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• How are we funded?

  How are we funded? The EGA is provided infrastructure, administrative support, advisory input, and other relevant and necessary aspects by: CRG The EGA at the CRG is funded and supported by “La Caixa Foundation”, The Generalitat de Catalunya (Catalan Government), the Spanish Ministerio de Asuntos Económicos y Transformación Digital (Spanish Government) and the Instituto de Salud Carlos III. The Generalitat de Catalunya contributes with funding to the CRG and to the Barcelona Supercomputing Centre (BSC). The Barcelona Supercomputing Center is decisively contributing to EGA by providing the required compute, storage and networking resources, as well as key personnel for shared operations of EGA infrastructure. EMBL-EBI As part of the European Molecular Biology Laboratory (EMBL), the EGA at EMBL’s European Bioinformatics Institute receives funding from the governments of EMBL’s member states. The UK government, via UK Research and Innovation, also continues to support EMBL-EBI technical infrastructure, with EMBL supporting operational costs. A major source of funding for the EGA both at CRG and EMBL-EBI are competitive projects.The EGA resource continues to be funded through strategic engagement in collaborative partnerships and projects. Please see our projects page for more information. Other funders include the European Commission, the US National Institutes of Health, the Wellcome Trust, UK Research Councils, and our Industry Programme partners. Last updated on October, 2024.

  Documentation about/projects-and-funders/funders

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the regulation of gene expression in immune cell subsets and its contribution to autoimmune diseases, Whole blood and various immune cell subsets from 119 Systemic Lupus Erythematosus (SLE), 65 Dermatomyositis & Polymyositis, 67 Systemic Sclerosis, 19 Mixed Connective Tissue Disease, 18 Sjogren's syndrome, 24 Rheumatoid Arthritis, 23 Behcet's disease, 18 Adult Onset Still���s Disease, 25 ANCA-associated Vasculitis, 16 Takayasu���s Arteritis and 141 healthy controls (C) were collected (Naive_CD4, Mem_CD4, Fr._I_nTreg, Fr._II_eTreg, Fr._III_T, Th1, Th2, Th17, Tfh, NK, Naive_CD8, Mem_CD8, EM_CD8, CM_CD8, TEMRA_CD8, Naive_B, USM_B, SM_B, DN_B, Plasmablast, CL_Mono (or CD16n_Mono), CD16p_Mono, Int_Mono, NC_Mono, mDC, pDC, LDG, Neu). Whole genome sequencing was performed with whole blood samples. RNA-seq was performed with each immune cell subset samples. After filtering and normalization of the gene expression data, eQTL analysis was performed in each immune cell type. 15 immune cell subsets ATAC-seq was also performed in 8 SLE and HCs, each.

  Study JGAS000220

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007034

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007033

• Single_cell_characterization_of_T_cell_lymphoma_

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas.

  Study EGAS00001005750

• 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

• A Pilot Study to Evaluate Tissue- and Plasma-based DNA Driver Mutations in a Cohort of Patients with Pancreatic Intraductal Papillary Mucinous Neoplasms

  This is a retrospective study aimed at evaluating concordance between intraductal papillary mucinous neoplasm (IPMN) tissue and patient-matched plasma circulating tumor DNA (ctDNA) using a targeted sequencing approach. Archival solid tissue genomic DNA was available for 46 patient FFPE blocks patient-matched plasma ctDNA was available from 15 of those patients. All tissues were obtained pre-treatment from patients with an IPMN diagnosis and subjected to Qiagen custom next generation sequencing panel.

  Study phs003043

• Characterization of the Gut-Associated Microbiome in Inflammatory Pouch Complications Following Ileal Pouch-Anal Anastomosis

  This study involved evaluation of the tissue associated microbiome of the ileal pouch following surgery for ulcerative colitis (UC) or familial adenomatous polyposis (FAP). Individuals were recruited, with biopsies taken from the ileal pouch and the pre-pouch ileum, microbial DNA was extracted and sequenced using 454 pyrosequencing. Total bacterial community structure and abundance were evaluated to determine which changes were characteristic of inflammatory phenotypes including pouchitis (inflammation of the ileal pouch) and a Crohn's disease-like phenotype.

  Study phs000659

• Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

  The analysis of e2QTL allows for the identification of context-specific eQTL effects (Kim-Hellmuth et al. (2017), PMID: 28814792). To evaluate how inter-individual genetic variability influences the regulation of DNA damage-induced apoptosis, we performed e2QTL analysis of CD8+ T cells from 461 healthy European participants stimulated with high doses of 5 different carcinogens. These include Methyl-methanesulfonate (MMS), tert-butyl-hydroperoxide (TBOOH), benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE), 4-hydroxycyclophosphamide (HC) and UVC radiation.

  Study EGAS50000000666

• Multiregional single nuclei RNA-seq and WGS of prostate cancer

  This study investigates the clonal evolution and metastatic dissemination of prostate cancer using multiregional single-nuclei RNA sequencing (snRNA-seq) and low-pass whole-genome sequencing (WGS) data from 43 spatially distinct tumor areas in five patients with locally advanced prostate cancer, including both primary and regional lymph node samples. We employed the Chromium Next GEM Single Cell 3’ Kit (v3/3.1, 10x Genomics) for single-nuclei transcriptome profiling and constructed bulk WGS libraries using the NEBNext Ultra II FS DNA Library Prep Kit (New England Biolabs) from the same pool of nuclei extracted from each area. snRNA-seq data were aligned to the human genome (GRCh38-1.2.0_premrna) using 10x Genomics Cell Ranger v5.0.0, while WGS reads were aligned to the human reference genome (GRCh38) with BWA MEM, achieving an average sequencing depth of 0.3X for low-pass WGS and 16X after resequencing.

  Dataset EGAD50000001357