Predicting RNA secondary structure by a neural network: what features may be learned?

PeerJ

article

Elizaveta I. Grigorashvili¹  Zoe S. Chervontseva²  Mikhail S. Gelfand¹ 

[1] Center of Molecular and Cellular Biology, Skolkovo Institute of Science and Technology;Institute of Information Transmission Problems

关键词: RNA secondary structure prediction;    Neural networks;    Representation learning;   

DOI  :  10.7717/peerj.14335

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data may be learned by such models. Here, we describe PredPair, a deep learning neural network trained to predict base pairs in RNA structure from sequence alone, without any incorporated prior knowledge, such as the stacking energies or possible spatial structures. PredPair learned the Watson-Crick and wobble base-pairing rules and created an internal representation of the stacking energies and helices. Application to independent experimental (DMS-Seq) data on nucleotide accessibility in mRNA showed that the nucleotides predicted as paired indeed tend to be involved in the RNA structure. The performance of the constructed model was comparable with the state-of-the-art method based on the thermodynamic approach, but with a higher false positives rate. On the other hand, it successfully predicted pseudoknots. t-SNE clusters of embeddings of RNA sequences created by PredPair tend to contain embeddings from particular Rfam families, supporting the predictions of PredPair being in line with biological classification.

【 授权许可】

CC BY