22948 results for "ega"

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• TSACP TruSeq Amplicon Panel dataset

  TSACP TruSeq Amplicon Panel dataset for the TraIT cell line use case

  Dataset EGAD00001002109

• Reverse Capture Hi-C

  Reverse Capture Hi-C

  Dataset EGAD00001002657

• Human small intestinal macrophages whole transcriptome

  RNA sequencing data of human small intestinal macrophage subtypes

  Dataset EGAD00001002744

• Melanoma post mortem analysis

  These are 21 metastatic melanoma exomes matched with 7 germlines from 7 multisite metastatic melanoma cases.

  Dataset EGAD00001005421

• GATCI whole genome sequencing fastqs

  Raw whole genome sequencing data (fastq) for the GATCI project

  Dataset EGAD00001005926

• SPECTA Lung cancer VCF files

  SPECTA Lung cancer VCF files

  Dataset EGAD00001006260

• Purple copy number segments for EGAS00001004572

  Bulk copy number segments from Purple analysis in EGAS00001004572

  Dataset EGAD00001006908

• HipSci - Congenital Hyperinsulinia - Exome Sequencing - July 2017

  HipSci - Congenital Hyperinsulinia - Exome Sequencing - July 2017

  Dataset EGAD00001003520

• Everything you need to know about Federated EGA Affiliates: Q&A

  Addressing common questions about Federated EGA (FEGA) Affiliates and providing answers. FEGA Affiliates have recently been defined as the third tier within the newly approved “Federated EGA Structure and Organisation” document, alongside Central EGA (CEGA) and the FEGA Nodes. In this blog post, we aim to answer the questions that may arise on this topic. For more general information about FEGA Affiliates, you can refer to this blog post. Let's now start with the questions and answers! What is the difference between a FEGA Affiliate and a FEGA Node? FEGA Nodes A FEGA Node is a national-level service that accepts submissions and provides archiving and access services within a single country's jurisdiction. Nodes provide end-to-end services for submitters in that jurisdiction, including submission support, processing, permissions management, and distribution. FEGA Affiliates A FEGA Affiliate is not a national submission service. Instead, it archives data from its own legal entity and/or selected partner institutions (for example, consortium partners). Affiliates are designed for organisations and consortia that need to keep data stewardship and access decisions within their own governance model, while still enabling discovery through the broader FEGA ecosystem. What do Affiliates commit to? Here is a practical checklist of the core expectations for a FEGA Affiliate. FEGA Affiliate checklist Discovery and metadata Submit non-personal metadata to CEGA to obtain EGA accessions and enable discovery (with metadata validation handled by the CEGA Helpdesk). Operations and resilience Maintain a data storage system and a defined backup/recovery approach. Ensure operational capacity (personnel and infrastructure) to deliver Affiliate services. Maintain systems and practices that support local data security and align with EGA best-practices. Access and user support Provide data distribution or access (e.g in a trusted research environment) to authorised users to datasets hosted at the Affiliate. Provide permissions management for datasets archived by the Affiliate (the CEGA DAC (Data Access Committee) Portal is always at your disposal to use). Provide a minimal helpdesk with sufficient capacity to support relevant DACs and approved users. Membership term Commit to a minimum membership term of four years. Governance and participation (lightweight by design) Affiliates integrate into the FEGA network operationally, but without the committee obligations associated with Nodes. When is becoming an Affiliate the right fit? Consider the Affiliate tier if: You host (or plan to host) controlled-access datasets that must be managed under your organisation’s or consortium’s governance, you want studies to be discoverable through EGA, but need access control and/or distribution to remain local, and you are not aiming to operate a national submission service tier.. What if a FEGA Node already exists in my jurisdiction? We encourage any potential Affiliate to reach out to their national FEGA Node to explore collaboration at the national level. Why? Federated models within a country already exist in FEGA, where a national Node operates like a Central EGA for their country. FEGA Nodes can also represent the interests of any Affiliates within their jurisdiction at the FEGA Committees. FEGA Nodes have experience and can support you on your FEGA journey. Ready to join as a FEGA Affiliate? We’re excited to open this formally defined pathway for organisations and consortia to participate in the federation. If you think the FEGA Affiliate tier fits your needs, start by assessing: Your scope (organisation/consortium-held data rather than public external submissions), Your ability to provide the core services (permissions, distribution/access, helpdesk, monitoring), and Your readiness to submit non-personal metadata to CEGA for accessioning and discovery. To express interest, please contact us here, and we'll help you understand the onboarding steps and how Affiliates integrate into the FEGA network.

