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• H3Africa - An integrated approach to the identification of genetic determinants of susceptibility to trypanosomiasis

  The over-arching aim of this network is to improve the health of people living in some of the poorest countries in the world that carry a disproportionate burden of infectious diseases. Despite their importance, the study of many tropical diseases has lagged behind that of diseases of developed countries. This network will redress the balance by performing high quality research into two neglected tropical diseases, human African Trypanosomiasis (HAT) and schistosomiasis. Our aim is to investigate the genetic determinants of the human reservoirs of HAT and schistosomiasis, filling an important knowledge gap, while extending and strengthening the underpinning infrastructure to deliver these scientific objectives in particular by developing the next generation of African geneticists.

  Study EGAS00001007173

• The genomic complexity of sporadic and inherited retinoblastoma with a matched orthotopic xenograft

  Retinoblastoma is a pediatric cancer of the developing retina. All retinoblastomas are believed to initiate with biallelic inactivation of the RB1 gene. To identify subsequent genetic lesions in retinoblastoma, we performed whole genome sequencing of tumor and normal DNA of 4 children with retinoblastoma and one matched orthotopic xenograft. Both alleles of RB1 were inactivated in the tumor samples. 3 of the patients had sporadic retinoblastoma and one patient had inherited retinoblastoma. Overall, there were few single nucleotide changes in coding regions of the genome and some of the tumors had few chromosomal lesions. There were very few new genetic lesions in the xenograft compared to the primary tumor. These data suggest that the genome in retinoblastoma is more stable than previously believed and there are relatively few recurrent genetic lesions in known cancer pathways other than the RB1 pathway.

  Study phs000352

• Genetic Basis of Isolated Arhinia and Bosma Arhinia Microphthalmia Syndrome

  The goal of this project is to determine the genetic etiology of congenital arhinia and Bosma arhinia microphthalmia syndrome through a combination of whole-exome, whole-genome, and targeted sequencing followed by in silico, in vitro, and in vivo functional modeling.

  Study phs001246

• Pediatric HGG WES and RNA-Seq

  Paediatric high-grade glioma (pHGG) is the leading cause of paediatric brain tumour death. Here we performed RNA and whole exome sequencing on pHGG samples to analyse transcriptomic and genetic alterations associated with this devastating disease, and to understand the relationship between them.

  Study EGAS00001005687

• Dac for "Development of a fully human glioblastoma-in-brain-spheroid model for accelerated translational research"

  This is the DAC for "Development of a fully human glioblastoma-in-brain-spheroid model for accelerated translational research" of Philipp Koch (philipp.koch@zi-mannheim.de), Frank Winkler (Frank.Winkler@med.uni-heidelberg.de), Moritz Mall( m.mall@dkfz-heidelberg.de)

  Dac EGAC50000000480

• Genome-wide gene expression analysis following CRISPRi of transposable elements

  human induced pluripotent stem cells constitutely expressing the CRISPRi machinery were transduced with gRNAs targeting genomic regions of transposable elements (TEs), and differentiated to neural progenitor cells. The aim of the study was to annotate the function of these TEs in the context of early neurodevelopmental processes.

  Study EGAS00001008265

• Detection and characterization of lung cancer using cell-free DNA fragmentomes

  Lung cancer remains the leading cause of cancer death world-wide, largely due to its late diagnosis. Non-invasive approaches for assessment of cell-free DNA (cfDNA) provide an opportunity for detection and intervention that may have broader accessibility than current imaging approaches. Using a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation, we examined a prospective study of 365 individuals at risk for lung cancer (Lung Cancer Diagnostic Study, LUCAS), including 129 individuals ultimately diagnosed with lung cancer and 236 individuals determined to not have lung cancer. We externally validated the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 predominantly early stage lung cancer patients. Combining fragmentation features with clinical risk factors and CEA levels followed by CT imaging detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites could be used to distinguish individuals with small cell lung cancer (SCLC) from those with non-small cell lung cancer (NSCLC) with high accuracy (AUC=0.98). Among individuals with lung cancer, a higher cfDNA fragmentation score was associated with tumor size and invasion, and represented an independent prognostic indicator of survival. These studies provide a facile approach for non-invasive detection of lung cancer and clinical management of this disease.

  Study EGAS00001005340

• ST dataset of subcortical MS

  Spatial transcriptomics from subcortical white matter controls and MS lesions, specifically chronic active (CA) and chronic inactive (CI), that paired with single-nuclei RNA-sequencing from the same samples , was used to identify spatial niches and key cell interactions driving inflammation and disease progression

  Study EGAS50000000353

• Genetic vulnerability of knockout cancer lines (2019-04-01)

  Genome-wide CRISPR/Cas9 library screen was performed in isogenic cell lines. Three biological replicates were used. Cells were harvested at initiation of screen and at specific time points following cell culture. Using this approach we aim to identify genes specifically important in cell survival in engineered cell lines. Please perform 72, instead of 36, PCR reactions as the screen performed at 200x coverage. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004881

• CONSERTING: integrating copy number analysis with structural variation detection

  We developed Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING), a novel algorithm for detecting somatic copy number alteration (CNA) using whole-genome sequencing (WGS) data. CONSERTING performs iterative analysis of segmentation by read depth change and localized structural variation detection to achieve high accuracy and sensitivity. Analysis of 43 pediatric and adult cancer genomes revealed novel oncogenic CNAs, complex re-arrangements and subclonal CNAs missed by alternative approaches. This study contains all data for "CONSERTING: an accurate method for detecting somatic DNA copy number alterations in whole genome sequencing data", which was previously split between EGAS00001001202 and EGAS00001001050.

  Study EGAS00001001202