IRCAS: a novel end-to-end approach to identify, rectify, and classify comprehensive alternative splicing events in a transcriptome without genome reference.

Abstract

Alternative splicing (AS) is a fundamental posttranscriptional mechanism that amplifies proteomic diversity and enables adaptive responses across eukaryotes. Current AS detection methods rely heavily on reference genomes, limiting their applicability to non-model organisms. Existing reference-free approaches suffer from inaccurate splice site prediction and treat detection and classification as separate processes, resulting in cascading errors. We present IRCAS, an integrated end-to-end framework for reference-free AS analysis, comprising three modules: identification, rectification, and classification. IRCAS employs colored de Bruijn graphs for AS detection, an attention-based convolutional neural network for splice site rectification, and a hybrid graph neural network combining graph attention network and Transformer layers for classification. Evaluation across four species demonstrates substantial improvements: splice site accuracy increased to 92%-96% versus 50%-55% for existing methods, and end-to-end inference accuracy reached 83.4% on rice (fine-tuned) compared to 44.7% for the previous best method. IRCAS establishes a new benchmark for reference-free AS detection in non-model organisms.