Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.

ABSTRACT: It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often insufficient for learning features of specific ncRNA families or structural motifs. Aiming to reliably identify intrinsic transcription terminators in bacteria, we introduce a novel pre-training approach that uses inverse folding to generate training data for predicting or identifying a specific family or structural motif of ncRNA. We assess the ability of neural networks to identify secondary structure by systematic in silico mutagenesis experiments. In a study to identify intrinsic transcription terminators as functionally well-understood RNA s

SUBMITTER: Brandenburg VB 

PROVIDER: S-EPMC9262186 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature