22951 results for "ega"

in 30.04 milliseconds.

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Autozygosity_pilot_Born_in_Bradford

  The offspring of first cousin marriages have ~6% of their genome autozygous, i.e. homozygous identical by descent, or even more if there was further consanguinity in their ancestry. In the UK there are large populations with very high first cousin marriage rates of 20-50%. Sequencing the exomes of a sample of these individuals has the potential both to support genetic health programmes in these populations, and to provide genetic research information about rare loss of function mutations. This pilot study based on existing cohort samples from the Born In Bradford study will identify homozygous individuals for almost all variants down to an allele frequency around 1%, plus individuals carrying hundreds of new homozygous rare loss-of-function variants, and will support development of community relations and ethics for a wider study currently being designed. The data deposited in the EGA consist of low coverage whole exome sequencing on these samples.

  Study EGAS00001000462

• Genomewide Association Study of Inflammatory Bowel Disease

  WTCCC genome-wide case-control association study for Inflammatory Bowel Disease (IBD) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000006

• WTCCC case-control study for Rheumatoid Arthritis

  WTCCC genome-wide case-control association study for Rheumatoid Arthritis (RA) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000011

• WTCCC case-control study for Type 1 Diabetes

  WTCCC genome-wide case-control association study for Type 1 Diabetes (T1D) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000014

• WTCCC case-control study for Type 2 Diabetes

  WTCCC genome-wide case-control association study for Type 2 Diabetes (T2D) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000016

• WTCCC case-control study for Coronary Artery Disease

  WTCCC genome-wide case-control association study for Bipolar disorder (CAD) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000003

• Molecular Phenotyping to Accelerate Genomic Epidemiology (MolPAGE)

  MolPAGE is an integrated EU project that aims to develop methods and tools for discovery of biomarkers associated with diabetes (see www.ocdem.ox.ac.uk for MolOBB and www.twinsuk.ac.uk for MolTWIN).

  Study EGAS00000000102

• Oncotrack DAC

  Dac EGAC00001000469

• EGAD00010000766

  We have established a mechanism for the collection of postal DNA samples from consenting National Joint Registry for England and Wales (NJR) patients and have carried out genotyping genome-wide in 903 patients with the condition Developmental Dysplasia of the Hip (DDH) on the Illumina CoreExome array

  Dataset EGAD00010000766

• WTCCC1 1958BC control dataset

  WTCCC1 project samples from 1958 British Birth Cohort

  Dataset EGAD00000000001

• Evaluation of size selection on cancer specific sequencing libraries

  A couple of previously characterized and sequenced libraries will be repeated using a couple of differing size selection criteria and skim sequenced using an Illumina HiSeq. The resulting sequence will be analyzed to determine the optimal DNA library size for our specific downstream analysis.

  Dataset EGAD00001000301

• Piloting exome resequencing in consanguineous families with homozygosity mapping intervals

  This pilot study aims to generate pilot data to inform future study designs in consanguineous families or inbred populations by resequencing the exome of six individuals from five families with neurodevelopmental diseases. For all of these families a single mapping interval containing the causal variant has previously been identified.

  Dataset EGAD00001000341

• Paroxysmal Neurological Disorders - rare epilepsies

  We are sequencing the exomes of patients with paroxysmal neurological disorders mainly focusing on migraine and epilepsy. Cases are collected from performance sites of members of EuroEPINOMICS. Most cases have a strong family history. The study sample will include both cases and controls.

  Dataset EGAD00001000647

• Discovery of resistance mechanisms to the BRAF inhibitor vemurafenib in metastatic BRAF mutant melanoma

  Discovery of resistance mechanisms to the BRAF inhibitor vemurafenib in metastatic BRAF mutant melanoma by massively-parallel sequencing of tumour samples. Comparison of genomic characteristics of pretreatment 'sensitive' to recurrence 'resistant' tumours to identify the genetics of drug resistance.

  Dataset EGAD00001000707

• Dataset Rdata segmentations

  This data set contains an .Rdata file for all the processed segmentation profiles from 81 lpWGS samples included used in downstream analyses from Github repository Evo_history_CACRC. Lastly, there is an .Rdata object with 50 segmentations for 50 total samples from 25 sporadic SNP adenomas used in the comparison with colitis samples.

  Dataset EGAD00010001586

• Tcells_CD_scRNAseq

  This set features unfiltered, aligned, UMI-based single cell RNA sequencing count data for 5290 Blood, intraepithelial ileum (IEL) and lamina propria ileum (LPL) T cells from Crohn's disease patients as published in Uniken Venema et al, Gastroenterology 2019

  Dataset EGAD00010001649

• ETMR_Meth

  77 ETMR samples were profiled using methylation array. DNA from frozen tissue and formalin-fixed, paraffin-embedded (FFPE) materials were analyzed with the Illumina Infinium HumanMethylation450 (450k) and MethylationEPIC (EPIC) array according to manufacturer’s instructions and with a modified method that was previously described (Torchia et al. Cancer Cell. 2016; Triche et al. Nucleic Acids Res. 2013).

