A bijective inference network for interpretable identification of RNA N6-methyladenosine modification sites

Abstract

The accurate identification of N${}^6$-methyladenosine (m${}^6$A) modification sites is crucial for unraveling various functional mechanisms. While existing methods primarily focus on learning high-quality embeddings of RNA sequences for this task, few of them consider incorporating specific RNA secondary structures, limiting their interpretability for in-depth post-transcriptional analysis. In this work, we introduce a novel bijective inference network, named m${}^6$A-BIN, which integrates RNA sequences and secondary structures within a unified parameter-shared framework, enhancing the accuracy of m${}^6$A modification site identification through the auxiliary supervision of RNA secondary structures. To begin with, m${}^6$A-BIN constructs sequential and structural graphs from RNA sequences and secondary structures, respectively. Bijective mapping functions are then specifically designed to couple the procedures of graph representation learning and interpretable dependency inference, providing informative supervision for learning sequential and structural embeddings of RNA. By fusing these two types of RNA embeddings, m${}^6$A-BIN efficiently performs the identification task. The attribution phase of m${}^6$A-BIN further ascribes the prediction results to nucleotide dependencies acquired during the interpretable dependency inference, including RNA sequence and structural patterns, thereby enhancing its interpretability. Extensive experimental results demonstrate the promising performance of m${}^6$A-BIN, showcasing its efficacy in terms of both accuracy and interpretability for the identification of novel m${}^6$A modification sites.