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

Manato Akiyama, Yasubumi Sakakibara

研究成果: Article査読

抄録

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 sequences are embedded for each base. We call this 'informative base embedding' and use it to achieve accuracies superior to those of existing state-of-The-Art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman-Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of O(n2) instead of the O(n6) time complexity of the naive implementation of Sankoff-style algorithm for input RNA sequence of length n.

本文言語English
論文番号lqac012
ジャーナルNAR Genomics and Bioinformatics
4
1
DOI
出版ステータスPublished - 2022 3月 1

ASJC Scopus subject areas

  • 遺伝学
  • 構造生物学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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