Deep Learning Algorithms in DNA Sequencing: Transforming Genetic Testing and Analysis

Deep learning algorithms have revolutionized DNA sequencing, providing unprecedented accuracy and efficiency in genetic testing and analysis. Traditional methods struggle with the vast complexity and volume of genomic data, leading to high costs and extended processing times. To address these challenges, we developed a novel deep learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN component captures spatial features from DNA sequences, while the LSTM component handles sequential dependencies, ensuring comprehensive analysis. Our model was trained on a dataset of 10 million sequences and validated using crossvalidation techniques. We incorporated model quantization and pruning to optimize performance and reduce computational costs. The proposed framework achieved up to 98.7% accuracy in identifying genetic mutations, outperforming traditional methods by 12%, and reduced processing times by 50%, with an average rate of $\mathbf{2, 0 0 0}$ sequences per second. Comparative analysis showed a 15% improvement in precision and a 20% reduction in false positives compared to existing models. This study presents a transformative approach to DNA sequencing, leveraging advanced deep learning algorithms to significantly enhance genetic testing and analysis, providing faster, more accurate, and cost-effective solutions for handling genomic data complexities. Future work will focus on further refining the model and exploring its application in personalized medicine and real-time genetic analysis, underscoring the potential of deep learning to revolutionize genetic research.