Population-scale interpretation of RNA isoform diversity enabled by Isopedia

Abstract Alternative splicing generates extensive transcriptomic complexity, yet “novelty” is often inflated because of incomplete reference annotations, with 20-70% of transcripts in RNA-Seq studies labeled as novel. Isopedia provides an expandable data structure for reference-agnostic isoform annotation, which we demonstrate here through a population-scale catalog of 1,007 long-read datasets spanning 37 diverse biological contexts. By transitioning from reference-dependent to evidence-weighted annotation, Isopedia provides the frequency-based context necessary to distinguish stochastic noise from biologically active isoforms. In HG002 benchmarks, Isopedia reduced apparent isoform novelty by up to 26-fold, achieving a >95% annotation rate even for low-abundance isoforms typically missed by standard catalogs. The framework further supports systematic exploration of challenging loci such as pseudogenes and gene fusions. Isopedia transforms isoform discovery into a systematic interpretation of the human transcriptome, providing a critical foundation for clinical and functional RNA research. Isopedia is open source and freely available: https://github.com/zhengxinchang/isopedia .