Orthology analysis and model organisms for the study of human genetic diseases through the development of a bioinformatics application

Identifier:  TFG:9367

Handle: https://hdl.handle.net/20.500.11797/TFG9367

Authors:  Vega Cuadros, Javier

Abstract:
This thesis explores the use of orthology relationships between human genes and model organisms to identify the best candidates for studying genetic diseases. Using bioinformatics tools and metrics such as InParanoid DIAMOND, the study aligns proteomes and evaluates sequence similarities through a Python-written program. This application downloads the proteomes of model organisms and the protein sequences of each selected disease from UniProt. Five model organisms were chosen based on the most used for laboratory studies. Six human genetic diseases were analyzed: three selected to demonstrate the program's correct functionality and three to propose solutions for diseases without current studies in model organisms. Once all sequences are obtained, an alignment is performed using InParanoid DIAMOND. Subsequently, the program evaluates the metrics of this gene-to-gene alignment method and determines which model organism is the closest to humans. The created program prompts the user to input the model organisms and the names of the diseases, extracting gene-to-gene metrics and global comparisons for each disease. These results have been analyzed and discussed to ensure that the outputs meet expectations and confirm that the program works properly. Finally, to assess the program's effectiveness, the best model organisms proposed by the created program are compared to other alternatives, such as Ensembl Compara. Finally, improvement proposals and long-term objectives for the application are suggested.