  Dataset EGAD00010001669

• COLONOMICS small RNA sequencing

  This dataset consists of SOLiD small RNA-seq of 250 colorectal samples: 100 tumor tissue samples, 100 normal tissue samples (adjacent to tumor sites) and 50 matched control samples of healthy individuals. CSfasta and qual files converted to single fastq files prior to uploading.

  Dataset EGAD00001004827

• Whole-exome sequencing and microRNA profiling predicted relapse risk of stage I lung adenocarcinomas for the study and all the datasets.

  This dataset contains CEL files and rma normalized expression value for microarray of stage I lung adenocarcinomas from Asian patients. In total, there are 69 patients and 138 samples, including 69 tumor samples and 69 adjacent normal samples.

  Dataset EGAD00010001901

• methylation_bc_buccal

  Raw methylation data from buccal samples in individuals with breast cancer.

  Dataset EGAD00010002082

• methylation_ctrl_buccal

  Raw methylation data from cervical samples in controls.

  Dataset EGAD00010002080

• methylation_ctrl_cervix

  Raw methylation data from cervical samples in controls.

  Dataset EGAD00010002079

• Trynka_sceQTL_genotypes

  Blood samples were obtained from 119 healthy individuals of British ancestry. Genomic DNA was isolated from a suspension of PBMCs from each individual using a DNA isolation kit (Qiagen). Genotyping was then performed using the Infinium CoreExome-24 (v1.3) chip (Illumina).

  Dataset EGAD00010002291

• Genotype-Multiple-Myeloma-case

  Genome-Wide Human SNP Array 6.0 or the CytoScan HD array, according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA) now part of Thermo Fisher Scientific (Thermo Fisher Scientific, Inc.)

  Dataset EGAD00010002102

• ChIP-seq ERa in primary breast cancer tissues

  ChIP-seq has been perfomed on 5 tumor fresh-frozen primary breast cancer tissues from female patients. Immunoprecipitation has been performed for ERa (SC-543, Santa Cruz). Raw single-end fastq data have been aligned using bwa-mem using Hg19 genome assebly as reference. Unfiltered aligment bam files are provided.

  Dataset EGAD50000000014

• Single-cell multi-omic analysis of control and glioblastoma samples from the brain and border regions.

  Single-cell RNA-sequencing. Some samples were analyzed using CITE-Seq and samples were multiplexed and run on the same 10X reaction

  Dataset EGAD50000000045

• RNA-Seq data of S24 cells for the publication Recording physiological history of cells with chemical labeling.

  Recordings of elevated calcium levels allowed selections of cells from heterogenous populations for transcriptomic analysis. Paired RNA-Seq from S24 cells using Takara SMARTer Ultra Low Input RNA v4 kit and sequencing on a Illumina NovaSeq 6000.

  Dataset EGAD50000000082

• 10x Genomics single-cell RNA sequencing of cerebrospinal fluid

  This dataset contains fastq files from single-cell RNA sequencing of cerebrospinal fluid of 81 patients with multiple sclerosis (n=33) or other neurological disease (n=48) using the 10x Genomics Chromium single cell 3’ v2 chemistry. Sequencing was performed using an Illumina HiSeq4000 sequencer.

  Dataset EGAD50000000184

• Single-cell RNA-sequencing of peritoneal fluid from patients with achalasia

  The data published here contains single-cell RNA-sequencing (scRNAseq) data as obtained using the 3' scRNAseq using Chromium Single Cell 3’ Reagent from 10X Genomics on peritoneal fluid (PF) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000251

• University of Bergen Rare Monogenic Autoimmune Syndrome (APS-1) Data Access Committee

  This is the DAC appointed by the University of Bergen for the datasets related to the study of APS-1 in the Department of Clinical Sciences . These data are from single cell transcriptomic analysis of expanded Tregs from patients with the rare monogenic autoimmune syndrome “APS-1” compared to healthy donors.

  Dac EGAC50000000081

• Exome sequencing for heterogeneity and evolution of DNA mutation rates

  Dataset comprising 27 pairs of high-depth (300x) WES results obtained from 54 PDXs derived from primary CRCs resected synchronously with their corresponding metastases. These exoms sequences are part of a larger study on the heterogeneity and evolution of DNA mutation rates in microsatellite-stable colorectal cancer.

  Dataset EGAD50000000208

• Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  This study explores the evolution of tumor and blood immune microenvironment and related mechanisms that shape breast cancer progression, culminating in a heterogeneous lethal cancer.

