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Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review
Runzhou Yu1, Ziyi Huang1, Theo Y C Lam1
1Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China.
Briefings in Bioinformatics
|July 14, 2024
Summary
This review compares profile hidden Markov model (pHMM) databases for viral metagenomics. It offers guidance to researchers for selecting the best pHMM databases to improve virus detection sensitivity and reliability.
Area of Science:
- Bioinformatics
- Virology
- Metagenomics
Background:
- Profile hidden Markov models (pHMMs) offer high sensitivity for remote homology searches.
- pHMMs are crucial for detecting novel or divergent viruses in metagenomic datasets.
- Existing pHMM databases vary in design, complicating user selection.
Purpose of the Study:
- To provide a comprehensive evaluation and comparison of commonly used pHMM databases for viral sequence discovery.
- To assess the strengths and limitations of different pHMM databases.
- To offer practical suggestions for optimizing the use of pHMM databases in viral metagenomics.
Main Methods:
- Characterized databases by size, taxonomic coverage, and model properties using quantitative metrics.
- Assessed database performance in virus identification using simulated and real metagenomic data.
- Evaluated performance across multiple application scenarios.
Main Results:
- Quantitative metrics revealed differences in database sizes, taxonomic coverage, and model properties.
- Performance assessment highlighted varying effectiveness of databases in virus identification.
- Experimental results provided insights into database strengths and limitations.
Conclusions:
- The study offers a critical assessment of pHMM databases for viral metagenomics.
- Practical suggestions are provided to enhance the quality and reliability of viral detection.
- Researchers can use these findings to optimize their selection and application of pHMM databases.
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