Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

ABSTRACT: Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We

SUBMITTER: Yu R 

PROVIDER: S-EPMC11246558 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature