tetano
Editor, Senior Moderator
Expert Syst Appl
. 2022 May 15;194:116559.
doi: 10.1016/j.eswa.2022.116559. Epub 2022 Jan 21.
Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network
Tirthankar Paul[SUP] 1 [/SUP], Seppo Vainio[SUP] 2 [/SUP], Juha Roning[SUP] 1 [/SUP]
Affiliations
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.
Keywords: Artificial neural network; Chaos game representation; Coronavirus.
. 2022 May 15;194:116559.
doi: 10.1016/j.eswa.2022.116559. Epub 2022 Jan 21.
Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network
Tirthankar Paul[SUP] 1 [/SUP], Seppo Vainio[SUP] 2 [/SUP], Juha Roning[SUP] 1 [/SUP]
Affiliations
- PMID: 35095217
- PMCID: PMC8779865
- DOI: 10.1016/j.eswa.2022.116559
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.
Keywords: Artificial neural network; Chaos game representation; Coronavirus.