23054 results for "ega"

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• Methylproteome

  TMT-labelled, SCX fractionation, global protein methylation profiled

  Dataset EGAD00010002541

• DAC_MATCH-R molecular driver

  Data access committee for MATCH-R molecular driver study

  Dac EGAC50000000335

• human HYPOMAP

  Data access for HYPOMAP raw sequence data and result data objects

  Dac EGAC50000000410

• This DAC will review all requests for data related to the dataset: EGAD00001015613.

  Dac EGAC00001003562

• CITE-seq for peripheral blood samples of 5 breast cancer patients

  Study EGAS00001006241

• Multi-omics analysis of an individual with multiple pancreatic neuroendocrine tumours (panNETs)

  Study EGAS00001006722

• Institut Curie Data Access Committee

  Data Access Committee to access U932 Transim NGS data

  Dac EGAC50000000205

• The Multiomics Blueprint of Extreme Human Lifespan

  Dac EGAC50000000515

• DAC for DNA methylation data (iMED, BCG prime, and 500FG)

  Dac EGAC00001003534

• Huch Lab, Max Planck Institute of Molecular Cell Biology and Genetics

  Dac EGAC50000000112

• human biopsies

  DAC for human samples derived from biopsies from patients with hydrocephaly.

  Dac EGAC50000000625

• Validation study of genome-wide polygenic score for body mass index in South Asians

  Dac EGAC00001003593

• Whole genome sequencing of multifocal small intestinal neuroendocrine tumors

  Study EGAS00001006294

• Shallow sequencing of organoid/xenograft or human colorectal metastases

  Study EGAS00001006696

• X chromosome dosage and the genetic impact across human tissues

  Study EGAS00001006996

• Single-nucleus transcriptomic profiling of aging Down Syndrome brains

  Dac EGAC00001002370

• CoV2 challenge data

  Repository containing the transcriptomic data for the CoV2 challenge study

  Dac EGAC50000000391

• H3N2 challenge data

  Repository containing the transcriptomic data for the H3N3 challenge study

  Dac EGAC50000000379

• anonymized_capTCRseq

  TCR-beta sequences, frequencies, and VDJ usage.

  Dataset EGAD00001008791

• RNA sequencing of chondrosarcoma

  To profile chondrosarcoma patients.

  Study EGAS00001004585

• CGP_CORE_CELL_LINES___RNA_seq

  Graham to complete

  Study EGAS00001000828

• Genome-wide somatic variants in CRC (GRCh38)

  Study EGAS00001004710

• Exome sequencing

  Exome sequence Pfizer & TwinsUK

  Study EGAS00001001194

• IBD_Whole_Genome_Sequencing_Phase_1

  IBD Whole Genome Sequencing Phase 1

  Study EGAS00001001735

• Acne meta-analysis

  Acne meta-analysis

  Dataset EGAD00001008342

• Comprehensive molecular profiling of ETMR

  ETMR RIPSeq

  Dataset EGAD00001004802

• anonymized_FoundationOne

  Recurrently altered genes based on FoundationOne sequencing.

  Dataset EGAD00001008790

• Schwannomatosis WES data

  Exome sequencing of sporadic schwannomatosis patients

  Dataset EGAD00001000963

• Exome

  Exome from EGAS00001002441

  Dataset EGAD00001003325

• Exome

  Exome Seq for EGAS00001001845

  Dataset EGAD00001002160

• Exome

  Exome from EGA00001001848

  Dataset EGAD00001002164

• whole exome seq

  whole exome sequencing data

  Dataset EGAD00001004464

• CCA targeted sequencing

  CCA targeted sequencing

  Dataset EGAD00001001994

• dataset_CML_WES_VCF

  dataset CML WES VCF

  Dataset EGAD00001002054

• Analysis scripts

  Analysis scripts and output

  Dataset EGAD00001003282

• RNA-seq

  RNA-seq data

  Dataset EGAD00001011376

• WES

  WES

  Dataset EGAD00001008994

• anonymized_GLIPH2

  TCR-beta specificity motifs based on GLIPH2.

  Dataset EGAD00001008794

• Whole-genome sequencing

  Whole-genome sequencing data

  Dataset EGAD00001011377

• 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

• Genomic landscape of childhood acute lymphoblastic leukemia

  Abstract in preparation

  Study EGAS00001001317

• Epitope-linked Ig-seq of self-reactive plasma cells in celiac disease

  Study EGAS00001003658

• Genentech DevSci DAC

  Dac EGAC00001002700

• High-grade serous ovarian carcinoma organoids as models of chromosomal instability Data Access Committee

  Dac EGAC00001003204

• TRACERx Non-small cell lung cancer

  Dac EGAC00001000632

• Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud university medical center

  Dac EGAC00001003193

• Barts Cancer Institute - Follicular Lymphoma Whole Genome Sequencing Project Data Access Committee

  Dac EGAC00001000086

• The University of Hong Kong Gastric Cancer Genomics Study Data Access Committee

  Dac EGAC00001000136

• Data Access Committee: Whole Genome Sequencing of 18 sequential samples from 3 CLL patients

  Dac EGAC00001000221

• Department of Pathology and Tumor Biology, Kyoto University Data Access Committee

  Dac EGAC00001000273

• Oxford Human Islet Data Access Committee

  Dac EGAC00001000335

• ICR_pHGG_Data_Access_Committee

  Dac EGAC00001000384

• DAC for genomic data obtained within the Micrometastasis (Oslo1) project from Oslo, Norway

