标题： 巨猎人：使用强化学习和蒙特卡洛树搜索在宏基因组数据中准确检测巨型病毒 显示英文标题

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

摘要： 动机：细胞质大DNA病毒（NCLDVs）因其大的基因组和广泛的功能基因库而引人注目，这有助于它们在环境中的广泛存在以及在宿主代谢重编程和养分循环等过程中的关键作用。宏基因组测序已成为在环境样本中发现新型NCLDVs的强大工具。然而，由于其基因组多样性高、参考基因组有限以及与其他微生物共享区域，从宏基因组数据中识别NCLDVs序列仍然具有挑战性。现有的基于比对和机器学习的方法在敏感性和精确度之间难以达到最佳平衡。结果：在这项工作中，我们提出了GiantHunter，一种基于强化学习的工具，用于从宏基因组数据中识别NCLDVs。通过采用蒙特卡洛树搜索策略，GiantHunter动态选择代表性的非NCLDVs序列作为负训练数据，使模型能够建立稳健的决策边界。在严格设计的实验中进行基准测试表明，GiantHunter在保持竞争力的敏感度的同时实现了高精确度，与第二好的方法相比，F1分数提高了10%，计算成本降低了90%。为了展示其实际应用价值，我们将GiantHunter应用于从长江沿线六个城市收集的60个宏基因组数据集，这些城市位于三峡大坝的上游和下游。结果揭示了NCLDV多样性与大坝接近程度之间的显著差异，这可能受到大坝导致流速降低的影响。这些发现突显了GiantSeeker在推进我们对NCLDVs及其在不同环境中生态作用的理解方面的潜力。 显示英文摘要

摘要： 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 identifying NCLDVs from metagenomic data. By employing a Monte Carlo tree search strategy, GiantHunter dynamically selects representative non-NCLDV sequences as the negative training data, enabling the model to establish a robust decision boundary. Benchmarking on rigorously designed experiments shows that GiantHunter achieves high precision while maintaining competitive sensitivity, improving the F1-score by 10% and reducing computational cost by 90% compared to the second-best method. To demonstrate its real-world utility, we applied GiantHunter to 60 metagenomic datasets collected from six cities along the Yangtze River, located both upstream and downstream of the Three Gorges Dam. The results reveal significant differences in NCLDV diversity correlated with proximity to the dam, likely influenced by reduced flow velocity caused by the dam. These findings highlight the potential of GiantSeeker to advance our understanding of NCLDVs and their ecological roles in diverse environments.