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• 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

• The Genomics and Randomized Trials Network (GARNET) Vitamin Intervention Stroke Prevention (VISP) Trial

  The VISP trial (PI Jim Toole, M.D., Wake Forest University School of Medicine) was a multi-center, double-blind, randomized, controlled clinical trial that enrolled patients aged 35 or older with homocysteine (Hcy) levels above the 25th percentile at screening and a non-disabling cerebral infarction (NDCI) within 120 days of randomization (Toole, 2002, PMID: 12417369). The trial was designed to determine if daily intake of a multivitamin tablet with high dose folic acid, vitamin B6 and vitamin B12 reduced recurrent cerebral infarction (primary endpoint), and nonfatal myocardial infarction (MI) or mortality (secondary endpoints). Subjects were randomly assigned to receive daily doses of the high-dose formulation (n=1,827), containing 25mg pyridoxine (B6), 0.4mg cobalamin (B12), and 2.5mg folic acid; or the low-dose formulation (n=1,853), containing 200mcg pyridoxine, 6mcg cobalamin and 20mcg folic acid. Enrollment in VISP began in August 1997, and was completed in December 2001, with 3,680 participants enrolled. Within the trial, 2,164 participants from 46 clinic sites provided DNA and agreed for it to be shared for use in a genetic subset study of VISP. This study is part of Genomics and Randomized Trials Network (GARNET), funded by the National Human Genome Research Institute (NHGRI). The overarching goal is to identify novel genetic factors that contribute to stroke through large-scale genome-wide association studies of treatment response in randomized clinical trials. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were performed at the GARNET Coordinating Center at the University of Washington. The data of the VISP trial have been released to dbGaP users in several segments: Version 1 (phs000343.v1.p1), consisted of n=4 phenotype datasets, and all raw, cleaned and imputed genotype data. Version 2 (phs000343.v2.p1) included n=14 additional phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=970 phenotype variables. Version 3 (phs000343.v3.p1) included all n=36 phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=1918 phenotype variables.Version 4 (phs000343.v4.p1), the current release, includes all previous phenotypic datasets, includes one new phenotypic dataset for the untargeted metabolomics data of 50 individuals, includes DNA methylation profiles for n=180 individuals, and sequencing data for n=179 individuals.

  Study phs000343

• Mucociliary Clearance Consortium (MCC) Rare Genetic Disorders of the Airways: Cross-section Comparison of Clinical Features, and Development of Novel Screening and Genetic Test

  We have established a consortium of 7 geographically-dispersed clinical research sites that are designed to study rare diseases of the airways, which involve defects in clearance of mucus secretions from the airways (defective "mucociliary clearance"). These sites will collaborate in diagnostic, genetic, and other studies in patients with impairments of mucociliary clearance, including primary ciliary dyskinesia (PCD), variant cystic fibrosis (CF), and pseudohypoaldosteronism (PHA). These disorders reflect genetic defects in airway host-defense, and typically result in chronic infection of the airways. Patients with these disorders of the conducting airways and sinuses have delayed (or incorrect) diagnoses, because diagnostic tests are not readily available. These patients may also have sub-optimal management of their clinical disease, because the cause of these disorders is not well-defined, and treatment regimens are usually not driven by evidence-based medicine. This current protocol is designed to employ a systematic approach to the diagnostic evaluation of these patients with chronic airways disease, which will yield precise diagnoses in individual patients, and will be associated with development of better diagnostic techniques, including genetic testing. In addition, we will compare/contrast clinical features (phenotype) across these disorders. A rigorous cross-sectional comparison of the clinical features will provide better understanding of the clinical disease of these disorders; in turn, this will lead not only to a better standard of clinical care, but will also assist in the identification of novel therapeutic approaches. The patient population for these studies includes individuals carrying a tentative diagnosis of the three disorders (PCD, variant CF or PHA), plus individuals suspected to have one of these disorders, but with inadequate or inconclusive diagnostic tests. Individuals in this latter category must have a preliminary clinical evaluation to evaluate for other more common disorders that may have similar manifestations including classical CF, immunodeficiency, asthma, and severe gastroesophageal reflux. We have an informal agreement with a network of pulmonologists across the United States to perform clinically-indicated (not research) preliminary clinical testing, if not directly available to patients who inquire about these studies. Blood will be collected for DNA to test for genetic mutations. The blood samples will be processed to establish a cell line (lymphocyte transformation) for a source of DNA for future genetic studies, in participants with disease. For participants unable to provide a blood sample, a buccal sample will be obtained for DNA.

