RNA-GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing.

ABSTRACT: Subcellular localization is essential to RNA biogenesis, processing, and function across the gene expression life cycle. However, the specific nucleotide sequence motifs that direct RNA localization are incompletely understood. Fortunately, new sequencing technologies have provided transcriptome-wide atlases of RNA localization, creating an opportunity to leverage computational modeling. Here we present RNA-GPS, a new machine learning model that uses nucleotide-level features to predict RNA localization across eight different subcellular locations-the first to provide such a wide range of predictions. RNA-GPS's design enables high-throughput sequence ablation and feature importance analyses to probe the sequence motifs that drive localization prediction. We find localization informative mo

SUBMITTER: Wu KE 

PROVIDER: S-EPMC7297119 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature