Wagatsuma Ryota, Nishikawa Yohei, Hosokawa Masahito, Takeyama Haruko
Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan.
Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-0072, Japan.
NAR Genom Bioinform. 2025 Jan 7;7(1):lqae185. doi: 10.1093/nargab/lqae185. eCollection 2025 Mar.
Recent advancements in viral metagenomics and single-virus genomics have improved our ability to obtain the draft genomes of environmental viruses. However, these methods can introduce virus sequence contaminations into viral genomes when short, fragmented partial sequences are present in the assembled contigs. These contaminations can lead to incorrect analyses; however, practical detection tools are lacking. In this study, we introduce vClean, a novel automated tool that detects contaminations in viral genomes. By applying machine learning to the nucleotide sequence features and gene patterns of the input viral genome, vClean could identify contaminations. Specifically, for tailed double-stranded DNA phages, we attempted accurate predictions by defining single-copy-like genes and counting their duplications. We evaluated the performance of vClean using simulated datasets derived from complete reference genomes, achieving a binary accuracy of 0.932. When vClean was applied to 4693 genomes of medium or higher quality derived from public ocean metagenomic data, 1604 genomes (34.2%) were identified as contaminated. We also demonstrated that vClean can detect contamination in single-virus genome data obtained from river water. vClean provides a new benchmark for quality control of environmental viral genomes and has the potential to become an essential tool for environmental viral genome analysis.
病毒宏基因组学和单病毒基因组学的最新进展提高了我们获取环境病毒基因组草图的能力。然而，当组装的重叠群中存在短的、片段化的部分序列时，这些方法可能会将病毒序列污染引入病毒基因组。这些污染可能导致错误的分析；然而，目前缺乏实用的检测工具。在本研究中，我们介绍了vClean，这是一种检测病毒基因组污染的新型自动化工具。通过将机器学习应用于输入病毒基因组的核苷酸序列特征和基因模式，vClean可以识别污染。具体而言，对于有尾双链DNA噬菌体，我们通过定义单拷贝样基因并计算其重复次数来尝试进行准确预测。我们使用从完整参考基因组衍生的模拟数据集评估了vClean的性能，二元准确率达到0.932。当vClean应用于从公共海洋宏基因组数据中获得的4693个中等或更高质量的基因组时，1604个基因组（34.2%）被鉴定为受污染。我们还证明了vClean可以检测从河水中获得的单病毒基因组数据中的污染。vClean为环境病毒基因组的质量控制提供了一个新的基准，并有潜力成为环境病毒基因组分析的重要工具。