  Study phs000595

• NHLBI TOPMed: The Cleveland Family Study (CFS)

  The Cleveland Family Study (CFS) is one cohort involved in the WGS project. The CFS was designed to provide fundamental epidemiological data on genetic and non-genetic risk factors for sleep disordered breathing (SDB). In brief, the CFS is a family-based study that enrolled a total of 2284 individuals from 361 families between 1990 and 2006. The sample was selected by identifying affected probands who had laboratory diagnosed obstructive sleep apnea. All first degree relatives, spouses and available second degree relatives of affected probands were studied. In addition, during the first 5 study years, neighborhood control families were identified through a neighborhood proband, and his/her spouses and first degree relatives. Each exam, occurring at approximately 4 year intervals, included new enrollment as well as follow up exams for previously enrolled subjects. For the first three visits, data, including an overnight sleep study, were collected in the participants' homes while the last visit occurred in a general clinical research center (GCRC). Phenotypic characterization of the entire cohort included overnight sleep apnea studies, blood pressure, spirometry, anthropometry and questionnaires. The GCRC exam (n=735 selected individuals) included more comprehensive phenotype data on a focused subsample of the larger cohort, to permit linking SDB phenotypes with cardio-metabolic phenotypes, with an interest in identifying genetic loci that are associated with these related phenotypes. In this last round of data collection, a subset of 735 individuals was selected based on expected genetic informativity by choosing pedigrees where siblings had extremes of the apnea hypopnea index (AHI). Participants underwent detailed phenotyping including laboratory polysomnography (PSG), ECG, spirometry, nasal and oral acoustic reflectometry, vigilance testing, and blood and urine collection before and after sleep and after an oral glucose tolerance test. A wide range of biochemical measures of inflammation and metabolism were assayed by a Core Laboratory at the University of Vermont. 994 individuals were sequenced as part of TOPMed Phase 1, including 507 African-Americans and 487 European-Americans. Among the sequenced individuals, 156 were probands with diagnosed sleep apnea, an additional 706 were members of families with probands, and 132 were from neighborhood control families. 298 individuals were sequenced as part of TOPMed Phase 3.5, including 169 African-Americans and 129 European-Americans. Among the newly sequenced individuals, 33 were probands with diagnosed sleep apnea, an additional 214 were members of families with probands, and 51 were from neighborhood control families. Please note: Phenotype and pedigree data are available through "NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)", phs000284.

  Study phs000954

• Oxford Nanopore Adaptive Sampling WGS

  Eight samples were analyzed on an Oxford Nanopore MinION flow-cell, and to evaluate multiplexing, four samples were run together on one flow-cell on the P2 Solo device. Using adaptive sampling, we targeted 10 regions of different structural variant types, including deletions, translocations and complex rearrangements. MinION sequencing resulted in between 14.1-18.3 Gb of data per flow cell, with mean autosomal on-target coverage of 28.4x and off-target read depth coverage of 5.3x.

  Dataset EGAD50000001821

• PASCAL-MID Targeted amplicon sequencing of CD34+ HSPCs (CGD)

  High-throughput amplicon sequencing were performed on CD34+ hematopoietic stem and progenitor cells (HSPCs) derived from 4 healthy donors and one unaffected heterozygous carrier of a CYBB c.252G>A variant causing X-linked granulomatous disease. Targeted genome editing were performed in the HSPCs of CYBA and CYBB genes using CRISPR-Cas9 and recombinant AAV6. To assess gene editing outcomes in CD34+ HSPCs, the targeted loci were amplified from genomic DNA and sequenced on an Illumina iSeq100.

  Dataset EGAD50000001651

• RNA-seq FASTQ files studied in Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

  This data set contains 12 paired fastq files (RNA-seq) from one wild-type iPS cell, two HLA-KO iPS clones and three CD14+ monocytes differentiated from them. The RNA-seq libraries were generated with Illumina TruSeq Stranded Total RNA library prep kit. The libraries were sequenced with Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD50000001700

• High-grade serous ovarian carcinoma tumour exome sequencing variants

  Tumour variants (in VCF format) from exome sequencing (using Agilent SureSelect and TWIST library preparation systems followed by sequencing on Illumina HiSeq and NovaSeq platforms) of 108 high-grade serous ovarian tumour samples. Germline variants from EGAD00001006030 for these individuals were used to call somatic and tumour-only variants separately, creating two VCF files per sample (except for one sample without paired germline data- somatic variant file only provided). The dataset comprises 215 VCF variant files.

  Dataset EGAD50000001132

• Transcriptome analysis of patient-derived Schwann cells isolated from human sural nerve biopsies

  This dataset contains the transcriptome analysis of human Schwann cell cultures obtained from sural nerve biopsies from six patients with polyneuropathies (N=3/patient, totalN=18) as well as the nerve fascicles from where they were isolated (N=1/patient, totalN=6). In addition, this dataset includes 8 samples out of commercial human Schwann cell cultures and one sample of a fibroblast culture, which serve as controls for further analysis.

  Dataset EGAD50000001021

• ST dataset from subcortical white matter MS lesions (CA & CI) and controls

  This dataset contains the FASTQ files, the correspondent H&E pictures with the fiducial frames and the json files that were used for the spatial transcriptomic analysis in our paper (n=19). Within the json files names, one can find the information about the slide name (V11M111-111) and the capture area (A1) for each sample.

  Dataset EGAD50000000520