Exon–intron boundary detection made easy by physicochemical properties of DNA

Genome architecture in eukaryotes exhibits a high degree of complexity. Amidst the numerous intricacies, the existence of genes as non-continuous stretches composed of exons and introns has garnered significant attention and curiosity among researchers. Accurate identification of exon-intron (EI) boundaries is crucial to decipher the molecular biology governing gene expression and regulation. This includes understanding both normal and aberrant splicing, with aberrant splicing referring to the abnormal processing of pre-mRNA that leads to improper inclusion or exclusion of exons or introns. Such splicing events can result in dysfunctional or non-functional proteins, which are often associated with various diseases. The currently employed frameworks for genomic signals, which aim to identify exons and introns within a genomic segment, need to be revised primarily due to the lack of a robust consensus sequence and the limitations posed by the training on available experimental datasets. To tackle these challenges and capitalize on the understanding that DNA exhibits function-dependent local physicochemical variations, we present ChemEXIN, an innovative novel method for predicting EI boundaries. The method utilizes a deep-learning (DL) architecture alongside tri- and tetra-nucleotide-based structural and energy features. ChemEXIN outperforms existing methods with notable accuracy and precision. It achieves an accuracy of 92.5% for humans, 79.9% for mice, and 92.0% for worms, along with precision values of 92.0%, 79.6%, and 91.8% for the same organisms, respectively. These results represent a significant advancement in EI boundary annotations, with potential implications for understanding gene expression, regulation, and cellular functions.