22938 results for "ega"

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• AT-AML samples dataset

  Serial samples from one AT-AML patient as described in publication Goldgraben et al Pediatric Blood & Cancer 2020. Whole exome sequencing of a AT-'germline' blood sample, one bone marrow sample (at AML diagnosis) and 3 AML blood samples. Library preped using the Illumina Nextera Rapid Capture Exome Enrichment Kit, and sequenced as PE150 on HiSeq4000. Provided: 5 BAM files (GRCh37); 2 VCF analyses (germline and somatic)

  Dataset EGAD00001006090

• Pre-clinical evolution of haematological malignancies_WGS

  Leukaemia and related blood cancers occur due to genetic changes that typically accumulate over many years. This study will employ targeted next-generation sequencing to retrace the preclinical evolution of several types of haematological malignancy. Investigating the progression of the earliest pre-malignant ancestral clones promises to offer valuable insights into early leukaemia evolution and therapeutic vulnerabilities of leukaemia stem cells.

  Dataset EGAD00001006423

• FASTQ files

  Raw FASTQ files obtained from in situ Hi-C of 16 normal B cells (3 naive B cells, 3 germinal center B cells, 3 plasma cells, and 3 memory B cells, together with a merge file for each subpopulation), 7 chronic lymphocytic leukemias (2 unmutated IGHV and 5 mutated IGHV), and 5 mantle cell lymphomas (2 conventional and 3 leukemic non-nodal).

  Dataset EGAD00001006485

• Valid reads

  Valid reads obtained after analyzing in situ Hi-C data of 16 normal B cells (3 naive B cells, 3 germinal center B cells, 3 plasma cells, and 3 memory B cells, together with a merge file for each subpopulation), 7 chronic lymphocytic leukemias (2 unmutated IGHV and 5 mutated IGHV), and 5 mantle cell lymphomas (2 conventional and 3 leukemic non-nodal).

  Dataset EGAD00001006486

• Prognostic and immunomodulatory consequences of ERV expression in ovarian cancer

  The dataset contains transcriptional data obtained using total RNA sequencing on a Illumina machin. 59 samples are from the Hammersmith Hospital (HH) cohort of human primary ovarian tumours and 20 samples are from ovarian cancer cell lines Kuramochi (3 replicates) and Ovsaho (2 replicates) treated with 1 uM DNMTi guadecitabine or vehicle, at an early (day 5) or late (day 8) timepoint.

  Dataset EGAD00001006589

• Somatic whole exome sequencing of a hypermutated gliosarcoma case

  The dataset includes the whole-exome sequencing (WES) of an extramedullary tumor anterior to the spinal cord at T4, which was resected and diagnosed as gliosarcoma. The patient initially diagnosed with a low-grade brain glioma via biopsy, followed by adjuvant radiation and temozolomide treatment. WES was performed using Illumina NovaSeq6000 with 2x100 bp reads. Mean coverage of 152.4x and 230.6x was achieved for normal and tumor, respectively.

  Dataset EGAD00001006816

• Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls

  We created three technical replicates of cell-free DNA from AML patient plasma to assess batch effects and utility of spike-in controls for the cfMeDIP-seq method. Each set of samples were given to three different technicians with slightly different protocols. Details can be found in Wilson et al. "Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls".

  Dataset EGAD00001006986

• NGS reads files of PSCCE

  The aligned bam file of next generation sequencing performed on PSCCE.

  Dataset EGAD00001007002

• Multiregion exome sequencing

  Multiregional analysis of three cases of GBM. For each tumor, 9 portions were analyzed by whole exome sequencing. A total of 27 bam files are present in our dataset.

  Dataset EGAD00001007063

• Balanced_Brain_Tumour_Whole_Genome_Sequencing

  We propose to definitively characterise the somatic genetics of a number of pediatric malignant tumours including ependymoma, high grade glioma and central nervous system primitive neurectodermal tumours through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Study EGAS00001000360

• WNT7B-reporter organoids sorted

  Transcriptomic data from pancreatic ductal adenocarcinoma (PDAC) organoids to investigate the role of WNT7B. This dataset includes: - Bulk mRNA sequencing of WNT7B-reporter pancreatic ductal adenocarcinoma (PDAC) organoids sorted for cells displaying low, medium and high expression of the reporter

  Study EGAS50000001543

• Deep sequencing of the gut microbiome from 946 healthy donors of the Milieu Intérieur cohort

  In this study, we aim to determine, in a thousand healthy donors, how the profile and dynamics of the gut microbiome are affected by host factors such as sex, age and lifestyle parameters.

  Study EGAS00001004437

• Improved Sezary cell detection and novel insights into immunophenotypic and molecular heterogeneity in Sézary syndrome

  We performed a prospective investigation in Sézary syndrome by the application of a standardized multiparameter flow cytometry, FACS-cell sorting, and RNA-sequencing for an in-depth immunophenotypic and transcriptional profiling of Sézary cells.

  Study EGAS00001005229

• Single-cell profiling maps the spectrum of crosstalk between glioma cells and tumor associated macrophages

  Tumor associated macrophages (TAMs) are a heterogeneous glioma infiltrate, implicated in cancer progression. We performed single-cell RNA-sequencing, to survey TAM response-patterns in glioma.

  Study EGAS00001002185

• Genetic landscape of pediatric ependymoma

  We performed whole-genome sequencing of 42 paired tumor/normal EPD genomes and analyzed somatically acquired single nucleotide variations (SNVs), insertion/deletions, structural variations and copy number variations in the cancer genomes

  Study EGAS00001000254

• Bulk RNA sequencing data of high-grade serous ovarian cancer samples (set 18-19)

  The dataset contains RNA sequencing data of 66 high-grade serous carcinoma (HGSC) patients sequenced with Novoseq 6000. The 155 samples are fresh frozen tissue samples that have been collected from different tissues and time points before and during treatment. The files provided are paired fastq files.

  Dataset EGAD50000002370

• RNA-sequencing of tumors from 45 patients with recurrent or metastatic gastric cancer treated with immune checkpoint inhibitors

  Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this work, we used our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identified four molecular subtypes that were prognostic for survival. We then built a support vector machine with linear kernel and the binary classifier to generate a risk score that is prognostic for 5-year overall survival and validated the risk score using three independent datasets. We also found that the molecular subtypes predicted response to adjuvant 5-fluorouracil and platinum after gastrectomy and immune checkpoint inhibitors in patients with metastatic or recurrent disease. The 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner. This EGA archive includes both St.Mary hospital cohort samples (n=28) and Yonsei immunotherapy patient samples (n=17).

  Study EGAS00001005588

• FEGA FAQs: a summary of the Q+A session from the FEGA workshop at ELIXIR AHM 2024

