Integration of Artificial Intelligence, Machine Learning and Deep Learning Techniques in Genomics: Review on Computational Perspectives for NGS Analysis of DNA and RNA Seq Data

In the current state of genomics and biomedical research, the utilization of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) have emerged as paradigm shifters. While traditional NGS DNA and RNA sequencing analysis pipelines have been sound in decoding genetic information, the sequencing data's volume and complexity have surged. There is a demand for more efficient and accurate methods of analysis. This has led to dependency on AI/ML and DL approaches. This paper highlights these tool approaches to ease combat the limitations and generate better results, with the help of pipeline automation and integration of these tools into the NGS DNA and RNA-seq pipeline we can improve the quality of research as large data sets can be processed using Deep Learning tools. Automation helps reduce labor-intensive tasks and helps researchers to focus on other frontiers of research. In the traditional pipeline all tasks from quality check to the variant identification in the case of SNP detection take a huge amount of computational time and manually the researcher has to input codes to prevent manual human errors, but with the power of automation, we can run the whole process in comparatively lesser time and smoother as the automated pipeline can run for multiple files instead of the one single file observed in the traditional pipeline. In conclusion, this review paper sheds light on the transformative impact of DL's integration into traditional pipelines and its role in optimizing computational time. Additionally, it highlights the growing importance of AI-driven solutions in advancing genomics research and enabling data-intensive biomedical applications.