Computational Viromics: Applications of the Computational Biology in Viromics Studies

Abstract

Viruses are a kind of biological entities which rely on host cells for survival. Depending on the genetic materials and replication mode, they can be grouped into double-stranded DNA (dsDNA), single-stranded DNA (ssDNA), doublestranded RNA (dsRNA), positive-sense single-stranded RNA (?ssRNA), negative-sense single-stranded RNA (-ssRNA), ssRNA reverse transcriptase viruses (ssRNART) and dsDNA reverse transcriptase viruses (dsDNA-RT) (Walker et al. 2020). Viruses can infect most kinds of biological entities, including viruses, bacteria, archaea and eukaryote (La Scola et al. 2008; Fermin, 2018). They have a great impact on the earth by shaping bacterial population dynamics and balancing the global ecosystem (Suttle, 2007). For humans, viruses, on the one hand, can cause high human morbidity and mortality and serious economic loss (Baud et al. 2020), on the other hand, they can promote and maintain the healthy balance of the gut microbiome (Seo and Kweon, 2019). Besides, some phages can be applied as the therapy of bacterial infections, especially for the bacterial strains resistant to multiple antibiotics (Altamirano and Barr, 2019). The viromics studies based on the high-throughput sequencing technology have become increasingly popular in recent years, and novel viruses are being discovered at an unprecedented pace (Gregory et al. 2019). For example, the Tara Oceans Project recently identified 195,728 viral populations which were more than 10 times as many as the known global ocean DNA virome (Gregory et al. 2019). However, several challenges exist in analyzing the sequencing data from viromics studies. Firstly, it is difficult to identify all viral nucleotide sequences from the nucleotide sequences that mixed with the sequences of other species and the possible pollutions (Roux et al. 2015a; Ren et al. 2017; Fang et al. 2019; Kieft et al. 2020); secondly, the annotation of viral nucleotide sequences is still challenging, especially for those with remote or no homology with the known viruses (Roux et al. 2015b; McNair et al. 2019; Zhang et al. 2019a); thirdly, the taxonomic assignment of novel viruses is difficult due to a lack of a unified classification system for viruses (Low et al. 2019); fourthly, rapid functional characterization of a large number of newly discovered viruses such as identifying the viral hosts is extremely difficult to achieve by using traditional experimental methods (Jofre and Muniesa, 2020). According to the above analysis, an emerging area of computational viromics which is defined as using the computational methods to solve the problems in viromics studies was proposed in the present study. It includes but not limited to the following aspects: