Supplementary file 1_Entropy-driven machine learning for deciphering mRNA rearrangement by splicing in complex microorganisms.docx

Intron retention (IR) is a specific type of alternative splicing in which introns remain unspliced in the mature mRNA. The exact regulatory code behind intron splicing remains not fully deciphered, and unraveling these mechanisms is critical to uncovering the foundations of genetic disorders: a substantial fraction of disease-causing mutations disrupt intron splicing.</p>Methods<p>We applied explainable machine learning (xML) models to intronic sequences from four species of the ciliate genus Tetrahymena (T. thermophila, T. malaccensis, T. borealis and T. elliotti), which allow the intron retention process to be analysed without tissue-specific confounders.

Retained introns (RIs) and constitutively spliced introns (CSIs) were extracted through an automated Galaxy pipeline and described by several features of the intronic sequences, including the absence of repetitive nucleotide motifs – quantified as entropy – the GC content, the complexity of the secondary structures as estimated by the Lempel–Ziv (LZ) measure, and the effective distance between the branch point and the 3′ splice site.