Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data.

Long non-coding RNA (lncRNA), microRNA, and messenger RNA enable key regulations of various biological processes through a variety of diverse interaction mechanisms. Identifying the interactions and cross-talk between these heterogeneous RNA classes is essential in order to uncover the functional role of individual RNA transcripts, especially for unannotated and sparsely discovered RNA sequences with no known interactions. Recently, sequence-based deep learning and network embedding methods are gaining traction as high-performing and flexible approaches that can either predict RNA-RNA interactions from sequence or infer missing interactions from patterns that may exist in the network topology. However, most of the current methods have several limitations, e.g., the inability to perform inductive predictions, to distinguish the directionality of interactions, or to integrate various sequence, interaction, expression, and genomic annotation datasets. We proposed a novel deep learning framework, rna2rna, which learns from RNA sequences to produce a low-dimensional embedding that preserves proximities in both the interaction topology and the functional affinity topology. In this proposed embedding space, the two-part "source and target contexts"capture the receptive fields of each RNA transcript to encapsulate heterogeneous cross-talk interactions between lncRNAs and microRNAs. The proximity between RNAs in this embedding space also uncovers the second-order relationships that allow for accurate inference of novel directed interactions or functional similarities between any two RNA sequences. In a prospective evaluation, our method exhibits superior performance compared to state-of-art approaches at predicting missing interactions from several RNA-RNA interaction databases. Additional results suggest that our proposed framework can capture a manifold for heterogeneous RNA sequences to discover novel functional annotations.