23249 results for "ega"

in 29.45 milliseconds.

• Chugai_colorectal_organoid_sequencing

  Genomic and transcriptomic characterization of drug-resistant colon cancer stem cell lines.

  Study EGAS00001000872

• Transcriptomic profiling of lymphedema

  RNA-seq of skin from human subjects with and without lymphedema

  Study EGAS00001004516

• Psoriatic_arthritis_WGS

  Whole genome sequencing of immune cells from patients diagnosed with psoriatic arthritis

  Study EGAS00001002822

• Infant_Spindle_Tumour_Study

  We aim to describe the transcriptomic landscape of infant spindle cell tumours.

  Study EGAS00001002935

• Plasma DNA profile in DNASE1L3 deficiency-Mouse AAV samples

  Plasma DNA profile in DNASE1L3 deficiency-Mouse AAV

  Study EGAS00001004409

• PBMC_dual_10X_kit

  Dual snRNAseq and ATACseq of replicate PBMC samples for 10X beta testing.

  Study EGAS00001004834

• Genomic analyses to identify metabolic vulnerabilities

  Genomic analyses to identify metabolic vulnerabilities on PDX samples

  Study EGAS00001007244

• Settlement of Polynesia

  Genotypes used to reconstruct the settlement timings and paths of Polynesia.

  Study EGAS00001005362

• HCA_Muscle_Adult_Wellcome_RNA_Managed_Access

  Transcriptomic analysis of skeletal muscle, to understand the mechanisms of muscle ageing.

  Study EGAS00001007495

• Bulk RNA-seq of ATCWGS42 PDX models

  This dataset contains bulk RNA-seq dataset for ATCWGS42 PDX models

  Dataset EGAD50000002145

• snRNA-seq of two samples of ATCWGS42

  This dataset contains snRNA-seq data of two samples of ATCWGS42

  Dataset EGAD50000002146

• G3-MB WGS

  Whole genome sequencing BAM files for four samples of MYC-amplified Group 3 medulloblastoma cell models.

  Dataset EGAD50000002302

• Single cell sequencing in D425

  Single cell sequencing aligned BAM files for the D425 cell line

  Dataset EGAD50000002304

• G3-MB ATAC Sequencing

  ATAC sequencing for two MYC-amplified G3-MB cell models.

  Dataset EGAD50000002305

• Brain mets discovery cohort copy number calls

  These are the Sequenza copy number calls for the 30 brain tumour samples within the discovery cohort.

  Dataset EGAD00001005983

• McGill EMC Release 4 for assay "H3K27ac"

  McGill EMC Release 4 for assay "H3K27ac"

  Dataset EGAD00001001298

• R code

  R code to run analyses on anonymized data from ABACUS for IMvigor010 ctDNA publication.

  Dataset EGAD00001007652

• MED12L Gene Alterations Define Aggressive BRCA2-Mutant Prostate Cancers

  Aligned sequence data from 14 Prostate cancer samples with BRCA2 mutations

  Dataset EGAD00001002739

• Exome sequencing of isolate populations and Generation Scotland

  Exome sequencing of isolate populations and Generation Scotland

  Dataset EGAD00001002715

• TRACERx RRBS

  TRACERx 100: RRBS data from a subset of the first 100 TRACERx tumours

  Dataset EGAD00001004798

• UK10K_NEURO_ASD_TAMPERE REL-2013-04-20

  UK10K_NEURO_ASD_TAMPERE REL-2013-04-20

  Dataset EGAD00001000437

• UK10K_NEURO_FSZNK REL-2013-04-20

  UK10K_NEURO_FSZNK REL-2013-04-20

  Dataset EGAD00001000439

• NSCLC WGS

  NSCLC WGS.

  Dataset EGAD00001000888

• Carcinoid study - WES dataset

  Fernandez-Cuesta et al, 2014, Nature Communication, Whole exome sequencing data set

  Dataset EGAD00001000820

• ChIPseq data

  ChIPseq data for CLL patients

  Dataset EGAD00001008665

• Cancer Alliance RNA-Seq total RNA

  Total RNA transcriptome profiling by high-throughput for individualized cancer interpretation

  Dataset EGAD00001006235

• BCL11B PacBio data set

  BCL11B PacBio data set, 4 samples

  Dataset EGAD00001007863

• RNA-seq

  RNA-seq data

  Dataset EGAD00001008959

• RNAseq of U251 and two ID1 gene knockouts

  RNAseq of U251 and two ID1 gene knockouts

  Dataset EGAD00001005089

• HipSci - Usher Syndrome - Exome Sequencing - July 2017

  HipSci - Usher Syndrome - Exome Sequencing - July 2017

  Dataset EGAD00001003528

• Reference epigenome ADMSC07 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007391

• MPNST exome and genome

  This is the complete dataset (exome and genome) for the EGAS00001000974 study.

