RNA independent fragment partition method based on deep learning for RNA secondary structure prediction.

ABSTRACT: The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of co

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC9938198 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature