共享标记谱的双向子集关系可实现准确的病毒分类。
Bidirectional subsethood of shared marker profiles enables accurate virus classification.
作者信息
Riccardi Christopher, Wang Yuqiu, Yooseph Shibu, Sun Fengzhu
机构信息
Quantitative and Computational Biology Department, University of Southern California, 1050 Childs Way, Los Angeles, 90089, CA, USA.
Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, I-50019, Italy.
出版信息
Microbiome. 2025 Jul 24;13(1):170. doi: 10.1186/s40168-025-02159-x.
BACKGROUND
Due to the impact of viral metagenomic sequencing, the official virus taxonomy is updated several times a year, with labels being renamed even substantially across releases. While this helps reveal newer aspects on the classification of viruses, existing bioinformatic methods for classification struggle to stay in sync with this ever-improving resource.
RESULTS
We developed a new computer program, named VIRGO, that is able to correctly predict virus families from metagenomic data with an F1 score above 0.9 using a novel viral sequence similarity metric proposed in this work. Moreover, it ensures compatibility with any version of the official taxonomy of viruses.
CONCLUSIONS
Virgo is designed to easily incorporate newer releases of the official taxonomy, thus representing a valuable resource in the virology community while raising awareness to develop computational methods that evolve alongside manually curated resources. Video Abstract.
背景
由于病毒宏基因组测序的影响，官方病毒分类法每年更新数次，甚至在不同版本之间标签也会大幅重命名。虽然这有助于揭示病毒分类的新方面，但现有的用于分类的生物信息学方法难以与这一不断完善的资源保持同步。
结果
我们开发了一个名为VIRGO的新计算机程序，它能够使用本研究中提出的一种新颖的病毒序列相似性度量，从宏基因组数据中正确预测病毒科，F1分数高于0.9。此外，它确保与官方病毒分类法的任何版本兼容。
结论
VIRGO旨在轻松纳入官方分类法的更新版本，因此在病毒学界是一种有价值的资源，同时提高了人们对开发与人工编纂资源同步发展的计算方法的认识。视频摘要。
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