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Bioinformatics Tools and Approaches for Virus Discovery in Genomic Data: A Systematic Review.

Julia Galeeva1, Polina Kuzmichenko1, Alexander Manolov1

  • 1Research Institute for Systems Biology and Medicine (RISBM), Department of Mathematical Biology and Bioinformatics, Moscow 117246, Russia.

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This review explores virus discovery tools for analyzing metagenomic data. Machine learning methods improve detection of diverse viruses, but challenges in interpreting results and handling incomplete genomes persist.

Keywords:
HMMbioinformaticsdeep learningmachine learningmetagenomicstaxonomic annotationviral classificationviruses

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Area of Science:

  • Bioinformatics
  • Virology
  • Computational Biology

Background:

  • Viral metagenomic data is growing exponentially, necessitating advanced discovery tools.
  • Extreme viral diversity, rapid evolution, and limited databases challenge traditional methods.
  • High-throughput sequencing generates vast data requiring efficient analysis for virus identification.

Purpose of the Study:

  • To systematically review trends and advances in virus discovery tools from 1990-2025.
  • To focus on first-line tools for analyzing high-throughput sequencing data.
  • To summarize current knowledge and future directions in viral detection.

Main Methods:

  • Systematic review following PRISMA 2020 guidelines.
  • PubMed database search with snowballing approach.
  • Analysis of over 54 key studies on virus discovery methodologies.

Main Results:

  • Evaluated alignment-based, similarity estimation, HMM, k-mer, and machine learning methods.
  • Machine learning approaches show improved detection of divergent viruses.
  • Challenges include interpreting model decisions and managing incomplete viral genomes.

Conclusions:

  • Methodological advances, particularly machine learning, enhance virus discovery.
  • Further development is needed to address interpretability and incomplete genome challenges.
  • This review provides a foundation for future virus detection tool development.