  The ELIXIR All Hands Meeting (AHM) was held in Uppsala, Sweden this June. We at EGA always look forward to this meeting as a chance to see many of our partners and colleagues face-to-face. The Federated EGA (FEGA) is one initiative that always benefits from the AHM, and this year FEGA was in the spotlight, with several questions related to the advances we've made recently. We held this year's FEGA workshop a little differently, with most time for discussions on various “hot topics”. Organised in different breakouts, over 2 rounds, participants could choose which session they wanted to attend. One breakout was a general Q+A about FEGA and the path to join. We had a diverse range of participants, including experts from Central EGA (CEGA) and FEGA inaugural nodes, people from up and coming nodes at various stages of readiness, as well as participants not directly a part of FEGA but curious to learn more. It made for a great discussion! In this blog post, we highlight some of the questions for those who didn’t have a chance to be there. What is the FEGA Collaboration Agreement? It is a legal agreement between the node and Central EGA (CEGA) where both parties agree to certain responsibilities, like what services will be provided, sharing of public metadata with CEGA, interoperable technologies and more. The agreement is for 4 years, with the expectation of long-term sustainability. If a node is made up of multiple institutes, one institute must be the signatory. This could require a legal agreement within the node. CEGA is composed of CRG and EMBL-EBI (EGA itself has its own legal agreement between these institutes) Check the FEGA Collaboration Agreement What about purely medical data? Is this accepted in FEGA? CEGA only accepts data approved for secondary research use. We do accept medical data collected in this context, as do some nodes. Nodes are able to accept any data they like, as long as it is sensitive, thus in need of controlled access. What about other data types other than genomics? Like proteomics or images? They can be accepted, although CEGA is currently not designed with those formats in mind, so they might not fit with precision in the data model. The FEGA nodes are welcome to accept any data they want. Proteomic studies generating sensitive data are increasing, and specific work will likely be developed in the near future to adress this need. Does CEGA require a certain amount or type of data to be shared by the node? No, we do not and it's up to each node to decide what specific data types they accept and in what format. Nonetheless, a FEGA node has the duty to be open to data submission from any institution of their country, and can't be limited by design to a single or limited data source. Does CEGA and FEGA require a certain amount or type of details and description on the data to be submitted to the node? Data description (technical and biological), usually called metadata in this field, is crucial for the findability of the submitted data. Thus, we highly recommend including as much information as available to maximise the discoverability of the data. Specifical minimal metadata is defined in the submission schema, and thresholds are specified by the nodes. We are currently working towards a unified FEGA metadata model. What are the geographical borders of FEGA? FEGA should make the E for Everyone, because any institute worldwide could join and we indeed do have countries and use-cases worldwide. Some of them are very close to officially joining the federation. What are the main differences between FEGA and GDI? The two networks have a lot in common and also some critical differences. You can read all about this in one of our latest blog post. If you have more questions, please consider having a look at the FEGA onboarding webpage and, of course, don't miss our workshop at the ELIXIR AHM next year!

  Blog fega-faqs-elixir-ahm-2024

• Identification of genetic etiology of CAMRQ2

  We aimed to identify genetic etiology of cerebellar ataxia, mental retardation, and disequilibrium syndrome (CAMRQ). Targeted sequencing of the entire CAMRQ2 locus, 7.1 Mb interval on chromosome 17p13, in three affected individuals and two obligate carriers uncovered the mutation associated with CAMRQ2.

  Study EGAS00000000099

• Peking University BIOPIC Data Access Committee (PUBDAC).The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any academic research institutions complying with the laws and bioethic regulation policies of China will be approved.

  Dac EGAC00001000551

• The genomic architecture of mesothelioma

  The genome-wide landscape of somatically acquired mutations in mesothelioma has not been deeply characterised to date, but advances in DNA sequencing technology now allow this to be addressed comprehensively. Harnessing massively parallel DNA sequencing platforms, we will identify somatically acquired point mutations in all coding regions of the genome from patients with mesothelioma. In addition, using paired-end sequencing, we will map copy number changes and genomic rearrangements from the same patients.

  Dataset EGAD00001000360

• BLUEPRINT release August 2014, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000916

• Molecular characterization of T-PLL by Next-Generation Sequencing

  Patients with T-cell prolymphocytic leukemia (T-PLL) were profiled with multiple OMICS approaches based on Next-Generation Sequencing (NGS). In total, data from RNA-Seq, Whole-Exome Sequencing, Whole-Genome Sequencing and amplicon panel analyses in 134 samples are available. All samples were processed as paired-end libraries on Illumina sequencing machines. The data are available as paired FastQ files.

  Dataset EGAD00001003825

• SC_DDD-G-1

  Batch 1 of unfiltered genotype data for DDD Study patients (N=2,997), some of which were used in the neurodevelopmental disorder discovery GWAS (Niemi et al., Nature 2018). Samples were genotyped on the Illumina HumanCoreExome BeadChip. QC'd data is available in release EGAD00010001604

  Dataset EGAD00010001598

• SC_DDD-G-2

  Batch 2 of unfiltered genotype for DDD Study patients (N=8,286), some of which were used in the neurodevelopmental disorder discovery GWAS (Niemi et al., Nature 2018). Samples were genotyped on the Illumina InfiniumCoreExome Beadchip. QC'd data is available in release EGAD00010001604

  Dataset EGAD00010001600

• GCAT| WGS Imputation Panel V1

  This dataset includes the .hap, .legend and .sample files from the GCAT|Panel (Iberian reference panel), built from 785 samples, after QC, from the 808 WGS GCAT cohort, including 30.3M SNVs, 5M Indels and 89K SVs. This resource has been generated using Shapeit4 and WhatsHap software. Technology used HiSeq 4000, read length 150 bp, inner mate disatance 300 bp.

