Genome Analysis at Scale: Leveraging HPC for AI-Driven Genomics Research

The examination of genomes is essential to the advancement of biology, genetics, and customized medicine. A viable strategy for accelerating genomics research is to make use of high-performance computing (HPC) and artificial intelligence (AI). We provide a new approach in this work, “Genome Analysis at Scale: Leveraging HPC for AI-Driven Genomics Research,” and compare its effectiveness with a number of established genomics techniques. To demonstrate the higher performance of our approach, we evaluate many important criteria such as variant calling accuracy, scalability, and data processing speed. The suggested approach uses parallelized analysis, AI-driven variant calling, and simplified data preprocessing-all within the context of high-performance computing. Our results show that the suggested technique outperforms conventional methods in terms of data processing speed, with an average speed difference of 216 times. It is also 10% more accurate at variant calling, which makes it an even more precise tool for genomics study. Scalability, a crucial factor in the big data age, is significantly improved, as our approach beats out conventional approaches by six points on a scale of one to ten. Our suggested approach is unique in the context of real-time analysis as it facilitates quick clinical diagnosis, which is essential in the genomics sector. Additionally, data security and privacy are improved, resulting in an astounding 23 % improvement in the protection of private genetic data. Lastly, computing efficiency is increased; our approach shows a significant 37% improvement over conventional approaches. These findings highlight how our strategy has the ability to completely change genomics research and its applications.