Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes

Abstract

There is growing evidence for the importance of 3 untranslated region (3UTR) dependent regulatory processes. However, our current human 3UTR catalogue is incomplete. Here, we developed a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3UTRs. We identify unannotated 3UTRs associated with 1,513 genes across 39 human tissues, with the greatest abundance found in brain. These unannotated 3UTRs were significantly enriched for RNA binding protein (RBP) motifs and exhibited high human lineage-specificity. We found that brain-specific unannotated 3UTRs were enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes were involved in synaptic function and brain- related disorders. Our data is shared through an online resource F3UTER (https://astx.shinyapps.io/F3UTER/). Overall, our data improves 3UTR annotation and provides novel insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.