**12. Conclusion**

*Bioinformatics Tools for Detection and Clinical Interpretation of Genomic Variations*

genomic selection methods dropping the penalties of each approaches like in elastic net, enabling the fitting of a certain statistical model. Therefore, an outstanding methodology to analyze genome is elastic net domain used in several study, like

Recently, the tuberculosis strain's differences were evaluated using the elastic net domain [34]. In that examination, 10 genome sequences of *Mycobacterium tuberculosis* with a window size of 10,000 bp were assessed combining the NDWT and elastic net domain. This study encompasses 10 strains: 2 from drug resistant, 6 from drug susceptible, 1 from multidrug resistant, and finally 1 from extensively drug resistant. The clustering detected on that analysis indicated to be real

Hurst exponent is applied as a degree of long-standing memory of time series. It associates to the autocorrelations of time series and the degree at which these decline as the lag between pairs of values intensifications. This coefficient has started to be established in hydrology, used to understand the variation level of dam size at Nile River over a long cycle of time. Harold Edwin Hurst was a British engineer that worked with hydrology; for this reason the coefficient has his surname. Later, this exponent was used in several areas, including fractal geometry, storage process, trends in financial market analyzing economic time series, mechanics, physics, mathematics, computation, and finally to the long-ranging dependency in DNA. **Figure 2** displays the values of Hurst exponent and their interpretation in a

Using the genetic data, the Hurst exponent approach is able to build genetic cluster based on genome sequence. There are a lot of estimation methods of Hurst exponent: the original and best-known is the alleged rescaled range (R/S) analysis promoted by [37, 38] and based on previous hydrological findings [39]. Alternatives include DFA, periodogram regression [40] aggregated variances [41], local Whittle's estimator [42], and wavelet analysis [43, 44] both in the time domain and frequency

In our case, we performed a Hurst exponent in the bacterial strains used in article [34]. We did many methods of Hurst exponent. Interestingly, the R/S methodology was the most similar to the cluster obtained on elastic net domain approach. This data is not shown due to being in a review process to an International journal currently. Our data agree with the majority of scientific papers published approaching the Hurst exponent, which so far applying the R/S

**10**

**Figure 2.**

*Hurst exponent pattern interpretation of the index value.*

[33–36].

adequate.

**11. Hurst exponent**

long-standing.

domain.

method.

We strongly believe that exploring the genetic variability of any organism using wavelet coupled with elastic net domain and/or Hurst exponent will be a valuable and interesting tool. It is not difficult and the free R software could solve easily the approach. In this way, it gives reliability and robustness in your results. Therefore, these bioinformatics apparatuses provide more possibility to scrutinize the genetic divergence of living organisms.
