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

ABSTRACT: Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, 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 this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-training 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 s

SUBMITTER: Akiyama M 

PROVIDER: S-EPMC8862729 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature