Abstract: We investigate various statistical techniques for analyzing DNA sequences, concentrating on four key species: humans, coronaviruses, maize, and soybeans. These species were chosen because of their significance in medicine, biology, the economy, the treatment of sickness, and agriculture. We examined the structure and dynamics of the DNA sequences of these species using techniques like Chaos Game Representation, Multifractal Detrended Fluctuation Analysis, and the complexity-entropy plane. Additionally, we used models based on Tsallis statistics to characterize the distribution of base pair lengths in human DNA, and we used Bayesian statistics to determine which model best fit these distributions.

The findings demonstrated a power law association in the genetic sequences under investigation, emphasizing the persistence of their composition and the existence of multifractal features. These results highlight the intricacy and challenge of genetic makeup prediction while also highlighting the potential of these statistical tools to advance our knowledge of DNA sequences and their applications to technology, agriculture, and health. The results open up new avenues for investigation, recommending the use of these approaches on a larger variety of creatures in an effort to fully explore the enormous genetic complexity that exists.}