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• 4C-seq data

  4C-seq data was generated for regions of interest to confirm enhancer-gene promoter interactions

  Dataset EGAD00001001847

• Isala Cross sectional Flow Meta Data

  This meta data contains extensive meta data from the cross sectional flow of the Isala Citizen Science project. The meta data file contains ENA Accession numbers for 16S Microbiome data, and cleaned responses to questionnaires.

  Dataset EGAD00001009890

• Dataset for synovial_sarcoma-WHOLE_GENOME

  Rare cancer sequencing data of 6 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008899

• Sequencing data for the manuscript "Early on-treatment changes in circulating tumor DNA fraction and response to enzalutamide or abiraterone in metastatic castration-resistant prostate cancer"

  This submission contains gzipped fastq files from paired-end targeted sequencing.

  Dataset EGAD00001010105

• RNA-seq dataset of CD34+ HSPCs from LRMDS patients

  To profile transcriptional alterations of HSPCs in LR-MDS patients, 7AAD−CD45+Lin-CD235a−CD34+ cells were isolated from healthy donors and LR-MDS patients for RNA sequencing.

  Dataset EGAD00001015771

• BRIDGE Bleeding and Platelet Disorders

  Whole exome sequencing BAM files for samples from the BRIDGE Consortium with pathogenic or likely pathogenic variants on genes linked to bleeding or platelet disorders.

  Dataset EGAD00001001333

• XClone for analyzing somatic copy number alterations

  Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

  Study EGAS00001007854

• National Institute of Neurological Disorders and Stroke (NINDS) Parkinson's Disease

  This study utilized the well characterized collection of North American Caucasians with Parkinson's disease, and neurologically normal controls from the sample population which are banked in the National Institute of Neurological Disorders and Stroke (NINDS Repository) collection. Two sub-studies are included, in sub-study NINDS-Genome-Wide Genotyping in Parkinson's Disease, genome-wide, single nucleotide polymorphism (SNP) genotyping of these publicly available samples was originally done in 267 Parkinson's disease patients and 270 controls, and this has been extended to include genome wide genotyping in 939 Parkinson's disease cases and 802 controls; in sub-study NINDS-Exome Sequencing in Parkinson's Disease, genome wide exome DNA sequencing was done in 618 Parkinson's disease samples deposited in Coriell NINDS repository. The DNA comes from a mixture of blood, and from cell lines (lymphoblast cell lines) derived from blood. The GWAS data and exome sequencing data was generated and provided by the laboratory of Neurogenetics lead by Dr. Andrew Singleton, NIA, and Dr. John Hardy, a former chief at NIA, NIH. The NINDS Parkinson's Disease Cohort study is utilized in the following dbGaP individual studies. To view genotypes, whole exome sequencing, and derived variables collected in these individual studies, please click on the following individual studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001172 NINDS Parkinson's Disease Cohort study. phs000089 PD GWAS phs001103 PD Exome

  Study phs001172

• Nasal MicroRNA during Bronchiolitis and Age 6y Asthma: MARC-35 Cohort

  We analyzed data from the 35th Multicenter Airway Research Collaboration (MARC-35) study to examine the relationship between nasal airway microRNAs (miRNAs) and the risk of developing childhood asthma. MARC-35 is a multicenter prospective cohort study which enrolled infants (age Structured interviews of the parents/guardians were performed by site investigators and clinical details were collected via emergency department and hospital inpatient chart reviews. Following the index hospitalization, trained study personnel conducted telephone interviews with parents/legal guardians at six-month intervals. Participants also underwent nasal swab sampling conducted by trained site personnel during the index bronchiolitis hospitalization and during an in-person follow-up examination at about age 6 years. Total RNA was extracted from nasal swab specimens using Trizol LS reagent (ThermoFisher Scientific, Waltham, MA) and the Direct-zol RNA Miniprep Kit (Zymo Research, Irvine, CA). Small RNA-seq was performed using the PerkinElmer NEXTFLEX® small RNA-seq v3 kit with Unique Dual Indexes (PerkinElmer, Waltham, MA) and sequenced on an Illumina NovaSeq6000 sequencer using an S2 50bp PE Flowcell (Illumina, San Diego, CA). Through dbGaP, limited de-identified phenotypic data (e.g., demographics) and RNA-seq FASTQ files will be made available. Among a cohort of infants hospitalized with bronchiolitis, we applied a small RNA sequencing approach and found miRNAs were associated with asthma risk factors, and highly expressed in lung tissue and immune cells (e.g., TH cells, neutrophils). They also played key roles in mechanisms relating to asthma, such as innate immunity, airway remodeling, and IgE regulation.

  Study phs003564

• WES analysis of a mixed cohort of pituitary tumors

  BACKGROUND: Sporadic pituitary adenomas are the second most common primary brain tumor, but their genetics remains incompletely understood. We performed deep DNA sequencing of 42 pituitary macroadenomas, including hormonally active and atypical adenomas. METHODS: Pituitary adenomas representative of a spectrum of histopathologic subtypes, primary and recurrent tumors, and MIB-1 proliferative indices were profiled. Whole-exome sequencing was performed on tumors and paired blood followed by analysis of somatic copy number alteration and mutation. RESULTS: On the whole, the tumors harbored a low rate of mutations, similar to other benign brain tumors. However, almost one-third (29%) exhibited somatic copy-number alterations across large fractions of the genome (up to 99%). Despite such widespread genomic disruption, these tumors had few focal events, which is atypical among highly-disrupted cancers. The other 71% of tumors formed a distinct molecular class, with somatic copy-number alterations involving less than 6% of the genome. Among the highly-disrupted group, 92% were functional or atypical null-cell adenomas, whereas 87% of the less-disrupted group were clinically non-functional adenomas. The only significantly recurrent events were arm-level losses of chromosomes 1, 2, 11, and 18. No significantly recurrent mutations or insertion/deletion events were identified, suggesting no genes are altered by exomic mutations in >15% of pituitary tumors. CONCLUSIONS: Genomic characterization of a large series of sporadic pituitary adenomas reveals two classes of tumor, based on genomic disruption from arm-level copy-number alterations, which associate with hormonal and histologic subtypes.

  Study EGAS00001001714