Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network
Paul, Tirthankar; Vainio, Seppo; Röning, Juha (2021-01-21)
Tirthankar Paul, Seppo Vainio, Juha Roning, Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network, Expert Systems with Applications, Volume 194, 2022, 116559, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.116559
© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022052037267
Tiivistelmä
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.
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