中山大学施莽等研究人员合作使用人工智能记录隐藏的RNA病毒圈。相关论文于2024年10月9日在线发表在《细胞》杂志上。
研究人员开发了一种名为LucaProt的深度学习算法，用于在来自全球多样化生态系统的10487个转录组中发现高度变异的RNA依赖性RNA聚合酶（RdRP）序列。LucaProt结合了序列和预测的结构信息，能够准确检测RdRP序列。
通过这一方法，研究人员鉴定出了161979个潜在的RNA病毒物种和180个RNA病毒超群，包括许多之前研究较少的群体，以及长度高达47250个核苷酸的RNA病毒基因组及其复杂的基因组结构。部分新发现的RNA病毒通过RT-PCR和RNA/DNA测序得到验证。
这些新发现的RNA病毒存在于各种环境中，包括空气、温泉和海底热泉，且病毒多样性和丰度在不同生态系统中显著不同。该研究推动了病毒的发现，揭示了病毒圈的广度，并提供了更好记录全球RNA病毒组的计算工具。
据介绍，当前的宏基因组学工具可能无法识别高度变异的RNA病毒。
附：英文原文
Title: Using artificial intelligence to document the hidden RNA virosphere
Author: Xin Hou, Yong He, Pan Fang, Shi-Qiang Mei, Zan Xu, Wei-Chen Wu, Jun-Hua Tian, Shun Zhang, Zhen-Yu Zeng, Qin-Yu Gou, Gen-Yang Xin, Shi-Jia Le, Yin-Yue Xia, Yu-Lan Zhou, Feng-Ming Hui, Yuan-Fei Pan, John-Sebastian Eden, Zhao-Hui Yang, Chong Han, Yue-Long Shu, Deyin Guo, Jun Li, Edward C. Holmes, Zhao-Rong Li, Mang Shi
Issue&Volume: 2024-10-09
Abstract: Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.
DOI: 10.1016/j.cell.2024.09.027
Source: https://www.cell.com/cell/abstract/S0092-8674(24)01085-7