ABSTRACT
Monitoring genetic mutations in DNA sequences and their subsequent characterisation provide the possibility for rapid development of diagnostics and therapeutic tools. Here, it is shown that the "DNA walk" (DNAW) representation together with multifractal detrended fluctuation analysis (MFDFA), i.e. DNAW/MFDFA, form a reliable characterization method for studying local and global properties of similar DNA sequences. The DNAW/MFDFA approach allows to study the stochastic properties of genetic sequences by constructing a one-to-one map of the sequence onto a walk, and is able to uncover the self-similarity properties of DNA walks. These features are illustrated on a set of similar DNA sequences of SARS-CoV-2 virus, in which the differences in nucleotide bases arise due to genetic mutations. The results show that DNAW/MFDFA can be used to extract long-range correlation information and type and degree of fractal complexity.
ABSTRACT
An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence. © 2023
ABSTRACT
Monitoring genetic mutations in DNA sequences and their subsequent characterisation provide the possibility for rapid development of diagnostics and therapeutic tools. Here, it is shown that the "DNA walk" (DNAW) representation together with multifractal detrended fluctuation analysis (MFDFA), i.e. DNAW/MFDFA, form a reliable characterization method for studying local and global properties of similar DNA sequences. The DNAW/MFDFA approach allows to study the stochastic properties of genetic sequences by constructing a one-to-one map of the sequence onto a walk, and is able to uncover the self-similarity properties of DNA walks. These features are illustrated on a set of similar DNA sequences of SARS-CoV-2 virus, in which the differences in nucleotide bases arise due to genetic mutations. The results show that DNAW/MFDFA can be used to extract long-range correlation information and type and degree of fractal complexity.
ABSTRACT
An approach based on fractal scaling analysis to characterize the organization of the SARS-CoV-2 genome sequence was used. The method is based on the detrended fluctuation analysis (DFA) implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence regions. The nucleotides sequence is mapped in a numerical sequence by using four different assignation rules: amino-keto, purine-pyrimidine, hydrogen-bond and hydrophobicity patterns. The originally reported sequence from Wuhan isolates (Wuhan Hu-1) was considered as a reference to contrast the structure of the 2002-2004 SARS-CoV-1 strain. Long-range correlations, quantified in terms of a scaling exponent, depended on both the mapping rule and the sequence region. Deviations from randomness were attributed to serial correlations or anti-correlations, which can be ascribed to ordered regions of the genome sequence. It was found that the Wuhan Hu-1 sequence was more random than the SARS-CoV-1 sequence, which suggests that the SARS-CoV-2 possesses a more efficient genomic structure for replication and infection. In general, the virus isolated in the early 2020 months showed slight correlation differences with the Wuhan Hu-1 sequence. However, early isolates from India and Italy presented visible differences that led to a more ordered sequence organization. It is apparent that the increased sequence order, particularly in the spike region, endowed some early variants with a more efficient mechanism to spreading, replicating and infecting. Overall, the results showed that the DFA provides a suitable framework to assess long-term correlations hidden in the internal organization of the SARS-CoV-2 genome sequence.