Accepted_test

The primary goal of the research is to develop and implement a highly efficient Python-based program to analyze SNPs, which are crucial in identifying hereditary diseases. This new software aims to significantly streamline the SNP analysis process by integrating data from various genetic databases such as dbSNP, SNPedia, 1000 Genomes Project, and Ensembl. The methods employed include data extraction from source files, identification of known genetic variations, clinical significance analysis, and frequency evaluation of variations across populations.The software automates SNP information extraction and annotation, simplifying the analysis for users with limited bioinformatics expertise. It provides customizable reports with visual elements such as charts and diagrams, which simplify the interpretation of genetic data for researchers and medical geneticists. The intuitive interface is designed to be user-friendly, reducing the barrier for entry for those without extensive bioinformatics training. The results of this study show that the developed program significantly reduces the time and complexity involved in SNP analysis compared to existing tools, thereby enhancing the diagnostic and treatment methodologies in medical genetics. Overall, this work contributes to the advancement of medical genetics by improving the integration of SNP analysis into clinical workflows, making genetic testing more accessible and interpretable in clinical settings. The program enhances genetic diagnostics and treatment, ultimately advancing medical genetics by integrating SNP analysis into clinical workflows, making genetic testing more accessible and interpretable in clinical settings.