Systematic review highlights bioinformatics tools, including hidden Markov models and k-mer approaches, indicating advances in viral metagenomic data analysis.
Key Points
Viral metagenomic data presents challenges for traditional sequence comparison methods, affecting virus discovery.
Analysis reviews over 54 studies, showcasing bioinformatics tools like hidden Markov models and k-mer approaches.
Review conducted following PRISMA 2020 guidelines, emphasizing various methodologies for virus detection.
Continuous improvements in machine learning approaches highlight the evolution of virus detection strategies.