Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/138846
Title: Desarrollo de una herramienta web para la clasificación de variantes genéticas
Author: Astete Frugoni, Joaquín Américo
Tutor: Castellanos Pérez, Elisabeth
Others: Ventura, Carles  
Abstract: With the advent of Next-Generation Sequencing (NGS) techniques, enormous amounts of genomic data are being obtained, which has allowed a great advance in clinical genetic diagnosis. However, the large number of genetic variations that are detected makes the work of analysts difficult. For the clinical classification of variants, the ACMG has agreed on classification guidelines, taking into account different criteria (population frequency of the variant, type of mutation, etc.). The manual application of such classification is usually a cumbersome and time-consuming activity. In the present work we have developed, in a first phase, a script in R language that performs a semi-automatic classification of genetic variants using the new recommendations and revisions of the ACMG guidelines. In a second phase, this script has been adapted to a Shiny environment that allows the user to enter criteria manually, as well as to modify the calculated criteria. Finally, a table is obtained with the classification obtained and data related to the variant. For the development of the tool we have mainly used the myvariant and Shiny packages, as well as data from OMIM, GWAS and ClinVar. As a result of the work, an intuitive and easy-to-use web tool has been developed, which facilitates the work of clinicians and researchers who need to obtain classifications of genetic variants.
Keywords: bioinformatics
biostatistics
genetics
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 20-Dec-2021
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

Files in This Item:
File Description SizeFormat 
jastete0TFM0122memoria.pdfMemoria del TFM4,88 MBAdobe PDFThumbnail
View/Open