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

Teresa Maria Rosaria Noviello, Francesco Ceccarelli, Michele Ceccarelli, Luigi Cerulo

Abstract: Author summary 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. 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 secondary structure being a key determinant of function in RNA.