  Blog fega-affiliates-q&a

• Best Practices for DACs

  Best Practices Data access committees (DACs) are institutional safeguards responsible for ensuring a balance between data protection and accessibility. However, there are no procedural standards that apply across DACs, which can lead to inconsistencies in their reviews and compromise their quality and effectiveness. Standardising DAC processes can foster trust and mutual recognition, paving the way for greater coordination, collaboration, and delegation between DACs and other oversight bodies to improve the efficiency of data access without sacrificing protection. To provide guidance, the following are suggestions for actions a DAC might take upon receiving a data access request: Aim to respond to all initial requests in less than 2 weeks. Failure to do so may result in follow-up emails from the EGA, journals, and ultimately dataset withdrawal. First, check that the data/EGAD number is consistent with your data submission. In other words, ensure that the requester has contacted the correct DAC. Verify that the user will be using the data within the terms of consent by asking them to sign up to the terms within the DAA. Ensure that data users who are granted access to the data comply with the terms of a Data Access Agreement (DAA) and to use the data only in approved ways. Look for an institutional email address for the requester. Search for evidence that the requester is "appropriately qualified/bona fide" for using the data, for example on PubMed, Research Gate, LinkedIn, etc. Confirm that the affiliated organisation is real and that the requester still works there. Inquire with the requester about who should have accounts created at the EGA under the terms of the agreement. If a negative decision is made, promptly communicate it to the requester and support it with the terms of the Data Access Policy that the requester has not met or cannot meet. Keep the information in the DAC up to date. If you leave your institution and are unable to manage data access requests on their behalf, you should add your replacement as a new contact in the DAC. Assist with data access requests for any further questions related to your data. The EGA can only check information that has been deposited in the repository. If the user has a specific question that the EGA cannot answer, we will redirect the user to the DAC. All the EGA studies and datasets referenced in a publication under your DAC must be publicly searchable on the EGA website before the paper is released. If possible, describe the permitted purposes for subsequent research projects, including associated limits and conditions, for all resources hosted in a repository using a common ontology, such as the GA4GH Data Use Ontology (DUO). It is considered best practice to try and release data only to those with an institutional email address. This gives reassurance to the EGA, research participants, and the general public that an appropriate individual is accessing and using the data. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security.

  Documentation access/data-access-committee/best-practices

• DAC for study: "Combination pembrolizumab and radiotherapy induces systemic anti-tumor immune responses in immunologically-cold non-small cell lung cancer."

  This is Data Access Committee will oversee data sharing for sequence data in the EGA study: "Combination pembrolizumab and radiotherapy induces systemic anti-tumor immune responses in immunologically-cold non-small cell lung cancer." This dataset includes 116 bam files from whole exome sequencing on the Illumina HiSeq2500, 102 fastq files from RNA sequencing on the Illumina NovaSeq 6000. The samples analyzed include tumor/normal DNA samples and tumor RNA samples from 72 individuals with non-small cell lung cancer treated with pembrolizumab and SBRT or pembrolizumab alone on the NCT02492568 trial.

  Dac EGAC50000000202

• atrial appendage miRNA-seq in non-, paroxysmal and persistent atrial fibrillation

  64 left atrial appendages from patients without atrial fibrillation (AF) undergoing cardiac surgery, patients with paroxysmal AF and with persistent AF (~20 per group). Trizol RNA isolation, size selection, single end miRNA sequencing on 4 lanes of illumina NextSeq 500. Provided are FastQ files, one per lane per sample. Additional data (e.g. clinical characteristics, RIN values etc.) can be provided upon request. The same RNA samples (62) were used for transcriptome sequencing. Results from transcriptome sequencing are stored in the EGA database and are managed by the same DAC.

  Dataset EGAD00001007694

• Single cell sequencing of human normal breast myoepithelial cells

  The dataset includes 6 FASTQ files with single cell transcriptome sequencing data of normal breast myoepithelial cells from ducts and TDLUs derived from reduction mammoplasties from three patients. Chromium Single Cell 3’ Reagent Kit v2 or v3 (10x Genomics) were used for processing of cells, whereafter sequencing was performed using the Illumina® NextSeq500/550 High Output Kit v2. Cell Ranger was used for generating FASTQ files and files from different lanes were concatenated prior to uploading the data to EGA.

