Next generation sequencing-based transcriptome data mining for virus identification and characterization: Review on recent progress and prospects

•Recent progress has significantly enhanced the efficiency and accuracy of virus identification through the utilization of sophisticated NGS data mining approach.•This study provides an in-depth discussion of these techniques, offering a detailed overview of workflows and applicable computational methods.•This review offers a comprehensive overview of current methodologies, discusses recent technological advancements, and highlights future directions to overcome existing challenges. Advancements in next-generation sequencing (NGS) technologies and innovative bioinformatics tools have significantly accelerated virus discovery by analyzing of NGS data. This approach provides a cost-effective and efficient method for processing large datasets, allowing for rapid virus detection and identification. Researchers can comprehensively understand virus-host interactions by integrating data mining with other omics data, such as proteomics (the study of proteins) and metabolomics (the study of metabolic processes). Recent progress has significantly enhanced the efficiency and accuracy of virus identification by using a sophisticated NGS data mining approach. This study provides an in-depth discussion of these techniques, offering a detailed overview of workflows and applicable computational methods. Despite these advantages, the virus discovery process through data mining encounters obstacles such as ethical issues, the absence of standardized protocols for virus discovery procedures, and challenges in validation and interpretation. Addressing these obstacles is crucial for fully realizing the potential of NGS data mining in virus research. This review discusses current methodologies, recent advancements, and future directions to overcome these challenges, ultimately contributing to our understanding of viral diversity and virus-host dynamics.