RNAirport: a deep neural network–based database characterizing representative gene models in plants

Abstract

A 5′-leader, known initially as the 5′-untranslated region, contains multiple isoforms due to alternative splicing (aS) and alternative transcription start site (aTSS). Therefore, a representative 5′-leader is demanded to examine the embedded RNA regulatory elements in controlling translation efficiency. Here, we develop a ranking algorithm and a deep-learning model to annotate representative 5′-leaders for five plant species. We rank the intra-sample and inter-sample frequency of aS-mediated transcript isoforms using the Kruskal–Wallis test–based algorithm and identify the representative aS-5′-leader. To further assign a representative 5′-end, we train the deep-learning model 5′leaderP to learn aTSS-mediated 5′-end distribution patterns from cap-analysis gene expression data. The model accurately predicts the 5′-end, confirmed experimentally in Arabidopsis and rice. The representative 5′-leader-contained gene models and 5′leaderP can be accessed at RNAirport (http://www.rnairport.com/leader5P/). This stage 1 5′-leader annotation records 5′-leader diversity and will pave the way to Ribo-Seq open-reading frame annotation, identical to the project recently initiated by human GENCODE.