  Dataset EGAD00001001040

• McGill EMC Release 4 for assay "H3K9me3"

  McGill EMC Release 4 for assay "H3K9me3"

  Dataset EGAD00001001299

• 15x WGS HELIC MANOLIS

  15x whole genome sequencing in samples from the Cretan Greek isolate collection HELIC MANOLIS

  Dataset EGAD00001001897

• CONTAGIOUS trial - COVID-19 16S metadata

  Study metadata, containing the clinical information on samples and patients

  Dataset EGAD00001008006

• Dataset of PPB

  Whole exome sequencing and miRNA-seq data of PPB.

  Dataset EGAD00001000822

• McGill EMC Release 4 for assay "H3K4me1"

  McGill EMC Release 4 for assay "H3K4me1"

  Dataset EGAD00001001296

• GWAS genotype data of Japanese COVID-19 cases

  GWAS genotype data of 2,393 Japanese COVID-19 cases.

  Dataset EGAD00001008957

• RNAseq metadata

  Meta-data/patient information for the bulk RNAseq data

  Dataset EGAD00001008815

• CD4 T-Cell RNA-Seq

  CD4 T-Cell RNA-Seq

  Dataset EGAD00001002687

• Whole-genome sequencing

  Variant calls from whole-genome sequencing of Ly1-07 using Complete Genomics paired-end sequencing service.

  Dataset EGAD00001004783

• Leeds GLASS Data Release 1

  Paired end Illumina whole exome sequencing of 9 GBM trios (blood, primary and recurrent tumour).

  Dataset EGAD00001004856

• WGS data for Glioblastoma (EGAS00001003953, H016)

  Whole genome sequencing data for 20 human glioblastoma patients.

  Dataset EGAD00001006545

• McGill EMC Release 4 for assay "H3K36me3"

  McGill EMC Release 4 for assay "H3K36me3"

  Dataset EGAD00001001295

• RCRF release 1

  Rare Cancer Research Foundation: leiomyosarcoma, large cell neuroendorcrine carcinoma, clear cell carcinoma

  Dataset EGAD00001010199

• Reference epigenome IPS06_X_ENeuron_WGBS data generated from KEP study

  A IPS06_X_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003478

• PacBio medulloblastoma datasets

  PacBio data from 5 medulloblastoma tumor-normal pairs. The data consists of BAM files aligned using NGMLR

  Dataset EGAD00001010852

• Familial Breast cancer dataset

  Illumina HiSeqXTen platform sequencing data of whole genome libraries prepared from 156 matched tumour-normal samples from 78 donors

  Dataset EGAD00001004494

• BLUEPRINT: DNaseI-seq for monocytes

  DNaseI-seq for monocytes

  Dataset EGAD00001000674

• Dataset for "Genomic landscape of oral cancers" (CGI WGS)

  Dataset for "Genomic landscape of oral cancers" (CGI WGS)

  Dataset EGAD00001004339

• OT2_Illumina_WGS_CONT

  Whole-genome sequencing on Illumina HiSeq2000/2500 of normal colon control tissue (OT2_cohort)

  Dataset EGAD00001003371

• Reference epigenome OB56_N_PreA_mRNA-Seq data generated from KEP study

  A OB56_N_PreA_mRNA-Seq paired end data for Preadipocytes(fat)

  Dataset EGAD00001003486

• Whole genome sequencing

  Whole genome sequencing of 5 IM samples

  Dataset EGAD00001010157

• McGill EMC Release 4 in tissue "kidney"

  McGill EMC Release 4 in tissue "kidney"

  Dataset EGAD00001001287

• DIPG alignments

  Whole genome alignments of DIPG patients

  Dataset EGAD00001000814

• DATA FILES FOR Histone-NSD2_RNASeq

  DATA FILES FOR Histone-NSD2_RNASeq

  Dataset EGAD00001000655

• TGL49_HBC sWGS

  Shallow whole genome sequencing of plasma samples from healthy control patients.

  Dataset EGAD00001010000

• McGill EMC Release 4 for assay "H3K4me3"

  McGill EMC Release 4 for assay "H3K4me3"

  Dataset EGAD00001001297

• Reference epigenome IPS05_X_NPC_WGBS data generated from KEP study

  A IPS05_X_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003477

• Modern Aboriginal Australians WGS

  BAM files for high-throughput whole genome sequence data of 17 modern Aboriginal Australians

  Dataset EGAD00001004492

• Normal CD19

  Bulk RNA-seq on normal human CD19+ cells

  Dataset EGAD00001011105

• ChIPseq data

  ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

  Dataset EGAD00001003258

• LLNEXT Pilot VLP sequencing

  This dataset contains sequencing data from VLP-enriched fecal microbiome from LLNEXT project

  Dataset EGAD00001011291

• IBC Whole exome sequencing

  Bam files of IBC whole exome sequencing, including tumor and matching normal

  Dataset EGAD00001011648

• FUNGAL INFECTION IN NEURAL TISSUE FROM AMYOTROPHIC LATERAL SCLEROSIS

  Sequence data from fungal infection isolated from neural tissue in ALS patients.