  Dac EGAC00001000558

• POPS Pregnancy Outcome Prediction Study

  Dac EGAC00001000582

• small RNA-Seq and RNA-Seq of cortical tubers from sufferers of tuberous sclerosis complex

  Dac EGAC00001000672

• DAC for Transcriptome analysis in very preterm infants with chronic lung disease after birth

  Dac EGAC00001000698

• RNA sequencing data of 66 matched primary and recurrent high grade serous ovarian cancer

  Dac EGAC00001000741

• DAC: Brain transcriptome of hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D)

  Dac EGAC00001000771

• Myeloma Research Laboratory, University of Adelaide and South Australian Health and Medical Research Institute

  Dac EGAC00001000849

• Division of Cancer Genomics, National Cancer Center Research Institute Data Access Committee

  Dac EGAC00001000957

• The University of Hong Kong Gastric Cancer Organoids Genomics Study Data Access Committee

  Dac EGAC00001000981

• DAC for Cardiac Translatomes of 80 Human Samples (65 DCM cases 15 controls)

  Dac EGAC00001001040

• The Scientific ethical comittee capital region of Denmark (De videnskabs etiske komiteer region hovedstaden)

  Dac EGAC00001001063

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

  Dac EGAC00001001072

• Expanding the neurodevelopmental phenotypes of individuals with de novo KMT2A variants Data Access Committee

  Dac EGAC00001001147

• AREP(Association pour la Recherche et l’Enseignement en Pathologie) Data Access Commitee

  Dac EGAC00001001196

• Data access committee for RNA-seq as a tool for evaluating human embryo competence

  Dac EGAC00001001215

• Control iPSC lines with clinically annotated genetic variants for versatile multi-lineage differentiation Committee

  Dac EGAC00001001226

• Data Access Commitee Polymorphisms in the Mitochondrial Genome associated with Bullous Pemphigoid in Germans

  Dac EGAC00001001341

• DAC: The genomic landscape of primary cutaneous anaplastic large cell lymphoma (pcALCL)

  Dac EGAC00001001607

• Human T-bet governs innate and innate-like adaptive IFN-g immunity against mycobacteria

  Dac EGAC00001001822

• ChIP-seq of plasma cell-free nucleosomes identifies cell-of-origin gene expression programs

  Dac EGAC00001001875

• International Genetics of Parkinson Disease Progression (IGPP) Consortium GWSS Summary Results Data Access Committee

  Dac EGAC00001002012

• Genomic Analysis of Focal Nodular Hyperplasia with Associated Hepatocellular Carcinoma Unveils its Malignant Potential

  Dac EGAC00001002123

• Evolutionary predictability of genetic versus non genetic resistance to anticancer drugs in melanoma

  Dac EGAC00001002126

• Genomics to select patients with metastatic breast cancer for targeted therapy DAC

  Dac EGAC00001002293

• Single-cell proteo-genomic reference map DAC

  Dac EGAC00001002343

• Targeted massively parallel sequencing of candidate regions on chromosome 22q predisposing to multiple schwannomas

  Dac EGAC00001002363

• Data Access Committee for the project: Epic arrays from human OB and their mimics

  Dac EGAC00001002482

• The Data Access Committee for Human Olfactory Mucosa Cells (DAC_HOM) at UEF

  Dac EGAC00001002527

• The Chinese University of Hong Kong (CUHK) Circulating Nucleic Acids Research Group (CNARG)

  Dac EGAC00001002733

• Somatic mutations predict an aggressive phenotype in meningioma but do not drive grade progression

  Dac EGAC00001002858

• Data Access Committee of the Medical Faculty of the University of Basel (MF-DAC)

  Dac EGAC00001002981

• The EMC-NICHE DAC considers appeals for hematopoietic/niche-related data sets.

  Dac EGAC00001001897

• EGAD00000000028

  Aggregate results from a GWAS study on 3352 cases abd 3145 controls

  Dataset EGAD00000000028

• EGAD00000000029

  Aggregate results from a case-control study on stroke and ischemic stroke.

  Dataset EGAD00000000029

• EGAD00000000074

  Gabriel samples from the Swedish BAMSE Cohort

  Dataset EGAD00000000074

• EGAD00000000075

  Gabriel samples from the Swedish BAMSE Cohort

  Dataset EGAD00000000075

• EGAD00000000076

  Gabriel samples from the Australian Bussleton Cohort

  Dataset EGAD00000000076

• EGAD00000000077

  Gabriel samples from the Australian Bussleton Cohort

  Dataset EGAD00000000077

• EGAD00000000082

  Gabriel samples from the French EGEA Cohort

  Dataset EGAD00000000082

• EGAD00000000083

  Gabriel samples from the French EGEA Cohort

  Dataset EGAD00000000083

• EGAD00000000086

  Gabriel samples from the multicenter GAIN cohort

  Dataset EGAD00000000086

• EGAD00000000087

  Gabriel samples from the multicenter GAIN cohort

  Dataset EGAD00000000087

• EGAD00000000088

  Gabriel samples from the Karelia Allergy Study

  Dataset EGAD00000000088

• EGAD00000000089

  Gabriel samples from the Karelia Allergy Study

  Dataset EGAD00000000089

• EGAD00000000090

  Gabriel samples from the Russian KMSU cohort

  Dataset EGAD00000000090

• EGAD00000000091

  Gabriel samples from the Russian KMSU cohort

  Dataset EGAD00000000091

• EGAD00000000092

  Gabriel samples from the German MAGIS cohort

  Dataset EGAD00000000092

• EGAD00000000093

  Gabriel samples from the German MAGIS cohort

  Dataset EGAD00000000093