Quantitative Analysis of Genomic Sequences of Virus RNAs Using a Metric-Based Algorithm

The worldwide spread of SARS-CoV-2 virus increases interest in the research of virus genomics and the creation of more advanced study methods. This work aims to develop a new fast DNA walk algorithm for one-dimensional visualization of RNAs based on a big-data method and comparative examination of several viruses and their lines and strains. In this work, a new metric-based algorithm for quantitative and visual analyses of RNAs is proposed and considered. It allows finding any fragments of genomic sequences using the Hamming distance between the binary-expressed RNA characters and symbols of a fragment under the search and building one-dimensional trajectories of genomic walks convenient for quantitative and qualitative analyses of RNAs and DNAs. Similarly, human-language texts can be processed and compared with genomic sequences.This algorithm is used to investigate the complete genomic sequences of SARS CoV-2, MERS, Dengue, and Ebola viruses available from Genbank and GISAID databases. The distributions of atg codon-starting triplets along with these sequences are built and considered as their atg-schemes. Additionally to the atg-walks, single-symbols distributions are calculated to detect the codon-content mutations, which do not change the atg-triplet coordinates along with genomic sequences. The visual analyses of distributions consisting of several hundred triplets enable us to define the level of stability of RNAs towards essential mutations and perform their classing. Statistical studies are applied to distributions of the inter-atg and inter-symbol distances along with genomic sequences. The fractal dimension values of these distributions are calculated, enabling them to correspond to the mutations discovered by Hamming walks and fractal-dimension values of several ten virus samples investigated here. The developed metric-based-based algorithm allows building one-dimensional RNA schemes of different scale levels and effectively analyzing the virus mutations with their classing.

The DNA walk and its demonstration of deterministic chaos—relevance to genomic alterations in lung cancer

MotivationAdvancements in cancer genetics have facilitated the development of therapies with actionable mutations. Although mutated genes have been studied extensively, their chaotic behavior has not been appreciated. Thus, in contrast to naïve DNA, mutated DNA sequences can display characteristics of unpredictability and sensitivity to the initial conditions that may be dictated by the environment, expression patterns and presence of other genomic alterations. Employing a DNA walk as a form of 2D analysis of the nucleotide sequence, we demonstrate that chaotic behavior in the sequence of a mutated gene can be predicted.ResultsUsing fractal analysis for these DNA walks, we have determined the complexity and nucleotide variance of commonly observed mutated genes in non-small cell lung cancer, and their wild-type counterparts. DNA walks for wild-type genes demonstrate varying levels of chaos, with BRAF, NTRK1 and MET exhibiting greater levels of chaos than KRAS, paxillin and EGFR. Analyzing changes in chaotic properties, such as changes in periodicity and linearity, reveal that while deletion mutations indicate a notable disruption in fractal ‘self-similarity’, fusion mutations demonstrate bifurcations between the two genes. Our results suggest that the fractals generated by DNA walks can yield important insights into potential consequences of these mutated genes.Availability and implementationIntroduction to Turtle graphics in Python is an open source article on learning to develop a script for Turtle graphics in Python, freely available on the web at https://docs.python.org/2/library/turtle.html. cDNA sequences were obtained through NCBI RefSeq database, an open source database that contains information on a large array of genes, such as their nucleotide and amino acid sequences, freely available at https://www.ncbi.nlm.nih.gov/refseq/. FracLac plugin for Fractal analysis in ImageJ is an open source plugin for the ImageJ program to perform fractal analysis, free to download at https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.html.Supplementary informationSupplementary data are available at Bioinformatics online.

Diagnosis of Lung Cancer by Fractal Analysis of Damaged DNA

Cancer starts when cells in a part of the body start to grow out of control. In fact cells become cancer cells because of DNA damage. A DNA walk of a genome represents how the frequency of each nucleotide of a pairing nucleotide couple changes locally. In this research in order to study the cancer genes, DNA walk plots of genomes of patients with lung cancer were generated using a program written in MATLAB language. The data so obtained was checked for fractal property by computing the fractal dimension using a program written in MATLAB. Also, the correlation of damaged DNA was studied using the Hurst exponent measure. We have found that the damaged DNA sequences are exhibiting higher degree of fractality and less correlation compared with normal DNA sequences. So we confirmed this method can be used for early detection of lung cancer. The method introduced in this research not only is useful for diagnosis of lung cancer but also can be applied for detection and growth analysis of different types of cancers.

LONG-RANGE FRACTAL CORRELATIONS IN DNA INTRONS AND EXONS

Spectral density and random walk methods of analyzing symbolic sequences, such as DNA bases, are compared. Systematic errors in DNA walk analysis, wide variability between samples, and small ensembles cast doubt on previous conclusion that long-range correlations in DNA occur in non-coding (intron) sequences. A new study of all GenBank release 73 annotated features demonstrates that both introns and exons have similar long-range fractal correlations with exponents consistent with previous category averages.

Studies of an 800-kilobase DNA stretch of the Drosophila X chromosome: comapping of a subclass of scaffold-attached regions with sequences able to replicate autonomously in Saccharomyces cerevisiae

We have previously mapped scaffold-attached regions (SARs) on an 800-kilobase DNA walk from the Drosophila X chromosome. We have also previously shown that the strength of binding, i.e., the ability of SARs to bind to all nuclear scaffolds or only to a fraction of them varied from one SAR to another one. In the present study, 71 of the 85 subfragments that bind scaffolds and 38 fragments that do not bind scaffolds were tested for their ability to promote autonomous replicating sequence (ARS) activity in Saccharomyces cerevisiae. Sixteen SAR-containing fragments from the chromosome walk were also examined for association to yeast nuclear scaffolds in vitro. All identified ARSs (a total of 27) were present on SAR-containing fragments, except two, which were adjacent to SARs. There is thus a correlation between ARS and SAR activities, and this correlation defines a SAR subclass. Moreover, the presence of an ARS on a DNA fragment appeared to be highly correlated with the strength of binding. The binding activity was highly conserved from Drosophila melanogaster to yeast. These data suggest that Drosophila DNA sequences responsible for binding to components of the nuclear scaffold from either D. melanogaster or yeast may be involved in the process of heterologous extrachromosomal replication in yeasts.