Informative RNA-base embedding for functional RNA structural alignment and clustering by deep representation learning

Abstract

Effective embedding is being actively conducted by applying deep learning to biomolecular information. Obtaining better embedding enhances the quality of downstream analysis such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations, and apply it to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-learning algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this "informative base embedding" and use it to achieve accuracy superior to that of existing state-of-the-art methods in RNA structural alignment and RNA family clustering tasks. Furthermore, by performing RNA sequence alignment combining this informative base embedding with a simple Needleman-Wunsch alignment algorithm, we succeed in calculating a structural alignment in a time complexity O(n2) instead of the O(n6) time complexity of Sankoff-style algorithms.