Evaluation of genome similarities using a wavelet-domain approach

Abstract

Introduction: Tuberculosis is listed among the top 10 causes of deaths worldwide. The resistant strains causing this disease have been considered to be responsible for public health emergencies and health security threats. As stated by the World Health Organization (WHO), around 558,000 different cases coupled with resistance to rifampicin (the most operative first-line drug) have been estimated to date. Therefore, in order to detect the resistant strains using the genomes of Mycobacterium tuberculosis (MTB), we propose a new methodology for the analysis of genomic similarities that associate the different levels of decomposition of the genome (discrete non-decimated wavelet transform) and the Hurst exponent. Methods: The signals corresponding to the ten analyzed sequences were obtained by assessing GC content, and then these signals were decomposed using the discrete non-decimated wavelet transform along with the Daubechies wavelet with four null moments at five levels of decomposition. The Hurst exponent was calculated at each decomposition level using five different methods. The cluster analysis was performed using the results obtained for the Hurst exponent. Results: The aggregated variance, differenced aggregated variance, and aggregated absolute value methods presented the formation of three groups, whereas the Peng and R/S methods presented the formation of two groups. The aggregated variance method exhibited the best results with respect to the group formation between similar strains. Conclusion: The evaluation of Hurst exponent associated with discrete non-decimated wavelet transform can be used as a measure of similarity between genome sequences, thus leading to a refinement in the analysis.

K-mer applied in Mycobacterium tuberculosis genome cluster analysis

“…In the most recent work of Ferreira et al (2020), who also employed the same methodology of Ferreira et al (2017) in the first part of the analysis. The verification of the formation of the groups was made using the technique of the exponent of Hurst obtained through five different methods.…”

“…The sequences (MTB strains) used in this work were extracted from the National Center for Biotechnology Information (NCBI) website and are described in Table 1 (NCBI, 2018). These same strains were also used in (Saini and Dewan, 2016;Ferreira et al, 2017Ferreira et al, , 2020.…”

Analysis of similarities in respiratory tract genomes: an approach using the Hurst exponent and the non-decimated discrete wavelet transform

“…Figure 1 shows the decomposition process that was carried out at each of the levels where the non-decimal discrete wavelet transform was applied. The Hurst exponent as a measure to verify similar genomic sequences has been previously addressed by Ferreira et al (2020), in which they apply the 5 techniques discussed here to assess similarities in strains of the tuberculosis genome.…”

“…In this work, 5 different methods were applied to estimate the Hurst exponent to study similarities in the genomes of respiratory tract viruses of the families Coronaviridae and Paramyxoviridae. This methodology has already been applied in the work by Ferreira et al (2020) finding similarities in tuberculosis strains.…”

Spectral Decomposition of Mappings of Molecular Genetic Information in the System Basis of Single Nucleotide Functions

“…The relationship between the coding regions of the plant genome and the distribution of energy during the photochemical process of photosynthesis is analyzed in [5]. To identify resistant strains using the genomes of Mycobacterium tuberculosis (MTB), the authors applied a technique for genomic similarity analysis that links different levels of genome decomposition (discrete non-decimated wavelet transform) and the Hurst exponent [6]. To analyze data on the same samples from multiple sources, a new model of related components was created in [7], which directly includes partially shared structures.…”