Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences

Abstract

Predicting the hosts of newly discovered viruses is important for pandemic surveillance of infectious diseases. We investigated the use of alignment-based and alignment-free methods and support vector machine using mononucleotide frequency and dinucleotide bias to predict the hosts of viruses, and applied these approaches to three datasets: rabies virus, coronavirus, and influenza A virus. For coronavirus, we used the spike gene sequences, while for rabies and influenza A viruses, we used the more conserved nucleoprotein gene sequences. We compared the three methods under different scenarios and showed that their performances are highly correlated with the variability of sequences and sample size. For conserved genes like the nucleoprotein gene, longer k-mers than mono- and dinucleotides are needed to better distinguish the sequences. We also showed that both alignment-based and alignment-free methods can accurately predict the hosts of viruses. When alignment is difficult to achieve or highly time-consuming, alignment-free methods can be a promising substitute to predict the hosts of new viruses.

Rapid identification of human‐infecting viruses

“…Two kinds of methods, the sequence alignment‐based and alignment‐free methods, have been developed to predict the viral host. For example, the methods based on k ‐mers extracted from viral genomes (Ahlgren, Ren, Lu, Fuhrman, & Sun, ; Li & Sun, ) or sequence blast have been developed to predict the hosts of the phage (Bolotin, Quinquis, Sorokin, & Ehrlich, ; Edwards, McNair, Faust, Raes, & Dutilh, ). Some studies also attempted to identify the human virus by using these methods.…”

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

“…Successful prediction of the future expansion of small subtrees of hemagglutinins (HA) part of the viral antigenic set was possible from training H3N2 and testing on H1N1, using reconstructed timed phylogenetic tree [37]. Machine learning can also be used to predict the hosts of newly discovered viruses based on analysis of nucleoprotein gene sequences and spike gene sequences, and can be a useful additional tool for tracing back viral origins, especially when the data set is large and comparative analysis is difficult or time-consuming [38].…”

Pathogenic virus detection method based on multi-model fusion

“…Li et al proposed a comparative study of predicting viral hosts based on alignment or alignment-free methods [1]. The results show that when alignment methods cannot detect or need to spend lots of time, the support vector machines model is a substitute method, which produces good prediction results.…”