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• CReATe Fertility Centre DAC

  DAC for the management of dataset own by CReATe Fertility Centre.

  Dac EGAC50000000646

• Human glioblastoma single cell DAC (Linnarsson)

  DAC for human glioblastoma single cell sequencing samples

  Dac EGAC50000000575

• Human developing meninges single cell DAC (Linnarsson)

  DAC for human developing meninges single cell sequencing

  Dac EGAC50000000576

• Asan Medical Center Data Access Committee

  Asan Medical Center Data Access Committee for BRISK study.

  Dac EGAC50000000733

• Hepatocellular Carcinoma Spatial transcriptomics Data Access Committee

  This is the DAC for study ST HCC

  Dac EGAC50000000635

• Pan Prostate Cancer Group Data Access Control Committee

  DACC for the PanProstate Cancer Group projects

  Dac EGAC50000000602

• GenomeWideData_for_Present_Day_Peruvian_individuals_living_in_urban_areas

  Genome-wide data for 432 Admixed individuals from Peru

  Dataset EGAD00010002795

• DAC SysMed

  Data access control for datasets submitted by SysMed

  Dac EGAC50000000089

• MOSAIC Window DAC

  Data Access Committee for the MOSAIC Window initiative.

  Dac EGAC50000000398

• DAC for Genetic and Microenvironmental Analysis of PTCL at Department of Hematology, University of Tsukuba

  Dac EGAC50000000600

• Genome sequencing in monozygotic twins discordant for Mayer-Rokitansky-Küster-Hauser syndrome

  Study EGAS00001006371

• Exome reads

  Exome sequencing for 2 infertile brothers

  Dataset EGAD00001003193

• Exome data for PDXs

  Exome data of PDX models.

  Dataset EGAD00001001863

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• RNA-Sequencing of cervical cancers

  Question: Can we identify cervical cancer patients who are at risk for distant metastatic (DM) recurrence following treatment with radiotherapy, concurrent weekly cisplatin and brachytherapy (RTCT). Findings: An immune-based 55 gene risk score was developed using a cohort of 81 patients treated with RTCT that was strongly predictive of DM and cause-specific survival (CSS). The risk score was validated in two independent patient cohorts. A high immune metastatic risk score was associated with a high tumor mutational burden and a ‘cold’, immune-excluded tumor microenvironment at diagnosis. Meaning: The immune gene expression risk score may help to identify patients at risk of DM and potential targets for mitigating this risk.

  Study EGAS50000000087

• Epigenomics Studies in Acute Myeloid Leukemia (AML)

  Acute Myeloid Leukemia (AML) remains a clinical challenge since most patients diagnosed with AML will die from the disease. Some patients harbor treatment-refractory disease and many others relapse with disease that in many cases is resistant to treatments. Our study was designed to understand the molecular basis of disease progression in AML through assessing genomics signatures in patient specimens collected through an international collaboration which assembled samples from 138 AML patients which experienced disease relapse and normal hematopoietic cells (n=15). It is hoped that this resource will help researchers understand mechanisms of disease relapse in AML and contribute to the general pool of data available for analyses for this disease and general research use.

  Study phs001027

• Selenium Chemoprevention: Benefits and Harms

  Selenium is a trace element that may have anti-carcinogenetic effect, but heterogenous treatment effects have been observed. This genome-wide association study investigates if genetic variation modifies the effect of selenium chemoprevention in the Selenium and Celecoxib Trial, a randomized, double-blind, placebo-controlled trial conducted at the University of Arizona Cancer Center. The objective of the clinical trial was to determine whether daily intake of 200 µg/d of selenium as a supplement could reduce the risk for developing metachronous colorectal adenomas. The study participants were recruited between 2001 and 2011. Genome-wide association study was performed for 1,323 study participants who provided blood sample at the time of study entry.

  Study phs002283

• Enhancer Mapping in Chronic Lymphocytic Leukemia

  In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19+/CD5+) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.

  Study phs001704

• The Genomic Analysis of Medulloblastoma

  Medulloblastoma is a heterogenous disease made up of at least four distinct subtypes of disease which appear to exploit and disrupt naturally occurring developmental pathways of cellular growth and hindbrain development. To better understand the driver mutations of this disease, we performed whole genome sequencing of 37 medulloblastomas and the corresponding normal DNA of the 37 affected children treated at St. Jude Children's Research Hospital. We have found several novel mutations which appear subtype specific. These mutations were checked for frequency in a separate tumor cohort of 56 children with medulloblastoma, also treated on the St. Jude Medulloblastoma 2003 trial, and were tested in several animal models of medulloblastoma for proof of oncogenic potential.

