22938 results for "ega"

in 30.99 milliseconds.

• LP2100098-DNA_D07

  LP2100098-DNA_D07

  Dataset EGAD00001009951

• LP2100098-DNA_F03

  LP2100098-DNA_F03

  Dataset EGAD00001009958

• LP2100098-DNA_F07

  LP2100098-DNA_F07

  Dataset EGAD00001009960

• LP2100082-DNA_G01

  LP2100082-DNA_G01

  Dataset EGAD00001009925

• LP2100098-DNA_E07

  LP2100098-DNA_E07

  Dataset EGAD00001009955

• LP2100082-DNA_A01

  LP2100082-DNA_A01

  Dataset EGAD00001009873

• LP2100030-DNA_A05

  LP2100030-DNA_A05

  Dataset EGAD00001009880

• LP2100082-DNA_B05

  LP2100082-DNA_B05

  Dataset EGAD00001009901

• LP2100082-DNA_B06

  LP2100082-DNA_B06

  Dataset EGAD00001009902

• LP2100082-DNA_G02

  LP2100082-DNA_G02

  Dataset EGAD00001009926

• LP2100082-DNA_G03

  LP2100082-DNA_G03

  Dataset EGAD00001009927

• LP2100082-DNA_H02

  LP2100082-DNA_H02

  Dataset EGAD00001009929

• LP2100098-DNA_C03

  LP2100098-DNA_C03

  Dataset EGAD00001009944

• LP2100098-DNA_H01

  LP2100098-DNA_H01

  Dataset EGAD00001009967

• LP2100100-DNA_A01

  LP2100100-DNA_A01

  Dataset EGAD00001009975

• LP2100100-DNA_C01

  LP2100100-DNA_C01

  Dataset EGAD00001009977

• LP2100100-DNA_C03

  LP2100100-DNA_C03

  Dataset EGAD00001009978

• LP2100098-DNA_C07

  LP2100098-DNA_C07

  Dataset EGAD00001009946

• LP2100098-DNA_D05

  LP2100098-DNA_D05

  Dataset EGAD00001009950

• LP2100098-DNA_E05

  LP2100098-DNA_E05

  Dataset EGAD00001009954

• LP2100030-DNA_A04

  LP2100030-DNA_A04

  Dataset EGAD00001009881

• LP2100082-DNA_A02

  LP2100082-DNA_A02

  Dataset EGAD00001009882

• LP2100082-DNA_F05

  LP2100082-DNA_F05

  Dataset EGAD00001009923

• LP2100082-DNA_F06

  LP2100082-DNA_F06

  Dataset EGAD00001009924

• LP2100098-DNA_H07

  LP2100098-DNA_H07

  Dataset EGAD00001009970

• LP2100098-DNA_H03

  LP2100098-DNA_H03

  Dataset EGAD00001009968

• LP2100098-DNA_H05

  LP2100098-DNA_H05

  Dataset EGAD00001009969

• LP2100098-DNA_D01

  LP2100098-DNA_D01

  Dataset EGAD00001009948

• LP2100082-DNA_C01

  LP2100082-DNA_C01

  Dataset EGAD00001009904

• LP2100082-DNA_C02

  LP2100082-DNA_C02

  Dataset EGAD00001009905

• LP2100082-DNA_C03

  LP2100082-DNA_C03

  Dataset EGAD00001009906

• LP2100082-DNA_C05

  LP2100082-DNA_C05

  Dataset EGAD00001009907

• LP2100082-DNA_D02

  LP2100082-DNA_D02

  Dataset EGAD00001009908

• LP2100082-DNA_D01

  LP2100082-DNA_D01

  Dataset EGAD00001009909

• LP2100082-DNA_D03

  LP2100082-DNA_D03

  Dataset EGAD00001009910

• LP2100082-DNA_E02

  LP2100082-DNA_E02

  Dataset EGAD00001009914

• LP2100082-DNA_E03

  LP2100082-DNA_E03

  Dataset EGAD00001009915

• LP2100082-DNA_E04

  LP2100082-DNA_E04

  Dataset EGAD00001009916

• LP2100082-DNA_E06

  LP2100082-DNA_E06

  Dataset EGAD00001009918

• LP2100082-DNA_F02

  LP2100082-DNA_F02

  Dataset EGAD00001009920

• LP2100082-DNA_F03

  LP2100082-DNA_F03

  Dataset EGAD00001009921

• LP2100082-DNA_H03

  LP2100082-DNA_H03

  Dataset EGAD00001009930

• LP2100098-DNA_C09

  LP2100098-DNA_C09

  Dataset EGAD00001009947

• LP2100098-DNA_D03

  LP2100098-DNA_D03

  Dataset EGAD00001009949

• LP2100082-DNA_H04

  LP2100082-DNA_H04

  Dataset EGAD00001009931

• LP2100082-DNA_H05

  LP2100082-DNA_H05

  Dataset EGAD00001009932

• LP2100098-DNA_A01

  LP2100098-DNA_A01

  Dataset EGAD00001009933

• LP2100098-DNA_A03

  LP2100098-DNA_A03

  Dataset EGAD00001009934

• LP2100098-DNA_A07

  LP2100098-DNA_A07

  Dataset EGAD00001009936

• LP2100098-DNA_A09

  LP2100098-DNA_A09

  Dataset EGAD00001009937

• LP2100098-DNA_B01

  LP2100098-DNA_B01

  Dataset EGAD00001009938

• LP2100098-DNA_B03

  LP2100098-DNA_B03

  Dataset EGAD00001009939

• LP2100098-DNA_B05

  LP2100098-DNA_B05

  Dataset EGAD00001009940

• LP2100098-DNA_B07

  LP2100098-DNA_B07

  Dataset EGAD00001009941

• LP2100098-DNA_B09

  LP2100098-DNA_B09

  Dataset EGAD00001009942

• LP2100098-DNA_C01

  LP2100098-DNA_C01

  Dataset EGAD00001009943

• HELIUS DAC The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dac EGAC00001000895

