Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network

Highlights

• CGR representation of SARS-CoV, SARS-CoV2, MERS and Alpha CoV sequences.

• A combine performance of CGR and ANN to detect the virus sequences.

• Encoding the genome sequence into an organized, well represented small data.

• CGR transforms the genome sequence into short even less than 1% of actual size.

Abstract

In this study, chaos game representation (CGR) is introduced for investigating the pattern of genome sequences. It is an image representation of the genome for the overall visualization of the sequence. The CGR representation is a mapping technique that assigns each sequence base into the respective position in the two-dimension plane to portray the DNA sequence. Importantly, CGR provides one to one mapping to nucleotides as well as sequence. A coordinate of the CGR plane can tell the corresponding base and its location in the original genome. Therefore, the whole nucleotide sequence (until the current nucleotide) can be restored from the one point of the CGR. In this study, CGR coupled with artificial neural network (ANN) is introduced as a new way to represent the genome and to classify intra-coronavirus sequences. A hierarchy clustering study is done to validate the approach and found to be more than 90% accurate while comparing the result with the phylogenetic tree of the corresponding genomes. Interestingly, the method makes the genome sequence significantly shorter (more than 99% compressed) saving the data space while preserving the genome features.