Using artificial intelligence techniques for COVID-19 genome analysis

Abstract

The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.

Keywords: COVID-19; Genome sequence; Mutation; Nucleotide bases; Sequential pattern mining.

Fig. 1

SARS-CoV-2 Structure [21]

Fig. 2

Structure of the SARS-CoV-2 genome [24]

Fig. 3

Proposed SPM and sequence prediction approach for analyzing COVID-19 genome sequences

Fig. 4

Sequential rules discovered in a genome sequence by ERMiner

Fig. 5

COVID-19 genome mutation in whole sequences

Fig. 6

COVID-19 genome mutation