Departments of Molecular Microbiology and Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Departments of Molecular Microbiology and Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Virus Res. 2017 Jul 15;239:136-142. doi: 10.1016/j.virusres.2017.02.002. Epub 2017 Feb 9.
The accurate classification of viral dark matter - metagenomic sequences that originate from viruses but do not align to any reference virus sequences - is one of the major obstacles in comprehensively defining the virome. Depending on the sample, viral dark matter can make up from anywhere between 40 and 90% of sequences. This review focuses on the specific nature of dark matter as it relates to viral sequences. We identify three factors that contribute to the existence of viral dark matter: the divergence and length of virus sequences, the limitations of alignment based classification, and limited representation of viruses in reference sequence databases. We then discuss current methods that have been developed to at least partially circumvent these limitations and thereby reduce the extent of viral dark matter.
病毒暗物质的准确分类——即那些起源于病毒但与任何参考病毒序列都不匹配的宏基因组序列——是全面定义病毒组的主要障碍之一。根据样本的不同,病毒暗物质在序列中所占的比例可能在 40%到 90%之间。这篇综述主要关注与病毒序列相关的暗物质的特殊性质。我们确定了导致病毒暗物质存在的三个因素:病毒序列的差异和长度、基于比对的分类的局限性以及参考序列数据库中病毒的代表性有限。然后,我们讨论了目前已经开发出的至少部分规避这些限制的方法,从而减少病毒暗物质的程度。