23217 results for "ega"

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• Amplicon sequencing libraries from the study "Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study".

  Amplicon sequencing libraries from the study "Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study". These are Illumina amplicon deep sequencing libraries (n = 118) to validate somatic predictions made in the whole genome sequencing libraries. Specifically, there are 72 tumor libraries and 46 normal libraries. Some patients may have multiple amplicon libraries sequenced.

  Dataset EGAD00001002896

• Colorectal Adenoma Gene Screen

  A pilot to establish the feasability of using a custom Agilent targeted pulldown of 110 genes implicated in colorectal tumourigensis to sequence for driver mutations in a set of 30 FFPE colorectal adenomas. If successful, we propose to sequence an additional 350 adenomas as part of a MRC research study in order to define the pattern of driver mutations across the spectrum of pathological subtypes including coventional adenomas, serrated adenomas and hyperplastic polyps

  Dataset EGAD00001001879

• Genome and transcriptome sequence data from a metastatic carcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic carcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002983

• Mutational_burden_in_oesophagus_following_chemotherapy_and_radiotherapy_treatment

  We wish to assess the changes in signature and mutational burden in patients who have undergone either chemotherapy or chemotherapy + radiotherapy. Using duplex sequencing will assess the mutational landscape in oesophageal epithelia in patients who have undergone these treatment regimes.

  Study EGAS00001005552

• IGH repertoire sequencing of GZMB+ and GZMB− B cells

  This dataset contains high-throughput sequencing data of immunoglobulin heavy chain (IGH) repertoires from sorted GZMB⁺ and GZMB⁻ B cell subsets. Samples were obtained from healthy donors (Healthy Volunteers, HVs) and drug-free tolerant kidney transplant recipients (TOLs). Libraries were generated from genomic DNA using targeted BIOMED-2 PCR amplification and sequenced on an Illumina MiSeq platform. The dataset enables comparative analysis of B cell clonality, diversity, and somatic hypermutation patterns.

  Dataset EGAD50000002452

• SCIMAP PILOT D21

  This dataset includes the RNA-seq and ATAC-seq fastq files, as well as the Cellranger ARC count table, for the first experiment with multimodal sequencing in the SCI-MAP project (1 sample). Here, human induced pluripotent stem cells underwent differentiation to chondrocytes for 21 days following an established protocol. Nuclei were isolated and sequenced in two modalities simultaneously; RNA and ATAC.

  Dataset EGAD50000001516

• mRNA profiling of CAFs and cancer cells in response to TGF-β and BMP signaling

  TGF-β and BMP signalings are important signaling pathways in the tumor microenvironment during cancer progression. This RNA-sequencing (62 fastq pairs) analysis explored how cancer-associated fibroblasts (CAFs) from human (19TT) and mice (Balbc A/B) respond to TGF-β, BMP2, or Gremlin antibody, as well as how cancer cells (66cl4) respond to Gremlin antibodies.

  Dataset EGAD50000001351

• SCA1 and control cerebral organoids

  3' RNA-sequencing data of cerebral organoids from controls and individuals with SCA1 was performed at day 31 and day 60 of organoid formation. Matched case-control cerebral organoids were used (Ctrl A+SCA1 A and Ctrl B+SCA1 B) as well as isogenic cerebral organoids, CRISPR/Cas9 gene-edited from Ctrl A (SCA1-/CAG and SCA1CAG/CAG). Biological triplicates were assessed for all lines at both time points.

  Dataset EGAD50000002473

• AHA Uganda Study RNAseq Dataset

  This dataset was generated as part of the AHA Uganda Study, an Acute Rheumatic Fever case-control prospective study conducted in Uganda. This dataset consists of raw fastqs from RNA sequencing for 266 samples (226 cases, 40 controls) from 162 individuals over five timepoints. RNA was extracted from Paxgene blood samples using the Sureselect HS2 library preparation kit. Sequencing was performed on Illumina NovaSeq 6000, yielding ~30-40 million raw paired reads per sample.

  Dataset EGAD00001015810

• WGS data from both single-cell and bulk samples

  This dataset contains the raw BAM/CRAM files used in the manuscript "Single-cell whole-genome sequencing reveals convergent evolution in Burkitt lymphoma" Steemers et al. (2026). This includes: - 332 PTA-based single-cell WGS samples (~15X depth) - 7 bulk tumour samples (~30X depth) with matched germline samples (~30X depth) - 14 bulk tumour samples seqeunced by the Princess Maxima Centre diagnostics department (~105X depth) with matched germline samples (~35X depth)

  Dataset EGAD50000002411

• cfDNA and CDX/PDX methylation profiling in SCLC

  We profiled the whole genome methylome of SCLC preclinical models and cfDNA samples. We assessed the sensitivity of cfDNA methylation profiling in detecting SCLC, especially at earlier stage of disease and the ability in discriminate among different SCLC molecular subtypes.

  Study EGAS00001005739

• Childhood cerebellar tumors mirror conserved fetal transcriptional programs

  We used single-cell transcriptomics to study >60,000 cells from the developing murine cerebellum, and show that different molecular subgroups of childhood cerebellar tumors mirror the transcription of cells from distinct, temporally restricted cerebellar lineages.

  Study EGAS00001003170

• 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

• 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