Efficient Full-Length RNA Isoform Reconstruction with ISAtools

Abstract Accurate identification and quantification of full-length RNA isoforms remain challenging in long-read RNA sequencing due to sequencing errors, complex splicing, and incomplete annotations. We present ISAtools, a sequencing data-driven framework that leverages weakly supervised static references to reconstruct and quantify full-length isoforms, including their splicing structures and transcript boundaries. Benchmarking on simulated, SIRV, and biological datasets shows that ISAtools achieves high accuracy across varying sequencing depths, annotation completeness, and transcriptomic complexity, while maintaining fast runtime and low memory usage. These results demonstrate that ISAtools enables efficient and accurate identification and quantification of full-length RNA isoforms from high-throughput long-read RNA sequencing.