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• Normal sample for patient SA1064

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009591

• Normal sample for patient SA1065

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009592

• Normal sample for patient SA1069

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009593

• Normal sample for patient SA1073

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009594

• Normal sample for patient SA1074

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009595

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA610

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009597

• Normal sample for patient SA992

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009598

• Normal sample for patient SA994

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009599

• Normal sample for patient SA288

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009601

• Normal sample for patient SA299

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009602

• Normal sample for patient SA415

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009603

• Normal sample for patient SA666

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009604

• Tumour sample for patient SA1065

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009605

• Tumour sample for patient SA1064

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009606

• Tumour sample for patient SA1026

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009607

• Tumour sample for patient SA1069

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009608

• Tumour sample for patient SA1017

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009609

• Tumour sample for patient SA1027

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009610

• Tumour sample for patient SA1073

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009611

• Tumour sample for patient SA1074

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009612

• Tumour sample for patient SA576

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009613

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA1040

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009615

• Tumour sample for patient SA992

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009616

• Tumour sample for patient SA610

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009617

• Normal sample for patient SA416

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009618

• Normal sample for patient SA1070

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009619

• Tumour sample for patient SA1070

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009620

• Tumour sample for patient SA997

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009621

• PREDICT-HD Huntington Disease Study

  The purpose of this project is to make clinical measurements from the PREDICT-HD consortium available through the dbGaP mechanism. The phenotype data will first be converted into a community open standard and subsequently exported to dbGaP for archival and open access distribution of the results of the studies. This will permit members of the scientific community to utilize a permanent resource for investigating the interactions of phenotypes upon an international cohort of early Huntington Disease. In version 2 cut of the data we provided HD CAG repeat lengths for both alleles as well as enrollment age of all participants. We have also generated unique identifiers prospectively compatible with the larger initiative GWAS in Huntington's Disease project (also on DbGaP). As such, the version 1 cut of the data was mainly proof of concept and should be deprecated. Going forward, all updates will add-on to version 2 cut of the data. In version 3 cut of the data, we provided baseline or the first usable MRI T1-weighted imaging analysis subcortical and cortical segmentations and cortical parcellations based on a customized Freesurfer 5.2 pipeline developed at The University of Iowa. The customizations to the standard pipeline were mainly to improve bias field correction and image normalization such that segmentation of gray, white, internal csf, dura and surface CSF are optimized for the Freesurfer pipeline. There are 1111 subjects with results in this data release. In version 4 cut of the data, we provided all longitudinal clinical measurements for all subjects (total of 1476) assessmented or enrolled through the end of 2013. Additionally, we are providing measurements on 39 baseline FDG PET images spatially normalized by SPM5 into MNI space, relative regional metabolic values computed in 120 volumes of interest (VOI) defined in the Automated Anatomical Labeling (AAL) Atlas (Tzourio-Mazoyer et al. 2002), and global metabolic values calculated by SPM standard mean voxel value (within per image fullmean/8 mask). This project is a funded ancillary study of PREDICT-HD. In version 5 cut of the data, we provided the first of many forthcoming results from ancilliary studies of PREDICT-HD. In this data cut, we provide individual subject results derived from structural MRI data. The earliest MRI session for each subject was used. The results summarized represent source based morphometry loading coefficients for 23 components (see: "Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome", Ciarochi et al., 2016, Front Neurol. 2016; 7: 147, Published online 2016 Sep 21. doi: 10.3389/fneur.2016.00147]. In this version 6 cut of the data, we provide a full set of derived data, more than 10,000 raw MRI images, and ancillary study data sets. For sample information please link to: PREDICT-HD Biospecimen Resources

  Study phs000222

• mRNA and T cell receptor sequencing of patients with Pandemrix-associated narcolepsy type 1

  Increased risk of Narcolepsy type 1 (NT1) was reported among children and adolescents vaccinated with AS03-adjuvanted pandemic influenza A vaccine (H1N1; Pandemrix®). We hypothesized that viral T cell epitope(s) in Pandemrix mimicked self-epitope(s), and that recognition of these cross-reactive epitopes contributed to disease-specific autoimmunity. We demonstrate that pediatric, Pandemrix-associated NT1 patients had enhanced T-cell immunity against dominant T-cell epitopes of Pandemrix vaccine viral proteins neuraminidase (NA) and nucleoprotein (NP), and also responded against a self-epitope in brain-expressed protein-O-mannosyltransferase 1 (POMT1). Here, the gene expression profiles and TCR repertoire of PBMC from patients and healthy controls were compared. PBMC samples were stimulated with peptides NA-175-189 or POMT1-675-589, and bulk RNA isolated and sequenced (RNAseq and TCRseq).

