Predictive models of subcellular localization of long RNAs

Abstract

Export to the cytoplasm is a key regulatory junction for both protein-coding mRNAs and long noncoding RNAs (lncRNAs), and cytoplasmic enrichment varies dramatically both within and between those groups. We used a new computational approach and RNA-seq data from human and mouse cells to quantify the genome-wide association between cytoplasmic/nuclear ratios of both gene groups and various factors, including expression levels, splicing efficiency, gene architecture, chromatin marks, and sequence elements. Splicing efficiency emerged as the main predictive factor, explaining up to a third of the variability in localization. Combination with other features allowed predictive models that could explain up to 45% of the variance for protein-coding genes and up to 34% for lncRNAs. Factors associated with localization were similar between lncRNAs and mRNAs with some important differences. Readily accessible features can thus be used to predict RNA localization.