetic variants and the risk of lung cancer. The stage 1 studies included 1737 cases and 3602 controls from the following studies: MD Anderson Lung Cancer Epidemiology Study, The Multiethnic Cohort Study (MEC), NCI-MD Lung Cancer-Case Control Study, Northern California Lung Cancer Study, Project CHURCH (Creating a Higher Understanding of Cancer Research δ Community Health), Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), Southern Community Cohort Study (SCCS), and the Karmanos Cancer Institute at Wayne State University (KCI/WSU). The stage 2 studies included an independent set of 866 cases and 796 controls from the following studies: The Black Women's Health Study (BWHS), The Harvard-MGH Lung Cancer Susceptibility Study (HLCS), MD Anderson Lung Cancer Epidemiology Study, MD Anderson/LBJ Hospital Biorepository, NCI-MD Lung Cancer Case-Control Study, Northern California Lung Cancer Study, Philadelphia Lung Cancer Study on Gene Environment Interactions (Plus-Gene), Southern Community Cohort Study (SCCS), and KCI/WSU.

  Study phs001210

• RNA isoform repertoire of neuropsychiatric risk genes in human brain

  Neuropsychiatric disorders are highly complex conditions and the risk of developing a disorder has been tied to hundreds of genomic variants that alter the expression and/or products (isoforms) made by risk genes. However, how these genes contribute to disease risk and onset through altered expression and RNA splicing is not well understood. Here we show our current understanding of gene isoforms is far from complete and reveal the precise splicing profiles of neuropsychiatric disorder risk genes. Combining our new bioinformatic pipeline IsoLamp with nanopore long-read amplicon sequencing, we deeply profiled the RNA isoform repertoire of 31 high-confidence neuropsychiatric disorder risk genes in human brain. We show most risk genes are more complex than previously reported, identifying 443 novel isoforms and 28 novel exons, including isoforms which alter protein domains, and genes such as ATG13 and GATAD2A where most expression was from previously undiscovered isoforms. The greatest isoform diversity was present in the schizophrenia risk gene ITIH4. Mass spectrometry of brain protein isolates confirmed translation of a novel exon skipping event in ITIH4, suggesting a new regulatory mechanism for this gene in brain. Our results emphasize the widespread presence of previously undetected RNA and protein isoforms in brain and provide an effective approach to address this knowledge gap. Uncovering the isoform repertoire of neuropsychiatric risk genes will underpin future analyses of the functional impact these isoforms have on neuropsychiatric disorders, enabling the translation of genomic findings into a pathophysiological understanding of disease.

  Study EGAS00001007744

• Comparison of WGBS, EPIC array, EM-seq, and Nanopore sequencing in assessing DNA methylation marks

  Bisulfite sequencing has long been the default method for analyzing methylation marks due to its single base resolution. However, the associated DNA degradation poses a concern for fragmented samples. Although several methods have been proposed to circumvent this issue, there has yet to be a clear consensus on which method is best suited for detecting methylation. We present an overview of the two mostly used and two alternative approaches: whole-genome bisulfite sequencing (WGBS), Illumina methylation microarray (EPIC), Enzymatic Methyl-sequencing (EM-seq) and third-generation sequencing by Oxford Nanopore Technologies (ONT). We evaluated DNA methylation levels in three human genomes derived from tissue, cell line, and whole blood.

  Study EGAS00001008014

• ABIS_1_MeDIP-seq

  We investigated changes in whole genome DNA-methylation in a group of 48 healthy five-year-old children differentiated by their hair cortisol levels. By combining this novel biomarker for chronic stress with methylated DNA immunoprecipitation sequencing (MeDIP-seq), we found that high cortisol were associated with a genome-wide decrease in DNA-methylation targeted to specific zinc finger transcription factor binding sites, repetitive elements and genes important for neurodevelopment and calcium transport. Large scale hypomethylation of repetitive regions, as well as a deficient calcium transport, have been reported in many stress related diseases, as well as in many cancers and biological aging. Thus our findings may aid in our understanding of how susceptibility to these diseases develops.

  Study EGAS00001001099

• scRNA-seq of HSPC treated with gemcitabine and carboplatin

  4 samples of 2000000 HSPCs in 2 ml media each were then prepared and treated for 24 hours: carboplatin-high: 150 µg/ml carboplatin carboplatin-low: 18.75 µg/ml carboplatin gemcitabine: 25 ng/ml gemcitabine control: no drug Single-cells were subsequently extracted using the ddSEQ™ Single-Cell Isolator (Bio-Rad) and later sequenced using the SureCell™ Whole Transcriptome Analysis 3' Library Prep Kit (Illumina) on the NextSeq 500 System (Illumina) using the NextSeq 500/550 High Output Kit v2.5 150 Cycles (Illumina) all following the manufacturer's instructions. The raw FASTQ-files are available as read 1 and read 2 files from 4 lanes for each sample giving a total of 16 FASTQ-files. The FASTQ-files were also processed following the ddSeeker (Romagnoli, D., et al., BMC Genomics 2018, doi:10.1186/s12864-018-5249-x) and Drop-seq (Macosko, E.Z., et al., Cell 2015, doi:10.1016/j.cell.2015.05.002) protocols for processing scRNA-seq data to yield the final digital gene expression (dge) for each cell of each sample. This has resulted in one dge text file and one dge summary text file per sample. These 8 text-files with dge data are found in the zip-compressed analysis data-file.

  Dataset EGAD00001006080

• Base modification analysis using single molecule real-time sequencing

  Based on the kinetic characteristics of single-molecule real-time sequencing, we implement a method to directly detect DNA modifications such as 5mC. However, there are room to further improve the accuracy of whole-genome-wide DNA modifications. Therefore, this study aims to utilize deep learning methods for the comprehensive profiling of multiple DNA modifications, including 5mC, 5hmC, and 6mA, using SMRT-seq.

  Study EGAS50000000366

• Gremlin 1 + fibroblastic niche maintains dendritic cell homeostasis in lymphoid tissues

  Although cross-species transcriptional analysis has been generated for DCs37, transcriptomic conservation between mouse and human FRCs at single-cell resolution has been unclear. To test whether GREM1+ FRCs might also play a role in DC homeostasis in humans, we performed scRNA-seq of CD45–PDPN+ stromal cells, as well as CD45+CD11c+ immune cells from healthy human LNs of three donors.

  Study EGAS00001006211