Integrating omics data from phenotypically-related genodermatoses. A Cytoscape approach using biological networks
| Autor: | Piedra Zayas, Hermes |
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| Přispěvatelé: | León Canseco, Carlos, Universidad Carlos III de Madrid. Departamento de Bioingeniería e Ingeniería Aeroespacial |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname |
| Popis: | The ongoing advance of high-throughput sequencing technologies is bringing to the biomedical research community the opportunity to disclose relatively uncharted and poorly addressed domains in genetic disorders. Specifically, this project aims to shed new light on the molecular mechanisms of three rare skin diseases: Recessive Dystrophic Epidermolysis Bullosa (RDEB), Kindler Syndrome (KS) and Xeroderma pigmentosum type C (XPC). To accomplish this, biological network construction is leveraged herein, by providing a convenient approach to integrate and downstream analyze molecular omics data obtained from the comparison of these three genodermatoses (RDEB, KS & XPC) against healthy control samples. Concretely, microRNAs, RNAs and protein datasets are conjointly combined in the form of graphs whose structure and arrangement can be analyzed. On this basis, and upon computational procedures, the representation of high-throughput omics data across networks serves for both a topological and functional characterization of the molecular entities embedded within the graphs. Cytoscape software harbors the toolkits needed to exploit the massive omics information presented in this work, closely operating with online ontologies containing crucial annotations on the molecular entities under the network conglomerates. Cytoscape platform is going to carry out the bioinformatics computational endeavours, conducting then to new insights where common mechanisms and candidate biomarkers shared by the three genodermatoses will be highlighted. In this manner, STRING, BiNGO and ClueGO (Cytoscape plug-ins) will assist in the finding of enriched functions (such as “cell adhesions” and “epidermal growth factor signaling”), whereas the topological analysis will rely on STRING and NetworkAnalyzer, following the principles of graph theory to identify candidate molecules like TFAP2A and L1CAM. With the aid of manual curations, these two approaches will stand for a narrowing-down strategy from which biological interpretations are obtained. Ingeniería Biomédica |
| Databáze: | OpenAIRE |
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