Evaluation of genome similarities using the non-decimated wavelet transform

Sáfadi

Rev. Soc. Bras. Med. Trop.

2020

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.

Section: Discussionmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 99%

“…Since the m values are logarithmically spaced, we further represent it as Therefore, (8) Thus, in a log-log plot we would expect to obtain a straight line with a slope equal to 2H-2.…”

Section: Differenced Aggregated Variance Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of genome similarities using a wavelet-domain approach

Sáfadi

Rev. Soc. Bras. Med. Trop.

2020

“…The first group with DS and DR sequences (lower energy) and the second group with MDR and XDR sequences (highest energy). Ferreira et al (2017) showed that the behavior of GC-content sequence can be succinctly described regarding the non-decimated wavelet transform, and indicated how this characterization can be used to improve clustering of the similar strains of the genome of the Mycobacterium tuberculosis, having a very efficient level of detail. The clustering analysis using the energy obtained at each level of the analyzed sequences was essential to verify the dissimilarity of the sequences.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Genome Similarities Using Independent Components

Sáfadi

2020

Rev. Bras. Biom.

We propose the use of independent component analysis to ﬁnd similarities of genomes. Considering diﬀerent numbers of independent components, the complete linkage method was used to identify groups based on the estimated coeﬃcients of the mixing matrix. The sequences analyzed correspond to the strains of the Mycobacterium tuberculosis genome, ten sequences were analyzed, obtained from the National Center for Biotechnology Information (NCBI, 2017). The GC-content of each sequence was evaluated using a sliding window of 10,000 bases. The clustering analysis using the independent components of the analyzed sequences was essential to verify the dissimilarity of the sequences.

J. Chem. Theory Comput.

Improved Protocol for the Selection of Structures from Molecular Dynamics of Organic Systems in Solution: The Value of Investigating Different Wavelet Families

Gonçalves

França

et al. 2022

Wavelets are mathematical tools used to decompose and represent another function described in the time domain, allowing the study of each component of the original function with a scale-compatible resolution. Thus, these transforms have been used to select conformations from molecular dynamics (MD) trajectories in systems of fundamental and technological interest. Recently, our research group has used wavelets to develop and validate a method, meant to select structures from MD trajectories, which we named OWSCA (optimal wavelet signal compression algorithm). Here, we moved forward on this project by demonstrating the efficacy of this method on the study of three different systems (non-flexible organic, flexible organic, and protein). For each system, 93 wavelets were investigated to verify which is the best one for a given organic system. The results show that the best wavelets were different for each system and, also, very close to the experimental values, with the wavelets db1, rbio 3.1, and bior1.1 being selected for the non-flexible, flexible organic, and protein systems, respectively. This reinforces our OWSCA as a very efficient and promising method for the selection of structures from MD trajectories of different classes of compounds. Our findings also point out that additional studies considering wavelet families are needed for defining the best wavelet for representing each system under study.