  Study phs000409

• Genomic Psychiatry Cohort (GPC) Whole Genome Sequencing and Genotyping Study

  The Center for Genomic Psychiatry at the University of Southern California (USC), SUNY Downstate and an extensive network of academic medical centers have created the Genomic Psychiatry Cohort (GPC). The GPC consists of a large clinical cohort of patients with schizophrenia, bipolar disorder, and healthy controls. The data generation of genotyping and whole genome sequencing of the GPC has been done in collaboration with the USC and the Broad Institute of MIT and Harvard. Genome-wide association studies are being performed with the samples to identify genetic risks for psychiatric disease. Genotyping is also being used to determine ancestry informative markers and replication of candidate genes. Whole genome sequencing identifies functional variants associated with disease risk.

  Study phs001020

• Genomic Landscape of High-Grade Neuroendocrine Neoplasms

  This project is a multi-omics study of high grade (grade 3) neuroendocrine neoplasms across diverse organs. Genomic analysis using low-pass whole genome sequencing for copy number analysis, whole exome sequencing and whole transcriptomic sequencing using RNAseq were performed. As high grade neuroendocrine neoplasms are highly lethal with a median survival of approximately 7 months, new insights are urgently needed for better prognosis, diagnosis and management. This study focuses on gene and pathway discoveries in this cancer, and includes samples from a diverse set of organs, including the lungs, gastroenteropancreatic tract, breast, prostate, head and neck and bladder. This study was made possible by a generous donation from The Helen B. Rodde Fund.

  Study phs002070

• CADD/GADD centers on Antisocial Drug Dependence

  CADD (Center for Antisocial Drug Dependence): Funded through NIDA 011015 to study genetic influences on, and treatment of, antisocial drug dependence, studying both clinical probands and their families, and community samples of matched controls, twins, and participants in an ongoing longitudinal adoption study. A collaboration between three organizations at two campuses of the University of Colorado. Longitudinal with three waves of data collection completed. GADD (Genetics of Adolescent Antisocial Drug Dependence): Funded originally through NIDA 012845, s multisite collaboration including adolescent subjects at high-risk for antisocial drug dependence and their siblings, recruited in Denver, CO and San Diego, CA. Longitudinal with two waves of data collection completed, one in progress as of May, 2018.

  Study phs001841

• Sequence Analysis of Mutations and Translocations Across Breast Cancer Subtypes

  This study combined whole exome (103 samples) and whole genome (22 samples) sequencing over a total of 108 breast tumors and matched normal DNA to identify novel mutations and translocations. Samples were subjected to paired-end Illumina sequencing with goal of 30x coverage of tumor/normal for whole genomes and 100x tumor/normal coverage for whole exomes. From these sequences, we used various computational techniques to identify somatic point mutations, insertion/deletions and structural rearrangements in these tumors. From these data, we identified new insights into the rates of background mutations in these cancers, novel recurrent mutated genes, and multiple gene rearrangements. One of these rearrangements appears to be a recurrent event in breast cancer.

  Study phs000369

• Epilepsy Genetics Initiative

  The Epilepsy Genetics Initiative (EGI) was created in 2014 with the goal of amassing a database of clinically-generated exome sequence data for patients with epilepsy who have had diagnostic whole exome sequencing. Most of the subjects enrolled in the study did not receive a secure genetic diagnosis from their initial sequencing. Following participant/family consent, EGI transfers raw data from the respective clinical lab to the Institute for Genomic Medicine at Columbia University Medical Center. The strategy of the initiative has been to carry out systematic reanalysis of data to identify new diagnoses that were not possible or missed at the time of initial sequencing and to aid in novel gene discovery in epilepsy.

  Study phs001551

• Improved Detection and Identification of Microsatellite Instability Features in Colorectal Cancer

  In this study, we analyzed a series of colorectal cancers using a new sequencing technology that sequences more than 200 microsatellite regions in parallel. With this technology, we evaluated mono-, di- , tri- and tetranucleotide repeats to provide a broad spectrum of microsatellite instability (MSI) events. This deep sequencing technology provides greater than 2000X coverage and eliminates the need for PCR amplification. Because every read corresponds to a single DNA molecule, we eliminated artifacts introduced by PCR that obscured true MSI events. Also included in our analysis is a new method for detection of somatic copy number changes in 83 cancer genes, with which we can infer chromosomal instability (CIN) status.

  Study phs001914