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• Berlin Institute for Medical Systems Biology - ccfDNA Methylation Data Access Commitee

  Dac EGAC00001003283

• A MITF germline mutation predisposes to melanoma and renal cell carcinoma

  Study EGAS00000000048

• Naive B-cell receptor heavy chain repertoire of celiac patients and healthy controls

  Study EGAS00001003337

• Analysis of the genomic landscape of chemoresistant multiple myeloma

  Study EGAS00001003709

• Genetic modification of primary human B cells to model high-grade lymphoma

  Study EGAS00001003560

• Molecular Profiles of BRCA1-Associated Ovarian Cancer Treated by Platinum-Based Therapy

  Study EGAS00001003980

• The data access committee for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

  Dac EGAC00001003279

• A body map of somatic mutagenesis in morphologically normal human tissues (WES)

  Study EGAS00001005459

• Data Access Policy of the HCEMM - Semmelweis University, Molecular Oncohematology Research Group

  Dac EGAC00001003240

• Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

  Dac EGAC00001003284

• dic(9;20) pediatric ALL with DNMT3B rearrangement study data access committee

  Dac EGAC00001003324

• The University of Hong Kong Gastric Cancer Exome Sequencing Project Data Access Committee

  Dac EGAC00001000032

• Cancer genomics project, the University of Tokyo Data Access Committee

  Dac EGAC00001000087

• GEH Data Access Committee

  Dac EGAC00001000139

• BGI Data Access Committee

  Dac EGAC00001000177

• TwinsUK Resource Executive Committee (TREC)

  Dac EGAC00001000274

• DAC for access to anonymised study data for UK and Norwegian AAD families

  Dac EGAC00001000333

• DAC for the study of response to EGFR Blockade in Colorectal Cancer

  Dac EGAC00001000360

• VIB Department of Molecular Genetics DAC AMG and NG

  Dac EGAC00001000395

• Data access committee of the Prostate Cancer Research Center, University of Tampere, Finland.

  Dac EGAC00001000423

• DAC for Dependency of Transcriptional circuit of glioblastoma-associated macrophages that drive mesenchymal differentiation

  Dac EGAC00001000441

• Wellcome Trust Sanger Institute Data Sharing Policy for Trachoma GWAS

  Dac EGAC00001000489

• The Chinese University of Hong Kong Hereditary Spastic Paraplegia Data Access Committee

  Dac EGAC00001000493

• DAC Genomic and transcriptomic profiling of signalling networks in follicular lymphoma

  Dac EGAC00001000577

• Agreement for accessing data of NGS based ctDNA tests

  Dac EGAC00001000598

• Data Access Committee for the Centre National de Recherche en Génomique Humaine (CNRGH)

  Dac EGAC00001000723

• RNA Seq fastq files generated for the "Proteogenomic landscape of medulloblastoma subgroups"

  Dac EGAC00001000792

• UT MD Anderson Cancer Center Department of Genomic Medicine Lipoasarcoma Data Access Committee

  Dac EGAC00001000821

• Peking University "Gene & Cognition" Research Group - Data Access Committee

  Dac EGAC00001000856

• Using iPSC-derived neurons to explore cellular phenotypes associated with schizophrenia Data Access Committee.

  Dac EGAC00001000870

• DAC for the Canadian Biobank on Respiratory and Allergic diseases (CoBRA)

  Dac EGAC00001000901

• The evolutionary history of human colitis-associated colorectal cancer

  Dac EGAC00001000917

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

  Dac EGAC00001000956

• DAC for study: Frequent mutation of the FOXA1 untranslated region in prostate cancer

  Dac EGAC00001000962

• DAC for project: Subcutaneous panniculitis-like T-cell lymphomas (SPTCL) with hemophagocytic lymphohistiocytic syndrome.

  Dac EGAC00001000992

• The molecular landscape of glioma in patients with Neurofibromatosis 1

  Dac EGAC00001001004

• Age-related remodeling of apparently normal esophageal epithelia by common cancer drivers

  Dac EGAC00001001067

• DAC for study: Evaluation of commercial Guardant360 ctDNA test in metastatic prostate cancer

  Dac EGAC00001001082

• snRNA-seq in white matter post-mortem tissue from MS and controls - DAC

  Dac EGAC00001001105

• SHANK2 mutations associated with Autism Spectrum Disorder cause hyperconnectivity of human neurons comittee

  Dac EGAC00001001117

• Germline TET2 and DNMT3A committee

  Dac EGAC00001001130

• Department of Hematology and Oncology, Nagoya City University Graduate School of Medical Sciences

  Dac EGAC00001001166