4210 results for "*geneti*"

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• Genetic, epigenetic and fragmentomic profiling of plasma cell-free DNA in cancer patients

  The uploaded data includes sequencing of plasma cfDNA from a total of 33 cancer patients. Plasma cfDNA samples were sequenced for whole-genome sequencing analysis using double-stranded DNA libraries, and paired bisulfite sequencing using single-stranded DNA libraries. All sequencing data are of extracted plasma cfDNA from human patients, and sequenced using the Illumina NovaSeq6000 platform. Alignment was performed to the human genome (hg19). Lambda DNA was added to samples subjected to bisulfite sequencing for quality control.

  Dataset EGAD50000002912

• Genomic signatures of recent human adaptation to the Foraging-to-Farming Transition

  Human populations across multiple regions transitioned from hunting and foraging to sedentary farming societies starting ~10 to 12kya, narrowing dietary diversity and transforming relationships with animals and pathogens. To investigate how these ecological changes shaped human adaptations, we analyzed high-coverage whole-genome sequence data from 1,092 individuals practicing hunter-gatherer, agriculturalist, and pastoralist lifestyles across eight geographic regions spanning Africa, Asia, and Oceania. Collectively, our findings illuminate the genetic legacy of ecological changes associated with the foraging-to-farming transition and highlight the central role of non-coding regulatory variation in human adaptation.

  Study EGAS50000001991

• A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

  Abstract: Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cancer cell trajectory regionalizes tumours into distinct tissue niches and manifests in a conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to jointly map cancer cell states and clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

  Dataset EGAD00001015516

• Evaluation Of The Role Of Tumor Mutational Burden As A Prognostic Biomarker In Follicular Lymphoma

  In this study, we have evaluated TMB through a 409-gene panel in 135 patients with FL or transformed FL (tFL) at presentation. We analyzed the association of TMB with prognosis and specific genetic alterations recurrent in FL, with the goal of identifying molecular markers that could improve prognostic assessment.

  Study EGAS50000001448

• Integrated analysis of targeted DNAseq and RNAseq on peripheral blood mononuclear cells (PBMC) of hip replacement surgery patients

  Patient samples were collected from individuals undergoing elective femoral head replacement surgery. Eligible participants were aged >50 years, classified as otherwise healthy, and able to provide written informed consent. Exclusion criteria were trauma-related femoral hip replacement and any known hematological malignancy.

  Study EGAS50000001648

• Heterogeneous endocrine cell composition defines human islet functional phenotypes

  Phenotyping and genotyping initiatives within the Integrated Islet Distribution Program (IIDP), the largest source of human islets for research in the U.S., provide standardized assessment of islet preparations distributed to researchers and enable the integration of multiple data types. Data from islets of the first 299 organ donors without diabetes analyzed using this pipeline highlights substantial heterogeneity in islet cell composition associated with hormone secretory traits, sex, reported race and ethnicity, genetically predicted ancestry, and genetic risk for type 2 diabetes (T2D). While α and β cell composition influenced insulin and glucagon secretory traits, the abundance of δ cells showed the strongest association with insulin secretion and was also associated with the genetic risk score (GRS) for T2D. These findings have important implications for understanding mechanisms underlying diabetes heterogeneity and islet dysfunction and may provide insight into strategies for personalized medicine and β cell replacement therapy.

  Study EGAS50000000697

• G4SCOPE identifies sequence features and epigenetic contexts of chromatin G-quadruplexes

  Cancer-associated epigenetic remodeling involves both DNA secondary structures and covalent cytosine modifications, yet predicting chromatin-constrained G-quadruplex (G4) folding remains challenging. Here, we develop G4SCOPE, a multi-task deep learning framework that predicts G4 occupancy and quantitative CUT&Tag signal directly from DNA sequence in isogenic human breast epithelial cells. Trained across wild-type, TP53-deficient, TP53/BRCA1-deficient and TP53/BRCA2-deficient backgrounds, the model integrates convolutional motif learning with transformer-based self-attention to capture local motifs and broader contextual sequence features without auxiliary epigenomic inputs. It distinguishes chromatin-associated G4s from GC-matched loci with in vitro G4-forming potential, captures quantitative variation in local CUT&Tag signal, and performs consistently across the four breast epithelial genotypes. Independent application in K562 cells further supports the portability of the framework to a distinct cellular context. Model-derived G4 representations also separate loci with distinct local 5-methylcytosine and 5-hydroxymethylcytosine profiles. Together, these results show that DNA sequence can predict chromatin G4 occupancy and quantitative CUT&Tag signal, and that the learned representations reflect differences in local cytosine-modification state.

  Study EGAS50000002094

• scD&D-seq of mobilized PBMC from a healthy individual having IDH2 R140Q CHIP

  Single-cell D&D-seq profiles from a mobilized CHIP (clonal hematopoiesis of indeterminate potential) PBMC sample (2 replicates) co-profiled with IDH2 R140Q GoT-ChA.

  Study EGAS50000001590

• scD&D Multiome of GATA1 regulatory network dynamics during erythropoiesis

  Ex vivo differentiation experiments of human CD34+ hematopoietic stem and progenitor cells and profiled cells along the erythropoietic trajectory at multiple timepoints (differentiation days 0, 5, 8, 18, and 24), for simultaneous assessment of transcriptome, chromatin accessibility and GATA1 binding

  Study EGAS50000001606

• SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population

  The SweGen project represents a comprehensive map of genetic variation in the Swedish population. This Study contains four datasets where the first one contains whole-genome sequencing data in CRAM-format for 942 individuals from the Swedish Twin Registry, the second one contains genetic variant frequencies in VCF-format for the same individuals, the third one contains whole-genome sequencing data in CRAM-format for 58 individuals from the Northern Sweden Population Health Study and the fourth one contains genetic variant frequencies in VCF-format for the same individuals. The data, serving as a reference dataset, is a valuable and national resource for genetics research and clinical diagnostics and can be used, for instance, to increase understanding of both rare and complex disorders. It also serves as an international resource for larger studies of human population genetics.

  Study EGAS50000000906