Kullback-Leibler cluster entropy to quantify local correlation in human genes

Given the massive increase in genomic data generated by sequencing technologies, the use of algorithms that do not require sequence alignment has become essential. In this study, we apply an information theoretical clustering method based on the Kullback-Leibler relative entropy, to analyse the structure of the human ASH1L gene. This gene is involved in epigenetic regulation of DNA and is associated with neurodevelopmental disorders such as autism spectrum disorder and intellectual disability. In this study, the gene sequence is first converted into numerical representation, then its divergence from synthetic fractional Brownian motion models taken as a reference is quantified. Our results show that the observed patterns remain consistent across different segmentation strategies, including both overlapping and non-overlapping windows, and highlight the potential of this method for broader applications in comparative genomics and gene function annotation.