Abstract

 In the biomedical research areas of whole genome sequence (WGS) analysis, disease diagnosis, and medication discovery, Next Generation Sequencing (NGS) data are the most recent and popular trend. The use of NGS data has improved the analysis of infectious diseases, WGS, illness identification, and medication discovery. Although the amount of NGS data is massive, researchers have worked and are continuously working to improve its quality and precision. Modern computational techniques increase the biological value of NGS data processing, making it more accessible to biomedical researchers. Although the complexity of NGS and the required computational power to analyse the data pose a significant threat to researchers, the introduction of various branches of Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) has given analysis, prediction, and diagnosis a new direction. Deep Learning's potential has been demonstrated in a variety of fields, including biomedical research, where it has outperformed traditional methods. The development of deep learning algorithms aids in the analysis of complicated datasets such as NGS by giving a variety of advanced computational methodologies. Different DL approaches are designed to manage enormous datasets and multiple jobs, and the genetic research business could be the next industry to benefit from DL. This paper discusses a variety of DL methods and tools for analysing NGS data in the fields of contagious diseases, WGS analysis, disease diagnosis, and drug design.