SpliSync: Genomic language model-driven splice site correction of long RNA sequencing reads

Abstract

Long RNA sequencing reads are rapidly replacing short reads in transcriptomic analyses, enabling full-length transcript sequencing and better identification of isoforms, alternative splicing events, and other transcript variants. However, their higher sequencing error rates can cause misalignments, especially at splice junctions, reducing the accuracy of transcript reconstruction and analysis. We developed SpliSync, a genomic language model-driven method for splice site correction that integrates a pre-trained genomic sequence model (HyenaDNA), alignment data, and a U-net architecture to predict splice sites at nucleotide resolution. SpliSync substantially improved the precision of RNA long-read alignments by 27%-194% across diverse datasets and consistently outperformed competing tools. As a preprocessing step, it increased alternative splicing detection accuracy by 26%-330%. In contrast, its benefit for transcript reconstruction was limited, likely due to the tools built-in correction mechanisms. The code was developed in Python using the PyTorch package, and is freely available at https://github.com/splicebox/SpliSync.