  Study EGAS00001004886

• MD Anderson GU Data DAC 1

  Dac EGAC00001001412

• Development and Validation of a Disability Severity Index for Charcot-Marie-Tooth Disease (CMT)

  In Charcot-Marie-Tooth Disease (CMT) or inherited neuropathy research studies, it is the researcher who has selected what they believe to be important markers of impairment in function in patients. For example, it has been inferred that the wearing of ankle-foot-orthosis (AFOs), the use of walking aids such as canes, or the use of wheelchairs, are appropriate markers for “mild”, “moderate” or “severe” disability, respectively. Whether patients agree with this classification is unknown. By understanding what patients classify as mild, moderate and severe disability in CMT, we will know what our treatments need to target, to have a meaningful impact on the patients' functional status. Primary objective: The purpose of this study is to compare patient and healthcare provider impressions of what constitutes mild, moderate and severe disability in CMT. Target population: 200 patients who have self-registered at the Inherited Neuropathy Consortium Contact Registry, a web-based contact registry developed and supported by the Data Management and Coordinating Center (DMCC) for the Rare Diseases Clinical Research Consortium (RDCRN), located at the University of South Florida, and 200 health care professionals attending conferences about CMT, such as the 4th International CMT Consortium to be held in Potomac, Maryland, June 29-July 1, 2011 and the MDA Clinic Directors meeting in Las Vegas, NV March 4-7, 2012. Methods: A brief, anonymous, 20-item survey, in which we measure what the physician and the patient think are important indicators of disability in CMT, will be distributed by paper to 200 health care professionals, and via an online link to 200 patients self-registered with the RDCRN contact registry. Out of the 200 patients, approximately 25 patients will be requested to take the survey twice in a 2 to 4 week period. Analysis: We will measure the agreement between items in the physician and patient groups, and use items with high agreement in a Disability Severity Index.

  Study phs001295

• 419 Japanese healthy control

  Genotypes of 419 Japanese healthy individuals genotyped by using of SNP arrays (Genome-Wide Human SNP Array 6.0 and Axiom Genome-Wide ASI 1 Array). CEL files and CHP files are included in this data set.

  Study JGAS000120

• Using Exome-sequencing to characterize the resistance to lirafugratinib

  We characterized resistance to lirafugratinib with whole exome sequencing in patients with FGFR2-driven cancers, treated in the phase 1/2 ReFocus trial (NCT04526106) and enrolled in the UNLOCK program at Gustave Roussy

  Study EGAS50000001371

• The dataset of Detection and characterization of lung cancer using cell-free DNA fragmentomes on NovaSeq 6000 at 1-2x coverage

  The dataset contains 364 CRAM files from whole-genome next-generation sequencing on the Illumina NovaSeq 6000 at 1- 2x coverage. These are plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD50000001961

• Whole genome sequencing of circulating cell-free DNA on Illumina and Ultima platforms

  The dataset includes Illumina (n=1) and Ultima (n=92) sequencing of circulating cell-free DNA. The dataset was generated through standard whole genome sequencing (Illumina n=1 & Ultima n = 15) and using a duplex unique molecular identifier whole genome sequencing workflow (Ultima, n=77). This dataset includes cancer samples and cancer-free control samples.

  Dataset EGAD50000001234

• Synthetic - GDI synthetic dataset (Population 11 Finland, Subgroup 2)

  Synthetic - This dataset contains the pheno-clinical and genomic information of 42046 individuals from COVID Population 11 Finland, Subgroup 2. 2010 are affected by Phenotype 1, 2010 are affected by Phenotype 2, 188 are affected by Phenotype 1 and 2, and 37838 are control. The dataset also contains the information about the smoking habits of each individual.

  Dataset EGAD50000000955

• APS-1 Global Gene Expression

  Single cell sequencing of expanded regulatory T cells (Tregs) in 9 APS-1 patients and 9 age and gender matched controls. Gene expression (GEX) libraries were generated by using the Library Construction Kit from 10x genomics.

  Dataset EGAD50000000260

• Y_chromosome_mis_segregation_in_the_DLD_1_cell_line

  Study of the well-established DLD-1 colorectal cancer cell line, engineered with an inactivatable Y centromere that leads to mis-segregation. The goal is to gain insights into the mechanism of chromothripsis due to chromosome mis-segration.