  Dataset EGAD00001003341

• Whole genome sequencing of familial isolated oesophagus atresia.

  Then individuals from three families segregating esophagus atresia.

  Dataset EGAD00001006092

• WGS data of plasma samples from healthy individuals

  WGS data of plasma samples from healthy individuals (N=29)

  Dataset EGAD00001006421

• RCRF release 1

  exome data from leiomyosarcoma, large cell neuroendocrine carcinoma, and clear cell sarcoma

  Dataset EGAD00001010197

• DATA FILES FOR GRUBER SJAMLM7 RNASEQ

  DATA FILES FOR GRUBER SJAMLM7 RNASEQ

  Dataset EGAD00001003135

• HipSci - Hypertrophic Cardiomyopathy - Exome Sequencing - July 2017

  HipSci - Hypertrophic Cardiomyopathy - Exome Sequencing - July 2017

  Dataset EGAD00001003523

• 20180221_EGA_OESO

  Illumina platform sequencing of whole genome libraries prepared from normal, Barrett's oesophagus and oesophageal cancer samples from 44 donors

  Dataset EGAD00001003996

• Genomic analysis of Japanese gastric cancer

  fastq of 345 Japanese gastric cancer

  Dataset EGAD00001004051

• Dataset for "Genomic landscape of oral cancers" (Illumina WGS)

  Dataset for "Genomic landscape of oral cancers" (Illumina WGS)

  Dataset EGAD00001004356

• UK10K_NEURO_FSZ REL-2013-04-20

  UK10K_NEURO_FSZ REL-2013-04-20

  Dataset EGAD00001000615

• Pseudogene RNAseq

  RNA sequencing to validate findings of somatic pseudogenes acquired during cancer development

  Dataset EGAD00001000732

• RNA sequencing of Resistant BCC samples.

  RNA sequencing of Resistant BCC samples.

  Dataset EGAD00001000881

• nccRCC targeted exome sequencing of 375 genes

  Targeted exome sequencing of 375 genes

  Dataset EGAD00001001047

• Anaplastic Meningioma V3 cancer gene panel

  Investigation into causal genes underlying anaplastic meningioma

  Dataset EGAD00001001430

• McGill EMC Release 4 for assay "H3K27me3"

  McGill EMC Release 4 for assay "H3K27me3"

  Dataset EGAD00001001294

• LGG Epilepsy Cohort RNA-Seq

  LGG Epilepsy Cohort RNA-Seq

  Dataset EGAD00001001666

• Maastricht Irritable Bowel Syndrome metagenomic sequencing

  Metagenomic shotgun sequencing of Irritable bowel syndrome patients and matched controls

  Dataset EGAD00001002668

• SSBP1 sample

  The dataset includes exome sequencing results for a patient with SSBP1 mutations that cause a complex optic atrophy spectrum disorder

  Dataset EGAD00001005475

• WGS data of plasma samples from BRCA patients

  WGS data of plasma samples from BRCA patients (N=10)

  Dataset EGAD00001006420

• Whole genome sequencing of EBV Associated Nasopharyngeal Carcinoma

  Whole genome sequencing of EBV Associated Nasopharyngeal Carcinoma

  Dataset EGAD00001006429

• INSPIRE Whole Exome Sequencing - Tumors

  INSPIRE whole exome sequencing of tumors updated

  Dataset EGAD00001006563

• Germline snv g.vcf for EGAS00001004572

  Bulk Germline snv vcfs from haplotypecaller analysis in EGAS00001004572

  Dataset EGAD00001006910

• dataset1

  Contains 4 clonal organoid samples + 1 bulk healthy tissue sample

  Dataset EGAD00001009485

• 108 WGS of epileptic patients from the CENet cohort

  Dataset is described in doi: https://doi.org/10.1101/2022.12.13.22283363

  Dataset EGAD00001011301

• 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

• 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

• Genetics of gene expression in primary human immune cells Data Access Committee

  Dac EGAC01000000009

• NB WGS 2015

  Dataset EGAD00001001687

• EGAD00010000238

  CLL Expression array

  Dataset EGAD00010000238

• EGAD00010000284

  NBS control samples only (Hap300)

  Dataset EGAD00010000284

• ICGC Germany PedBrain Medulloblastoma Pilot_2_LM

  ICGC Germany PedBrain Medulloblastoma Pilot_2_LM

  Dataset EGAD00001000027

• UK10K_NEURO_MUIR REL-2011-01-28

  UK10K_NEURO_MUIR REL-2011-01-28

  Dataset EGAD00001000052

• Renal Matched Pair Cell Line Exome Sequencing

  Renal Matched Pair Cell Line Exome Sequencing

  Dataset EGAD00001000100