ArticleExpert systems with applications2022
Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network.
Article in Expert systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Advancements in Viral Genomics: Gated Recurrent Unit Modeling of SARS-CoV-2, SARS, MERS, and Ebola viruses.Revista da Sociedade Brasileira de Medicina Tropical · 2025Article
- Exploring geometry of genome space via Grassmann manifolds.Innovation (Cambridge (Mass.)) · 2024Article
- Polarization- and Chaos-Game-Based Fingerprinting of Molecular Targets of Listeria Monocytogenes Vaccine and Fully Virulent Strains.Current issues in molecular biology · 2023Article
- A new profiling approach for DNA sequences based on the nucleotides' physicochemical features for accurate analysis of SARS-CoV-2 genomes.BMC genomics · 2023Article
- Quantification of the Diversity in Gene Structures Using the Principles of Polarization Mapping.Current issues in molecular biology · 2023Article
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Authors and funding
3 authors.
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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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Registered trials
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