  Study EGAS00001002551

• RNAseq of ribosomal footprints

  Cancer genes can affect ribosomal RNA processing and this can underlie their essentiality to cells, making them cell-essential in the same way as ribosomal genes themselves. We want to confirm this, in order to understand the results of our CRISPR drop-out screens.NOTE FROM BESPOKE TEAM: Run a single read 1 (forward read) of 30 bases, then an index 1 read as normal. This would fit a 50cycle kit

  Dataset EGAD00001001930

• PIK3R4 (VPS15)

  WES of human: A mutation in VPS15 (PIK3R4) causes a ciliopathy and affects IFT20 release from the cis-Golgi WES (Agilent SureSelect All Exon XT2 50 Mb kit) has been realized on three affected siblings (II.1, II.3, II.5) and one healthy sister (II.4). Raw data (BAM files) are provided: - II.1.aligned.sorted.dedup.realign.recal.bam - II.3.aligned.sorted.dedup.realign.recal.bam - II.5.aligned.sorted.dedup.realign.recal.bam - II.4.aligned.sorted.dedup.realign.recal.bam

  Dataset EGAD00001002736

• Targeted amplicon sequencing on 218 samples from Stage 1 epithelial ovarian cancer biopises

  BAM files (aligned against the hg38 genome) from a targeted amplicon sequencing (139 genes) experiment (median depth 1000X) on 218 samples from Stage 1 epithelial ovarian cancer biopises. Samples labeled "bis" or "tris" with the same ID are relapses; "left" or "right" samples indicate, in the case of bilateral tumor, from which ovary the sample was taken.

  Dataset EGAD00001006873

• Targeted sequencing data of peripheral blood mononuclear cells obtained from 4 ATL patients and 10 HTLV-1-infected asymptomatic carriers.

  Targeted capture sequencing data of peripheral blood mononuclear cells (PBMCs) obtained from 4 ATL patients (6 samples) and 10 HTLV-1-infected asymptomatic carriers.

  Dataset EGAD00001007031

• Single-cell long-read transcriptomes from CH, MDS and AML patients with CH mutations

  Single-cell long-read (ONT) transcriptome sequencing from CH (n = 3), MDS (n = 5, MDS02 in 2 replicates) and AML (n =1, in 2 replicates) samples with mutations in splicing factors (SF3B1 - n = 8, U2AF1 n = 1) or transcription regulators (DNMT3A - CH04). Full length cDNA from 10x Genomics 3' V3.

  Dataset EGAD00001011282

• Whole transcriptome RNA sequencing as comprehensive diagnostic tool for acute myeloid leukemia.

  BackgroundAcute myeloid leukemia (AML) is characterized by uncontrolled proliferation of malignant hematopoietic cells in the bone marrow that are arrested in differentiation. In AML pathogenesis, hematopoietic progenitor cells acquire multiple genetic aberrations often occurring in the same set of genes that ultimately lead to malignant transformation. In the WHO classification 2016, six AML classes with different prognosis are identified by chromosomal translocations measured by standard cytogenetics. All balanced translocations produce fusion genes, except for t(3;3)/inv(3;3), which lead to overexpression of MECOM/EVI1 associated with poor prognosis. For accurate risk assessment of cytogenetically normal AML, four genes need to be screened to distinguish AML with mutated NPM1 in the absence of FLT3 mutations and AML with bi-allelic CEBPA mutations, which have a favorable prognosis, and AML with RUNX1 mutations which have a poor prognosis. Recently, a full genomic classification system has been proposed by Papaemmanuil et al. (NEJM 2016). In this system, the same six classes with chromosomal translocations are identified by standard cytogenetics as well as five additional classes characterized by genetic mutations in 14 different genes. AimThe aim of the study was to investigate whether whole transcriptome RNA-sequencing (RNAseq) can be used as single technology for classification of AML. MethodA panel of 100 AML were analyzed and a bio-informatics pipeline called HAMLET (Human AML Expedited Transcriptomics) was developed in which 4 modules are integrated to detect (1) fusion genes, (2) small variants in 13 recurrently mutated genes, (3) internal tandem duplications in FLT3 (FLT3-ITD) and partial tandem duplications in KMT2A (KMT2A-PTD) and (4) overexpression of MECOM/EVI1. All mutations that were called by HAMLET were validated by diagnostic data as available for all 100 AML or targeted PCR followed by Sanger or next generation sequencing on all positive AML and at least an equal number of negative cases. Results & discussionThe data showed that HAMLET accurately identified all genetic aberrations with high sensitivity and specificity. In addition, in 7 AML, fusion transcripts were detected that are not measured by standard cytogenetics including three cases that lack any class-defining lesion according to Papaemmanuil et al. Moreover, overexpression of MECOM/EVI1 was detected in two AML with inv(3) as well as in five cases without inv(3)/t(3;3), and a gene signature was measured to distinguish AML with CEBPA mutations with favorable prognosis. In conclusion, HAMLET provides a comprehensive and flexible pipeline for RNAseq analysis to retrieve all relevant information for current classification of AML as well as additional information that may improve classification in the future.

