0dcffad2-f77f-4e9e-a178-6d594b3d3279 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001011305 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:As part of the study "Systematic analysis of paralogous regions in 41,755 exomes uncovers clinically relevant variation" we wanted to identiffy the SNVs that are located in STRC and which ones in STRCP1. For this we applied targeted long-read sequencing.
| EGAS00001007513 | EGA
Project description:Effective molecular diagnosis of congenital diseases hinges on comprehensive genomic analysis, traditionally reliant on various methodologies specific to each variant type — whole exome or genome sequencing for single nucleotide variants (SNVs), array CGH for copy-number variants (CNVs), and microscopy for structural variants (SVs). We introduce a novel, integrative approach combining exome sequencing with chromosome conformation capture, termed Exo-C. This method enables the concurrent identification of SNVs in clinically relevant genes and SVs across the genome and allows analysis of heterozygous and mosaic carriers. Enhanced with targeted long-read sequencing, Exo-C evolves into a cost-efficient solution capable of resolving complex SVs at base-pair accuracy. Through several case studies, we demonstrate how Exo-C's multifaceted application can effectively uncover diverse causative variants and elucidate disease mechanisms in patients with rare disorders.
2024-01-24 | GSE253950 | GEO
Project description:Statins prevent cardiovascular disease via their salutary function as inhibitors of cholesterol biosynthesis and mediators of pleiotropic effects on the cardiovascular system. The current study focuses on the class effect of statins on the transcriptome of human iPSC-derived cardiomyocytes (iPSC-CMs), applied at serum peak concentrations. We report a comprehensive transcriptomic analysis of iPSC-CMs derived from four healthy donors and different differentiation batches following treatment with fluvastatin, simvastatin, atorvastatin, and lovastatin. Our data display dynamic transcriptional networks and reveal a statin-induced molecular signature in iPSC-CMs independent of genetic background and technical variability. Finally, in-depth pathway enrichment analysis uncovers that all statins affect mainly metabolic properties of iPSC-CMs and particularly the regulation of cholesterol biosynthesis and fatty acid metabolism. Our study provides a global insight into the cardiomyocyte effects of statins revealing novel aspects of their role on cardiomyocyte metabolic regulation, when applied at clinically relevant concentrations.
2020-04-29 | GSE113546 | GEO
Project description:<p>Translating whole exome sequencing (WES) for prospective clinical use may impact the care of cancer patients; however, multiple innovations are necessary for clinical implementation. These include: (1) rapid and robust WES from formalin-fixed paraffin embedded (FFPE) tumor tissue, (2) analytical output similar to data from frozen samples, and (3) clinical interpretation of WES data for prospective use. In this study, we describe a prospective clinical WES platform for archival FFPE tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a "long tail" of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15/16 patients. Overall, this methodology may inform the widespread implementation of precision cancer medicine.</p>
| phs000694 | dbGaP
Project description:Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling processes. While thousands of mutations have been identified, mostly by genome-wide sequencing, systematic interpretation of their role in cancer and impact on cellular information processing is presently missing. Here, we propose a computational approach (ReKINect) to identify mutations attacking signaling networks. We demonstrate six types of network-attacking mutations (NAMs) including changes in kinase modulation, network rewiring as well as the genesis and extinction of specific phosphorylation sites. Through global, quantitative analysis of the exomes and (phospho-)proteomes of five ovarian cancer cell lines we identify and validate numerous NAMs. Finally, we explore the entire cancer genome repertoire and predict hundreds of NAMs affecting kinase and SH2 driven signaling. Our approach is scalable with the complexity of cancer genomes and cell signaling, and can be readily applied in personalized precision medicine.
2016-07-08 | PXD000901 | Pride