A systematic delineation of 3'UTR regulatory elements and their contextual associations

The 3'UTR encodes regulatory information that shapes transcript abundance and subcellular distribution, yet the underlying sequence rules remain incompletely defined. We quantified the effects of ∼2 million synthetic 3'UTR inserts in A549 and HCT116 cells on RNA output and nuclear-cytoplasmic partitioning. We identified a broad repertoire of short 2- to 8-nt elements. Their effects largely followed a dominant coupled axis linking high RNA output with cytoplasmic enrichment and low RNA output with nuclear enrichment and were predominantly additive. A context-aware deep learning model captures most of this behavior while revealing that strong context dependence is uncommon and emerges mainly in rare, extreme contexts consistent with higher-order constraints. Applying the model to ClinVar 3'UTR variants prioritizes a subset of pathogenic single-nucleotide variants (SNVs) with aberrant predicted effects, frequently explained by creation or disruption of splice-site-like U1 motifs that can trigger telescripting-associated readthrough, highlighting a potent route to 3'UTR-driven disease mechanisms. A record of this paper's transparent peer review process is included in the supplemental information.