  Study EGAS00001004956

• Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

  Targeted cfDNA and WBC sequencing data from patients profiled in the study "Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer". Note that 'Exome sequencing' is listed under Dataset Type since there is no option for targeted panel sequencing.

  Dataset EGAD50000000303

• Pediatric HGG panel sequencing

  Targeted glioma panel sequencing of pediatric hemispheric high-grade gliomas and diffuse midline gliomas. File type is paired-read fastq files (2 per sample). Sequencing was performed on Illumina NextSex instruments. Genes covered: TP53, H3F3A, HIST1H3B, HIST1H3C, IDH1, IDH2, KRAS, PIK3CA, TERT promoter, PTPN11, FGFR1/2/3, MYB, BRAF, MYBL1, EGFR, PDGFRA, MYCN, MYC, CDKN2A.

  Dataset EGAD50000000326

• INCLIVA-CC-WES

  This dataset consists of WES NGS paired-end raw data (FASTQ R1, R2 and UMI sequence) obtained from 25 localised colon cancer patients. Specifically, we have 25 primary tumor tissue samples, 17 metastatic tissues, 25 white blood cell samples, 25 plasma samples collected at relapse, 12 baseline plasma samples and 15 plasma samples post-surgery.

  Dataset EGAD50000000293

• RNA sequencing

  For RNA sequencing: All sample numbers starting with 6717 are tumor samples which has been sequenced using transcriptomics (see BAM files). It concerns biopsies of metastatic lesions from patients with BRAFV600 mutated melanoma, obtained before, during and after the study treatment (see samples metadata). RNA sequencing is performed using the Illumina Novaseq 6000 system.

  Dataset EGAD50000000383

• Whole exome sequencing of preneoplasia lung adenocarcinoma

  Multi-region exome sequencing of 271 pulmonary nodules and matched adjacent lung tissue including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=49), adenocarcinoma in situ (AIS, N=42), minimally invasive adenocarcinoma (MIA, N=37), invasive lung adenocarcinoma (ADC, N=86) and adjacent lung tissues (Normal, N=57).

  Dataset EGAD50000000397

• Tumor heterogeneity and immune-evasive microenvironment in T follicular helper cell lymphomas

  Single-cell analysis data for tumor and immune cells from lymph node and peripheral blood samples of 14 patients with T follicular helper cell lymphomas

  Study EGAS50000000275

• Human Brain Small Extracellular Vesicles Contain Selectively-Packaged, Full-Length mRNA

  sEVs were isolated from postmortem human brain tissue. RNA was extracted from the source brain tissue and the sEVs and prepared for RNA sequencing. cDNA was sequenced on the Illumina HiSeq using a 2x150 paired-end read configuration.

  Dataset EGAD50000000042

• RNA sequencing of Non-Small Cell Lung Cancer and adjacent normal tissue

  RNA sequencing was performed on 108 NSCLC tumor samples and their paired adjacent normal tissues (n=21) to identify associations with clinical and immune characteristics.

  Study EGAS50000000246

• ctDNA diva aggregate data

  ctDNA biomarker and relevant clinical data for divarasib phase I GO42144 study, including tumor type, KRAS G12C mutation detectability in plasma at baseline, baseline SLD, sites of metastasis, lines of therapy, best response, confirmed best response, PFS, ctDNA tumor fraction and KRAS G12C VAF at baseline/C1D15/C3D1.

  Dataset EGAD50000000454

• Clinical and molecular features of early onset pancreatic cancer

  A retrospective cohort study on the clinical and molecular features of early onset pancreatic cancers (i. e. diagnosed at <50 years old) in comparison with later onset tumors.

  Study EGAS50000000362

• IMSGC and MultipleMS project MS patients from Germany, Mainz

  Genotype and phenotype data on 301 MS patients from Germany, Mainz. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002484

• The evolution of ovarian high grade serous carcinoma from STIC lesions

  We performed shallow whole genome sequencing of normal fallopian tube, stroma, p53 signatures, STIL/STIC, invasive and metastasis carcinoma from 5 high grade serous carcinoma cases.

  Study EGAS50000000360

• IMSGC and MultipleMS project MS patients from Italy, OSR

  Genotype and phenotype data on 538 MS patients from Italy, OSR. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002486

• IMSGC and MultipleMS project MS patients from Germany, TUM

  Genotype and phenotype data on 683 MS patients from Germany, TUM. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002489

• IMSGC and MultipleMS project MS patients from Italy, Piedmont

  Genotype and phenotype data on 1067 MS patients from Italy, Piedmont. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002490

• Oxford Nanopore WGS

  A total of 20 individuals, were analyzed using Oxford Nanopore Technologies long-read sequencing. Each individual was sequenced on one PromethION flow-cell, providing approximately 25-30X coverage of the genome. The data is deposited as nanopore BAM files with methylation information included.

  Dataset EGAD50000000573