Understanding the genetics and taxonomy of ancient viruses will give us great insights into not only the origin and evolution of viruses but also how viral infections played roles in our evolution. Endogenous viruses are remnants of ancient viral infections and are thought to retain the genetic characteristics of viruses from ancient times. In this study, we used machine learning of endogenous RNA virus sequence signatures to identify viruses in the human genome that have not been detected or are already extinct. Here, we show that the k-mer occurrence of ancient RNA viral sequences remains similar to that of extant RNA viral sequences and can be differentiated from that of other human genome sequences. Furthermore, using this characteristic, we screened RNA viral insertions in the human reference genome and found virus-like insertions with phylogenetic and evolutionary features indicative of an exogenous origin but lacking homology to previously identified sequences. Our analysis indicates that animal genomes still contain unknown virus-derived sequences and provides a glimpse into the diversity of the ancient virosphere.

了解古代病毒的遗传学和分类学将使我们不仅对病毒的起源和进化有深刻的了解，而且使我们了解病毒感染如何在我们的进化中发挥作用。内源性病毒是古代病毒感染的残余物，被认为可以保留古代病毒的遗传特征。在这项研究中，我们使用机器学习的内源RNA病毒序列特征来识别人类基因组中尚未检测到或已经灭绝的病毒。在这里，我们表明ķ古代RNA病毒序列的早期发生与现存RNA病毒序列的发生类似，并且可以与其他人类基因组序列区分开。此外，利用这一特征，我们筛选了人类参考基因组中的RNA病毒插入片段，并发现了具有系统发育和进化特征的病毒样插入片段，这些特征表明存在外源起源，但与先前鉴定的序列缺乏同源性。我们的分析表明，动物基因组仍然包含未知的病毒衍生序列，并可以一窥古代病毒圈的多样性。