  Study EGAS00001003096

• Genetic Basis of Cryptorchidism

  Non-syndromic cryptorchidism is a common malformation, but the genetic loci that increase susceptibility to cryptorchidism remain unknown. A genome-wide association study (GWAS) was completed to determine whether common allelic variants are associated with susceptibility to cryptorchidism. Two sets of discovery groups were genotyped. Group 1 (559 cases and 1772 controls) was genotyped with HumanHap550v1.0, HumanHap550v3.0 or Human610-quad v1.0. Group 2 (353 cases and 1149 controls) was genotyped with HumanOmniExpress-12v1 or HumanOmniExpress-12v1-1. The sample and marker QCs were performed separately using PLINK (v1.07; http://pngu.mgh.harvard.edu/purcell/plink/) and logistic regression analyses were performed with first and second multidimensional scaling (MDS) components as covariates.

  Study phs000986

• Feasibility Study to Identify the Optimal Adjuvant Combination Scheme of Ipilimumab and Nivolumab (OpACIN) in resectable stage III melanoma patients

  OpACIN is a feasibility study including 20 patients with palpable stage III melanoma that were 1:1 randomized to receive ipilimumab 3mg/kg and nivolumab 1mg/kg, either 4 courses after surgery (adjuvant arm), or 2 courses prior to surgery and 2 courses post-surgery (neoadjuvant arm). WES and RNA-seq was performed on baseline biopsy material and correlated with outcome of the patients (relapse free survival). TCRseq from baseline tumor samples was used to analyse the top 100 tumor resident clones and analyse their change in PBMC comparing baseline versus week 6 of immunotherapy.

  Study EGAS00001003099

• Resuscitation Outcomes Consortium Trial of Continuous Compressions Versus Standard CPR in Patients With out-of-Hospital Cardiac Arrest (ROC CCC-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions.Objectives: To compare the rate of survival to hospital discharge after continuous chest compressions versus standard American Heart Association recommended cardiopulmonary resuscitation with interrupted chest compressions in patients with out-of-hospital cardiac arrest.Background: Standard cardiopulmonary resuscitation (CPR) consists of manual chest compressions to maintain blood flow and positive-pressure ventilation to maintain oxygenation until spontaneous circulation is restored. Chest compressions during standard CPR are interrupted frequently by ventilations. These interruptions reduce blood flow and potentially reduce the effectiveness of CPR. In animals with cardiac arrest, the interruption of chest compressions has been associated with decreased survival. Observational studies involving humans with out-of-hospital cardiac arrest of presumed cardiac cause have suggested that continuous compressions are associated with higher rates of survival than interrupted compressions.Participants: Of 35,904 patients with out-of-hospital cardiac arrest who were screened, 26,148 were eligible for participation in the trial and were enrolled in the trial during either the run-in phase or the active-enrollment phase. The active-enrollment phase included 23,711 patients, of whom 12,653 were assigned to the intervention group and 11,058 to the control group.Design: There were 114 participating EMS agencies across the eight participating Resuscitation Outcomes Consortium (ROC) sites, which were grouped into 47 clusters. Clusters of agencies were randomly assigned, in a 1:1 ratio, to perform continuous chest compressions or interrupted chest compressions during all the out-of-hospital cardiac arrests to which they responded. Each cluster was required to begin enrolling patients in a run-in phase to demonstrate adherence to the protocol before entering the active-enrollment phase of the trial. Twice per year, each cluster was crossed over to the other resuscitation strategy.Patients assigned to the group that received continuous chest compressions (intervention group) were to receive continuous chest compressions at a rate of 100 compressions per minute, with asynchronous positive-pressure ventilations delivered at a rate of 10 ventilations per minute. Patients assigned to the group that received interrupted chest compressions (control group) were to receive compressions that were interrupted for ventilations at a ratio of 30 compressions to two ventilations; ventilations were to be given with positive pressure during a pause in compressions of less than 5 seconds in duration. The primary outcome was the rate of survival to hospital discharge. Secondary outcomes included neurologic function at discharge, which was measured with the use of the modified Rankin scale, and adverse events. Conclusions: During the active-enrollment phase, 1129 of 12,613 patients in the continuous chest compression group and 1072 of 11,035 patients in the interrupted chest compressions group survived to hospital discharge.In patients with out-of-hospital cardiac arrest, continuous chest compressions during CPR performed by EMS providers did not result in significantly higher rates of survival or favorable neurologic function than did interrupted chest compressions.

  Study phs003901