Development of Advanced Bioinformatics Tools for Integrating Genomic Data and Enhancing Diagnosis of Rare Diseases

Abstract

Rare diseases pose significant challenges in diagnosis and treatment due to their genetic complexity and the limited availability of comprehensive genomic data. Current bioinformatics tools often struggle with accurately detecting rare mutations and integrating diverse genomic and clinical data, leading to delays in diagnosis and suboptimal patient care. This research proposes the development of an advanced bioinformatics pipeline aimed at enhancing the accuracy of mutation detection, integrating genomic, phenotypic, and clinical data, and providing a user-friendly interface for clinicians. The pipeline uses machine learning algorithms for improved mutation calling and data integration techniques to correlate genetic variants with clinical outcomes. The tool was evaluated on multiple rare disease datasets, demonstrating significant improvements in diagnostic accuracy and efficiency. With precision and recall rates of 92% and 88%, respectively, and a 40% reduction in diagnostic time, this approach promises to revolutionize rare disease diagnostics by facilitating faster, more accurate diagnoses and personalized treatment options.