  Dataset EGAD00001008468

• Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment

  Study EGAS00001003886

• Data Access Committee for the Nichols Group at LHSC

  Dac EGAC00001002164

• EGAD00000000011

  WTCCC1 project Autoimmune Thyroid Disease (ATD) samples

  Dataset EGAD00000000011

• EGAD00000000025

  WTCCC2 project Ulcerative Colitis (UC) samples

  Dataset EGAD00000000025

• EGAD00010000596

  PCGP Ph-likeALL GEA

  Dataset EGAD00010000596

• EGAD00010000598

  PCGP Ph-likeALL SNP6

  Dataset EGAD00010000598

• EGAD00010000610

  Samples from the Greek island of Crete, MANOLIS cohort

  Dataset EGAD00010000610

• EGAD00010000264

  WTCCC2 project samples from Ischaemic Stroke Cohort

  Dataset EGAD00010000264

• Pancreatic adenocarcinoma QCMG 20110901

  Pancreatic adenocarcinoma QCMG 20110901

  Dataset EGAD00001000049

• Genetic variation in Kuusamo

  Genetic variation in Kuusamo

  Dataset EGAD00001000055

• DATA FILES FOR NBL

  Neuroblastoma whole genome sequencing

  Dataset EGAD00001000135

• Matched Pair Cancer Cell line Whole Genomes

  Matched Pair Cancer Cell line Whole Genomes

  Dataset EGAD00001000145

• UK10K_NEURO_FSZNK REL-2012-01-13

  UK10K_NEURO_FSZNK REL-2012-01-13

  Dataset EGAD00001000183

• BRAF and MEK resistant cell line clones

  BRAF and MEK resistant cell line clones

  Dataset EGAD00001000205

• UK10K_NEURO_MUIR REL-2012-07-05

  EGAD00001000236_UK10K_NEURO_MUIR_REL_2012_07_05

  Dataset EGAD00001000236

• OLD DATA FILES FOR SJMB - Superseded by EGAD00001001864

  OLD DATA FILES FOR SJMB - Superseded by EGAD00001001864

  Dataset EGAD00001000269

• ICGC prostate cancer miRNA sequencing

  ICGC prostate cancer miRNA sequencing

  Dataset EGAD00001000304

• ICGC prostate cancer RNA sequencing

  ICGC prostate cancer RNA sequencing

  Dataset EGAD00001000305

• UK10K_NEURO_ASD_TAMPERE REL-2012-11-27

  UK10K_NEURO_ASD_TAMPERE REL-2012-11-27

  Dataset EGAD00001000314

• UK10K_NEURO_FSZNK REL-2012-11-27

  UK10K_NEURO_FSZNK REL-2012-11-27

  Dataset EGAD00001000332

• ICGC prostate cancer MBD sequencing (DNA methylation)

  Dataset EGAD00001000632

• Whole genome sequencing dataset from: Biased allelic expression in human primary fibroblast single cells.

  Dataset EGAD00001001083

• OAfg-M-1

  Methylation profiling of hip osteoarthritis patients who have undergone total joint replacement

  Dataset EGAD00010001291

• 450K_DKFZ_JMML_DBL

  DNA methylation analysis from primary human JMML and normal blood samples using 450K

  Dataset EGAD00010001296

• Nanostring

  Nanostring PanCancer immune profiling data for The interface of malignant and immunologic clonal dynamics in high-grade serous ovarian cancer

  Dataset EGAD00010001515

• scoop-G-1

  Human Core Exome Genotyping for 1456 severe early onset obesity cases (SCOOP)

  Dataset EGAD00010001623

• RHD_SA_HC24_Cases

  Definite and borderline rheumatic heart disease cases and patients with mild non-diagnostic valvulopathy recruited in Samoa

  Dataset EGAD00010000960

• WP8_DNA_Methylation_450K_UCL

  BLUEPRINT DNA methylation profiles of monocytes, T cells and B cells in type 1 diabetes-discordant monozygotic twins

  Dataset EGAD00010001025

• CRU303 GWAS Raw Data Batch 1 Controls

  Raw data files of samples in batch 1 of CRU303 GWAS

  Dataset EGAD00010001955

• CRU303 GWAS Called Data Batch 2

  Called genotypes of samples in batch 2 of CRU303 GWAS

  Dataset EGAD00010001956

• Blood dataset - Case-control

  blood-based gene expression from breast cancer cases and age-matched controls

  Dataset EGAD00010001062

• CRU303 GWAS Raw Data Batch 2 Controls

  Raw data files of samples in batch 2 of CRU303 GWAS

  Dataset EGAD00010001953

• ADME_gene_expression

  Gene expression for 303 ADME and ADME related genes (averaged log2 signal intensities using Human-WG6v2 Expression BeadChip)

  Dataset EGAD00010001709

• GWAS results from Danjou et al, Nature Genetics 2015

  GWAS results in epacts format for Danjou et al, Nature Genetics 2015

  Dataset EGAD00010001722

• scRNA-seq of glioblastoma cells alone or after co-culture with cerebral organoid cells

  Dataset EGAD00001005315

• tupiniquim_masked_Axiom_InCor_BB

  Sample genotyped with Axiom InCor BB (Affymetrix) with local ancestry masking of non-Native American ancestry

  Dataset EGAD00010001801

• Methylation_monocytes_female_CD

  Whole-genome DNA methylation profiling of CD14+ monocytes obtained from CD-active, CD-remissive and non-CD individuals

  Dataset EGAD00010001846