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• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  CITE-Seq (cellular indexing of transcriptomes and epitopes) of 51 lymph node (LN) samples from including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL), and marginal zone lymphomas (MZL, n = 11), in addition to non-malignant reactive lymph nodes (rLN, n = 8). Of the malignant LN samples, 20 were collected at the time of initial diagnosis and 23 were from patients who had previously undergone one or more lines of systemic treatment. Relapse samples were collected at least 3 months after cessation of systemic treatment. This dataset also includes 5 prime RNA sequencing and immune receptor sequencing (10X Genomics) for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL (GCB) and 2 MZL).

  Dataset EGAD50000000497

• Single nuclei ATAC-Seq data from the human ganglionic eminences

  Data generated through single nuclei ATAC sequencing on whole ganglionic eminences from 3 human fetuses (two of 16 and one of 17 gestational weeks). Tissue was acquired from the MRC-Wellcome Trust Human Developmental Biology Resource with ethical approval. snATAC-Seq libraries were prepared from ~8,000 nuclei per sample using Chromium Next GEM Single Cell ATAC (v1.1) reagents (10X Genomics). Quality control of libraries was performed using the Agilent 5200 Fragment Analyzer before sequencing on an Illumina NovaSeq 6000 to a depth of at least 617 million read pairs per library. Raw sequencing data were converted into FASTQ files. For a full description of data generation, please see Cameron et al, Schizophrenia Bulletin 2024, https://doi.org/10.1093/schbul/sbae083. Please note that 10X generated BAM files, rather than FASTQ files, have been uploded. FASTQ files can be regenerated using the 10X Genomics bamtofastq tool. https://support.10xgenomics.com/docs/bamtofastq

  Dataset EGAD50000000601

• EpiMatch_DNA_Methylation_Resource

  Genome-wide DNA Methylation Data from Illumina HumanMethylationEPIC arrays for whole blood samples from 403 healthy individuals. Additional raw data (IDAT files) and associated phenotype information are available for all individuals included in this study (n=570) directly from CIBMTR. Data are available under controlled access release upon reasonable request and execution of a data use agreement. Requests should be submitted to CIBMTR at info-request@mcw.edu and include the study reference IB17-04

  Dataset EGAD00010002283

• EGAD00010000630

  The TEENAGE study target population comprised adolescent students aged 13 to 15 years attending the first three classes of public secondary schools located in the wider Athens area of Attica.

  Dataset EGAD00010000630

• Catalogue Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue What is in the EGA? The European Genome-phenome Archive (EGA) overview The EGA archives and distributes the results of several types of studies. Such studies include genome-wide association studies (GWAS), different purpose sequencing and molecular diagnosis assays among others. Moreover, these studies target many different kind of diseases from cancer to neurodegenerative alterations. We summarised all this data in the charts below. The statistics are computed daily based on an updated list of studies. Each pie chart depicts a different aspect of the studies: which disease type was studied, which type of sampling method was used and what was the scope of the genomic analysis. Number of studies per disease type Studies in the EGA by disease barHorizontalChart('disease-graph', 'https://stats.ega-archive.org/catalog/by_disease') Number of studies per technology type Studies in the EGA by technology barHorizontalChart('technology-graph', '/stats/studies/technology', 'Include dbGaP') Number of studies per sample type Studies in the EGA by samples barChart('samples-graph', '/stats/studies/sample-types', 'Include dbGaP')

  Documentation about/statistics/catalogue

• Genome-wide association study of response to warfarin in a UK prospective cohort

  Genome-wide association study of Warfarin dosing in 714 British, recruited in Liverpool and Birmingham. Three phenotypes were investigated: warfarin mean dose, warfarin mean stable dose, and INR over 4 during first week of treatment. Stability was defined by at least three INR measurements within target in a three week period. The samples were genotyped on the Illumina 610K chip and imputed using Impute v2.1, with a combined filtered set composed of HapMap 3 release 2 and 1000 genomes pilot 1 CEU.

  Study EGAS00001001130

• Genetic ancestry contributes to somatic mutations in lung cancers from admixed Latin American populations

  Inherited lung cancer risk, particularly in non-smokers, is poorly understood. Genomic and ancestry analysis of 1,153 lung cancers from Latin America revealed striking associations between Native American ancestry and their somatic landscape, including tumor mutational burden (TMB), and specific driver mutations in EGFR, KRAS, and STK11. A local Native American ancestry risk score predicted EGFR and KRAS mutation frequency more strongly than global ancestry, suggesting that germline genetics (rather than environmental exposure) underlie these disparities.

  Study EGAS00001004752

• Constit Genome Sequencing

  WGS blood data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 67 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 100bp paired-end protocol to a minimum mean coverage of 30x.

  Dataset EGAD00001003190

• Leiomyosarcoma Cancer Genome Sequencing

  WGS cancer data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 100bp paired-end protocol to a minimum mean coverage of 50x.

  Dataset EGAD00001003191

• Cancer Genomics of the Kidney

  The aim of CAGEKID is to carry out comprehensive detection of DNA markers for conventional (clear cell) renal carcinoma. The project includes complete analysis of somatic and constitutional DNA variation, methylation patterns and expression in a large number of constitutional/tumor pairs. CAGEKID is a part of the International Cancer Genome Consortium, ICGC.

  Dataset EGAD00001004018