  Dataset EGAD00010002153

• Expression array

  The whole study comprises of two patient cohorts. Screening cohort: 40 patients of Germany; validation cohort: 40 patients from Asia. Further, bile duct and CCA cell lines have been analyzed.
This dataset contains expression array data for 32 patients of the screening cohort with 8 of them having paired normal tissue plus an additional relapse tumor/normal pair of one of those 8 patients and a patient only with normal tissue. Data was generated on a HumanHT-12 v4 Bead Array (Illumina) and is stored in IDAT file format.

  Dataset EGAD00010002596

• Whole genome sequencing of Sinonasal hemangiopericytoma and patient derived cell line model

  Solitary fibrous tumor/Hemangiopericytoma (SFT/HPC) is a rare subtype of soft tissue sarcoma associated with NAB2-STAT6 gene fusions. This study established and characterized a novel SFT/HPC patient-derived cell line called SFT-S1 using whole genome sequencing.

  Study EGAS50000000025

• Single-nucleus mRNA-Sequencing of prenatal and postnatal samples from the brain and its border regions

  Single-nucleus mRNA Sequencing of prenatal and postnatal samples from the brain and its border regions. Most samples were multiplexed with several samples run in one 10X reaction. A separate immune cell dataset was combined with published data from Braun et al 2023 and Yang et al 2021 integrated using harmony is included.

  Dataset EGAD50000000044

• Next generation sequencing on cardiac samples in Hungarian patients of dilated cardiomyopathy

  Heterozygous (HET) truncating mutations in the TTN gene (TTNtv) encoding the giant titin protein are the most common genetic cause of dilated cardiomyopathy (DCM). We investigated 127 clinically identified DCM human cardiac samples with targeted sequencing using the TruSight Cardio panel on an Illumina MiSeq system with a special focus on TTNtvs. This dataset belongs to the publication of Kellermayer, D et al. Truncated titin is integrated into the human dilated cardiomyopathic sarcomere

  Dataset EGAD50000000066

• RNAseq and ATACseq data, derived from HGSC cell lines pre- and post- treatment with an epigenetic compound

  These data consist of transcriptome and chromatin accessibility data (RNAseq and ATACseq respectively) derived from five High-Grade Serous Ovarian Carcinoma (HGSC) cell lines. HGSC lines included PEO1, PEO4, PEA2, OVCAR5 and OVCAR8. Samples were taken pre- and post-treatment with a novel epigenetic compound, HKMTi-1-005, which targets the activity of two histone methyltransferases, EZH2 and G9a.

  Dataset EGAD50000000064

• Arcagen – thoracic malignancies

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting the dataset that contain NGS files from rare thoracic malignancies (n=102)

  Dataset EGAD50000000168

• RNA-Seq data from tumor samples collected from 12 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates differentially expressed genes associated with the progression of translocation renal cell carcinoma (tRCC) by analyzing RNA-Seq data from 23 tumor samples obtained from 12 patients. The samples were collected from affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital and Children’s Medical Center. RNA isolated from tumor samples was sequenced using the Illumina platform.

  Dataset EGAD50000000172

• Multiomics characterisation of the response to stimulation in Long Covid patients

  We generated 10X, droplet-based paired snRNAseq+snATACseq (Multiome) of the response to P. Aeruginosa or RPMI in patients suffering from Long Covid. In total, we included 15 patients. Single nuclei libraries are genetically multiplexed across donors, genotype files are available to enable demultiplexing. Phenotype sheets provide information of pools/donors as well as donor phenotype.

  Dataset EGAD50000000203