Modelagem em superestatística da distribuição de tamanho de sequências de DNA codificantes para Genoma Eucarionte

Abstract

This study explores the fundamental principles of superstatistical formalism and applies these concepts to the analysis of genomic data from eukaryotic organisms. A generalized Heston model was employed within the superstatistical framework to capture short-range correlations in the size distributions of coding regions, exons, of the Homo sapiens genome. For vegetables studies, a time series analysis approach was adopted to describe short-range correlations in the Cucurbitaceae family. The resulting models, the q-Gamma and q-Inverse Gamma distributions, proved effective in describing genomic data. Furthermore, the application of Bayesian analysis allowed us to assess the uncertainty associated with model parameters and to select the most appropriate model for data description. Our findings indicate that the superstatistical approach and the use of generalized distributions are valuable tools in genomic data analysis, offering deeper insights into short-range correlations in exon size distribution.