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• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Study EGAS00001004538

• 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

• Genome-wide association study identifies common variants associated with breast cancer in South African Black women

  Genome-wide association studies (GWAS) have characterized the contribution of common variants to the development of breast cancer (BC) in populations of European descent, however no GWAS have been reported in resident African populations. This GWAS included 2,485 resident African BC cases and 1,101 population matched controls. Two risk loci were identified, one on chromosome 15, located between UNC13C and RAB27A (rs7181788, p = 1.01x10-08) and a second in an intron of USP22 on chromosome 17 (rs899342, p = 4.62x10-08). RAB27A is a member of the RAS oncogene family associated with invasive growth and metastasis, and UNC13C is involved in tumor progression. The rs899342 SNP is an eQTL for expression of USP22 in a wide range of tissues. Several genome-wide significant signals were detected in a hormone receptor subtype analysis. Comparison of a meta-analysis of other African ancestry BC GWAS datasets predominantly of West African ancestry with our South African data showed limited evidence of shared risk loci, suggesting potential heterogeneity in genetic risk factors for breast cancer in different African populations. A European ancestry derived polygenic risk model for BC was applied to our data, explaining only 0.79% of variance, with an area under the curve of 0.56. This study identified genetic risk factors for BC in a resident African population, but larger studies in this and other African populations are needed to further define the contribution of common variants to BC risk.

  Study EGAS00001008032

• Kibbutzim Family study

  The Kibbutzim Family Study (KFS) aimed to investigate the environmental and genetic determinants of cardiometabolic traits (phenotype is LDL-C)

  Dataset EGAD00010001551

• RBWES

  Retinoblastoma is a rare childhood cancer of the retina. We studied retinoblastoma by Whole-Exome-Sequencing.

  Dataset EGAD00001007591

• Amplicon sequencing data from organoids of colorectal cancer patients.

  The dataset includes 13 bam files. Each bam file is a different colorectal cancer patient organoid.

  Dataset EGAD00001004313

• Average_5mC_Roadmap_histoneMarks

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002422

• Average_5hmC_Roadmap_histoneMarks

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics histone mark annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002417

• Average_5hmC_Roadmap_chromatinStates

  Average hydroxymethylation levels based on megalodon calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002414

• Average_5mC_Roadmap_chromatinStates

  Average methylation levels based on nanopolish calls from Roadmap Epigenomics chromatin state annotations using different cell types. Data from 8 individuals at different time points.

  Dataset EGAD00010002413

• Reference epigenome KNIH009 mRNA-seq data generated from KEP study

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175

• Reference epigenome KNIH007 mRNA-seq data generated from KEP study

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

• Reference epigenome SMC09 h3k9me3 ChIP-Seq data generated from KEP study

  SMC09 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007354

• Mixing Sample Low Grade Glioma Gender Male, Age , 34,44

  Mixed Sample of scRNA-Seq primary low grade glioma. Genders: Male, Age: 34, 44.

  Dataset EGAD00001006018

• Reference epigenome SMC04 h3k9me3 ChIP-Seq data generated from KEP study

  SMC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007330

• Reference epigenome SMC01 h3k9me3 ChIP-Seq data generated from KEP study

  SMC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007312

• Reference epigenome SMC02 h3k36me3 ChIP-Seq data generated from KEP study

  SMC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007315

• Reference epigenome ADMSC01 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007356

• Reference epigenome ADMSC06 input ChIP-Seq data generated from KEP study

  ADMSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007387

• Reference epigenome SMC08 h3k27ac ChIP-Seq data generated from KEP study

  SMC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007344

• Reference epigenome ADMSC01 input ChIP-Seq data generated from KEP study

  ADMSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007361

• Reference epigenome ADMSC02 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007363

• Reference epigenome ADMSC06 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007385

• Reference epigenome ADMSC06 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007386

• Reference epigenome ADMSC08 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007398