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• Multiple esclerosis and mixed microbial infections

  This Project deals with the sequencing of ITS1 region, which is highly variable both in length and in nucleotide sequence for different yeast using yeast-specific primers ITS1 and ITS2. A total of 19 samples involving different brain regions from patients with different conditions were analysed. Of these, 10 are controls-healthy patients and 9 multiple sclerosis (MS) patients.

  Study EGAS00001002957

• Next Generation Children project - WGS study of patients in NICU and PICU and their families

  With growing evidence that rare single gene disorders present in the neonatal period, there is a need for rapid, systematic, and comprehensive genomic diagnoses in ICUs to assist acute and long-term clinical decisions. This study aimed to identify genetic conditions in neonatal (NICU) and paediatric (PICU) intensive care populations. Whole genome sequencing data.

  Study EGAS00001003002

• FAM50A_Disruption_in_TOV21G_Cells___RNAseq

  In this project we aimed to assess the transcriptional consequences of disrupting FAM50A in TOV21G cells in vivo. To do this we generated a dox inducible FAM50A gRNA allele such that we could conditionally inactivate FAM50A by feeding mice a diet containing doxycycline. Tumour masses were harvested from mice when they reached the ethical limit and RNA was extracted for RNA-Seq analysis.

  Study EGAS00001004156

• Single_cell_resolution_of_human_CNV_body_map

  12 tissues from the warm autopsy are selected for this project. Using 10X Chromium technology we will generate ~1000 single cell/nulei genomic libraries per tissue. Each tissue will be whole genome sequenced (~2 lanes per 1000 cells) on hiseq X10. per single cell we will generate CNV profile and we investigate the level of genomic heterogenity with in tissue and across different tissues.

  Study EGAS00001003162

• Analysis of exonic somatic variants in light-chain amyloidosis (ALA) and ALA concomitant with multiple myeloma

  We aim to provide the very first insight into ALA+MM molecular profiles and compare the results with ALA and with MM. Our detailed study of ALA and ALA+MM represents an important step towards improved understanding of their genetic and transcriptomic background, which is a prerequisite for development of optimal treatment strategies in the future.

  Study EGAS00001004214

• whole-genome sequencing of gastric cancer

  Gastric cancer is one of the most common cancers. Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer tumorigenesis. Here, we analyzed structural variations and mutational signatures via whole-genome sequencing of 168 gastric cancer samples. Our data demonstrates diverse models of complex structural variations operative in GC, which lead to high-level amplification of oncogenes.

  Study EGAS00001003512

• RNA data for MMML (EGAS00001002422)

  RNA sequencing data for MMML (3 tumor samples and 1 gcbcell)

  Dataset EGAD00001003285

• WGS data for MMML (EGAS00001002422)

  Whole genome sequencing data for MMML (7 tumors and 8 controls)

  Dataset EGAD00001003286

• RNA-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  RNA-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Dataset EGAD00001003433

• A new subgroup of hepatocellular adenomas with sonic hedgehog pathway activation

  Using sequencing and gene expression analyses, we identified a subgroup of HCA characterized by fusion of the INHBE and GLI1 genes and activation of sonic hedgehog pathway. Molecular subtypes of HCAs associated with different patients’ risk factors for HCA, disease progression, and pathology features of tumors. This classification system might be used to select treatment strategies for patients with HCA. Related Publication: Molecular Classification of Hepatocellular Adenoma Associates With Risk Factors, Bleeding, and Malignant Transformation Nault, Jean-CharlesLaurent, Christophe et al. Gastroenterology , Volume 152 , Issue 4 , 880 - 894.e6 http://dx.doi.org/10.1053/j.gastro.2016.11.042

  Dataset EGAD00001003092