Tumor Organoids from glioblastoma, 2 patients, treated with different small molecule inhibitors. Paired RNA-Seq was done on NovaSeq 6000 with the Illumina TruSeq stranded mRNA Kit. The small inhibitors GW2580, BLZ945 and PLX3397 were used. DMSO was used as control.
RNA was extracted from flow-sorted CD19+. RNA-Seq was performed on 32 samples of 30 patients (2 replicates per samples). RNA-Seq libraries were subjected to non-stranded paired-end (2 x 75 bp) sequencing on HiSeq 2500 (Illumina). The files are in FASTQ format.
ChIP-Seq data for 2 macrophage - T=6days B-glucan sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816
This data contains DNA methylation data obtained from the PBMCs obtained from type 2 diabetes adolescents and controls. There are 21 diabetic samples and 10 controls. This dataset also contains metabolic data obtained from the serum of 155 samples. There are 113 diabetic and 42 control samples.
Raw FastQ Files of 69 samples of endometrial tissue from uterus (rudiments) of patients diagnosed with MRKH Type 1/2 or healthy controls. Each sample consists of 2 lanes paired-end RNA sequencing data.
Single-cell RNA sequencing was performed on bone marrow mononuclear cells of 2 acute myeloid leukemia patients at refractory stage. The profiling was performed using 10x Genomics Chromium Single Cell 3ʹ Gene Expression platform. The raw data are available as fastq files.
RNA-Seq and ATAC-Seq of iPSC derived neurons under baseline and KCl stimulation conditions from 10 distinct donors, including 5 healthy controls and 5 schizophrenic individuals. scATAC of human post mortem prefrontal cortex from 4 adult individuals including 2 neurotypical individuals and 2 schizophrenic individuals.
In this dataset are the data from :- 17 patients studied by WGS- 49 patients studied by WES- 9 (/49) patients studied by RNASeq at 2 time points- the same 9 patients studied by ERRBS at 2 time points
Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.
The Cleveland Family Study is the largest family-based study of sleep apnea world-wide, consisting of 2284 individuals (46% African American) from 361 families studied on up to 4 occasions over a period of 16 years. The study was begun in 1990 with the initial aims of quantifying the familial aggregation of sleep apnea. NIH renewals provided expansion of the original cohort (including increased minority recruitment) and longitudinal follow-up, with the last exam occurring in February 2006. Index probands (n=275) were recruited from 3 area hospital sleep labs if they had a confirmed diagnosis of sleep apnea and at least 2 first-degree relatives available to be studied. In the first 5 years of the study, neighborhood control probands (n=87) with at least 2 living relatives available for study were selected at random from a list provided by the index family and also studied. All available first degree relatives and spouses of the case and control probands also were recruited. Second-degree relatives, including half-sibs, aunts, uncles and grandparents, were also included if they lived near the first degree relatives (cases or controls), or if the family had been found to have two or more relatives with sleep apnea. Blood was sampled and DNA isolated for participants seen in the last two exam cycles (n=1447). The sample, which is enriched with individuals with sleep apnea, also contains a high prevalence of individuals with sleep apnea-related traits, including: obesity, impaired glucose tolerance, and HTN. Phenotyping data have been collected over 4 exam cycles, each occurring ~every 4 years. The last three exams targeted all subjects who had been studied at earlier exams, as well as new minority families and family members of previously studied probands who had been unavailable at prior exams. Data from one, two, three and four visits are available for 412, 630, 329 and 67, participants, respectively. In the first 3 exams, participants underwent overnight in-home sleep studies, allowing determination of the number and duration of hypopneas and apneas, sleep period, heart rate, and oxygen saturation levels; anthropometry (weight, height, and waist, hip, and neck circumferences); resting blood pressure; spirometry; standardized questionnaire evaluation of symptoms, medications, sleep patterns, quality of life, daytime sleepiness measures and health history; venipuncture and measurement of total and HDL cholesterol. The 4th exam (2001-2006) was designed to collect more detailed measurements of sleep, metabolic and CVD phenotypes and included measurement of state-of-the-art polysomnography, with both collection of blood and measurement of blood pressure before and after sleep, and anthropometry, upper airway assessments, spirometry, exhaled nitric oxide, and ECG performed the morning after the sleep study. Data have been collected by trained research assistants or GCRC nurses following written Manuals of Procedures who were certified following standard approaches for each study procedure. Ongoing data quality, with assessment of within or between individual drift, has been monitored on an ongoing basis, using statistical techniques as well as regular re-certification procedures. Between and within scorer reliabilities for key sleep apnea indices have been excellent, with intra-class correlation coefficients (ICCs) exceeding 0.92 for the apnea-hypopnea index (AHI). Sleep staging, assessed with epoch specific comparisons, also demonstrate excellent reliability for stage identification (kappas>0.82). There has been no evidence of significant time trends-between or within scorers- for the AHI variables. We also have evaluated the night-to-night variability of the AHI and other sleep variables in 91 subjects, with each measurement made 1-3 months apart. There is high night to night consistency for the AHI (ICC: 0.80), the arousal index (0.76), and the % sleep time in slow-wave sleep (0.73). We have demonstrated the comparability of the apnea estimates (AHI) determined from limited channel studies obtained at in-home settings with in full in-laboratory polysomnography. In addition to our published validation study, we more recently compared the AHI in 169 Cleveland Family Study participants undergoing both assessments (in-home and in-laboratory) within one week apart. These showed excellent levels of agreement (ICC=0.83), demonstrating the feasibility of examining data from either in-home or in-laboratory studies for apnea phenotyping. Data collected in the GCRC were obtained, when possible, with comparable, if not identical techniques, as were the same measures collected at prior exams performed in the participants' homes. To address the comparability of data collected over different exams, we calculated the crude age-adjusted correlations ~3 year within individual correlations between measures made in the most recent GCRC exam with measures made in a prior exam and demonstrated excellent levels of agreement for BMI (r=.91); waist circumference (0.91); FVC (0.88); and FEV1 (0.86). As expected due to higher biological and measurement variability, 149 somewhat lower 3-year correlations were demonstrated for SBP (0.56); Diastolic BP (0.48); AHI (0.62); and nocturnal oxygen desaturation (0.60). NHLBI Candidate-gene Association Resource. The NHLBI initiated the Candidate gene Association Resource (CARe) to create a shared genotype/phenotype resource for analyses of the association of genotypes with phenotypes relevant to the mission of the NHLBI. The resource comprises nine cohort studies funded by the NHLBI: Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health Study (CHS), Cleveland Family Study (CFS), Coronary Artery Risk Development in Young Adults (CARDIA), Cooperative Study of Sickle Cell Disease (CSSCD), Framingham Heart Study (FHS), Jackson Heart Study (JHS), Multi-Ethnic Study of Atherosclerosis (MESA), and the Sleep Heart Health Study (SHHS). A database of genotype and phenotype data will be created that includes records for approximately 50,000 study participants with approximately 50,000 SNPs from more than 1,200 selected candidate genes. In addition, a genome wide association study using a 1,000K SNP Chip will be conducted on approximately 9,500 African American participants drawn from the 50,000 participants in the nine cohorts. Some relevant CARe publications CARe Study: PMID 20400780 CVD Chip Design: PMID 18974833
