Deep learning predicts short non-coding RNA functions from only raw sequence data.

ABSTRACT: Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of seconda

SUBMITTER: Noviello TMR 

PROVIDER: S-EPMC7682815 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature