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http://hdl.handle.net/10609/120746
Title: | Diseño de una herramienta web para la priorización de variantes genómicas detectadas mediante secuenciación masiva |
Author: | Martínez Rubio, Roser |
Tutor: | Maynou Fernández, Joan |
Others: | Maceira, Marc Canovas Izquierdo, Javier Luis |
Abstract: | New massive sequencing technologies allow a large amount of information to be obtained quickly and cheaply in the form of nucleic acid sequences. This fact, which a priori is an advantage, in turn entails the inconvenience of the large volume of information to be processed. In this aspect, Bioinformatics plays a fundamental role making tools that allow detecting changes with respect to a reference genome and establishing which of these changes are related to the disease. This second point is where we will focus. Therefore, the purpose of our work will be to carry out an application that allows geneticists to filter and prioritize in a simple and friendly way the detected genomic variants in order to determine which one or which of them are responsible for the observed phenotypes. To carry it out we will work with the R programming language on files in .vcf format that contain the annotated information on the variants of the individuals to be studied. The result that we have obtained is an application that allows us to select those variants that are found in certain genes or related to certain phenotypes (using HPO terms) and that meet certain requirements chosen by the user (geneticist), such as the type of inheritance , the allelic frequency or the clinical significance of the variant. |
Keywords: | variants of classification variants classification pathogenicity NGS HPO variants prioritization |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jul-2020 |
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 | Size | Format | |
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rmartinezrubiTFM0620memoria.pdf | Memoria del TFM | 2,94 MB | Adobe PDF | View/Open |
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