Refining RNA-Seq Transcriptome Profiles with Transcript-Specific Minimal-Shared Regions

Accurate transcript quantification remains a critical challenge in RNA sequencing (RNA-seq) due to ambiguities in read assignment caused by shared regions among transcripts. Beyond shared exons among homologous isoforms, we identified the widespread presence of extensive undefined shared regions-overlapping sequences common to exons or transcripts of heterologous genes-across diverse species, including animals and plants. To overcome this issue, we developed a minimal-sharing strategy that partitions transcripts into unique sets of k-mers and systematically evaluates overlap across all transcripts. By isolating minimal-shared regions specific to each isoform, our approach reduces mapping uncertainty, thereby minimizing false positives and false negatives in transcript detection. Comprehensive evaluations using both in silico simulations and empirical sequencing data demonstrated that our method outperforms traditional probabilistic model-based quantification methods. This strategy improves isoform abundance estimates, enhances gene ontology annotations, and identifies distinct transcript isoforms critical for understanding biological processes, as exemplified in a Huntington's disease mouse model.