PGVWeb: Aplicación web para la priorización de variantes genéticas en un contexto clínico
Next-generation sequencing has led to a breakthrough in clinical diagnosis. Detection of genetic variations allows to determine the cause of a pathology of large number of cases. However, the huge amount of genetic variants detected difficult the task of analysts. Therefore, the design of a simple a...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/127407 |
| Acceso en línea: | http://hdl.handle.net/10609/127407 |
| Access Level: | acceso abierto |
| Palabra clave: | variante genética next-generation sequencing (ngs) herramienta web genetic variant web tool variant genètica next-generation sequencing (NGS) eina web Bioinformatics -- TFM Bioinformàtica -- TFM Bioinformática -- TFM |
| Sumario: | Next-generation sequencing has led to a breakthrough in clinical diagnosis. Detection of genetic variations allows to determine the cause of a pathology of large number of cases. However, the huge amount of genetic variants detected difficult the task of analysts. Therefore, the design of a simple and intuitive graphical interface that facilitates the operation and visualization of variants becomes essential. Another indispensable aspect in clinical diagnosis is the prioritization of candidate variants, that is, prioritizing those variants most likely to be the cause of the pathology. In this project we have developed a web application in R programming language, which allows you to import and visualize, interactively, all the genetic variants detected by sequencing. In addition, filters and prioritization algorithms have been added to show the variants that are more likely to be the cause of pathology. Various R packages have been used to carry out this project, including the Shiny package, used to create an interactive user interface. Different databases have also been used such as ClinVar, HPO and OMIM that make it possible to generate specific filters and prioritize based on the patient's phenotype. As a result of this work, a web application has been developed that combines simplicity of use and great flexibility in order to facilitate and focus the analysis on disease-causing variants. |
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