New Intraclass Helitrons Classification Using DNA-Image Sequences and Machine Learning Approaches

Touati, Rabeb; Messaoudi, Imen; Oueslati, Afef Elloumi; Lachiri, Zied; Kharrat, Maher

doi:10.1016/j.irbm.2019.12.004

Cited by 16 publications

(21 citation statements)

References 22 publications

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“…The used methods are an alignment free method, requesting only a sequence database and a bioinformatics and signal processing knowledges [16][17][18]. Those methods transformed every nucleotide sequence in numerical form.…”

Section: Discussionmentioning

confidence: 99%

“…Those methods transformed every nucleotide sequence in numerical form. CGR technique has been used because it has a remarkable capacity to differentiate between genetic sequences belonging to different species [17]. Comparing to alignment-based methods, it uses an image-based approach generated by different techniques to elaborate genomic signatures.…”

Section: Discussionmentioning

confidence: 99%

“…This point CGR n is repeatedly placed halfway between the previous plotted point CGR n−1 and the segment joining the vertex corresponding to the read letter S n [19]. This value is chosen according to previous research [16,17,19]. The proposed CGR steps are shown in the following Algorithm 1.…”

Section: Cgr Techniquementioning

confidence: 99%

“…The organism's genomic signature is a very important graphical representation that allows understanding of the intragenic variations [16][17][18], especially for handling large genomic data. This assay is based on the Chaos Game Representation (CGR) derived from the chaos theory of Jeffrey et al (1990) and it was considered as a mapping method of large genome sequences [19].…”

Section: Introductionmentioning

confidence: 99%

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Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

et al. 2020

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Coronaviruses are responsible on respiratory diseases in animal and human. The combination of numerical encoding techniques and digital signal processing methods are becoming increasingly important in handling large genomic data. In this paper, we propose to analyze the SARS-CoV-2 genomic signature using the combination of different nucleotide representations and signal processing tools in the aim to identify its genetic origin. The sequence of SARS-CoV-2 was compared with 21 relevant sequences including Bat, Yak and Pangolin coronavirus sequences. In addition, we developed a new algorithm to locate the nucleotide modifications. The results show that the Bat and Pangolin coronaviruses were the most related to SARS-CoV-2 with 96% and 86% of identity all along the genome. Within the S gene sequence, the Pangolin sequence presents local highest nucleotide identity. Those findings suggest genesis of SARS-Cov-2 through evolution from Bat and Pangolin strains. This study offers new ways to automatically characterize viruses.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Cgr Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

et al. 2020

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“…The (CGR) is a genomic sequence mapping. It has been proposed by Jeffrey [21]. The frequency chaos game representation (FCGR) is a nucleotide frequency matrix extracted from the CGR [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Involving FCGR images in studying fractality and multifractality in human chromosome 22 and bacteria complete genome

2021

IJATCSE

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The frequency chaos game representation (FCGR) is a simple yet powerful visualization method of DNA sequences. It provides the possibility of representing genomes by images, revealing in such a way different fractal structures. In this paper, we perform a fractal and multifractal analysis of Human chromosome 22 and some complete genomes based on the FGCR image. We used the fractal dimension (FD) and the multifractality degree (ΔDq) to characterize and distinguish genomes. First, we construct the FCGR image with different orders of human chromosome 22. Next, we calculate the fractal dimension, the general dimension spectrum and the multifractal spectrum of each FCGR image using the box-counting method. Then, we examine the FCGR image fractal and multifractal characteristics impact on highlighting the existence of repetitive DNA sequences in human chromosome 22. We also observe the relationship between fractality and multifractality. After that, we apply this study to bacteria completes genomes and C.elegans chromosome I. The obtained results show that the multifractal spectra of all organisms studied are multifractal-like and chromosome 22 strong multifractality proves its richness of repetitive sequences. Also, we observed that with the increasing the FCGR order value, the multifractality grows and the fractal dimension lessens. Finally, by assigning to each sequence a point in two-dimensional space (FD, ΔDq), we obtained three classes of genomes. We can easily distinguish the human chromosome 22 from other genomes and Bacteria are almost close in the spaces (FD, ΔDq).

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Matching Pattern in DNA Sequences Using Machine Learning Approach Based on K-Mer Function

Ravikumar

Prashanth

Guru

2022

Studies in Computational Intelligence

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New Intraclass Helitrons Classification Using DNA-Image Sequences and Machine Learning Approaches

Cited by 16 publications

References 22 publications

Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

Involving FCGR images in studying fractality and multifractality in human chromosome 22 and bacteria complete genome

Matching Pattern in DNA Sequences Using Machine Learning Approach Based on K-Mer Function

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