GiantHunter: accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search.

ABSTRACT:

Motivation

Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to their widespread environmental presence and critical roles in processes such as host metabolic reprogramming and nutrient cycling. Metagenomic sequencing has emerged as a powerful tool for uncovering novel NCLDVs in environmental samples. However, identifying NCLDV sequences in metagenomic data remains challenging due to their high genomic diversity, limited reference genomes, and shared regions with other microbes. Existing alignment-based and machine learning methods struggle with achieving optimal trade-offs between sensitivity and precision.

Results

In this work, we present GiantHunter, a reinforcement learning-based tool for ide

SUBMITTER: Qu F 

PROVIDER: S-EPMC12261416 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature