Consensus RNA secondary structure prediction using information of neighbouring columns and principal component analysis

Abstract

RNA is a family of biological macromolecules. It is important to all kinds of biological processes. RNA structure is closely related to its functions. Hence, determining the structure is invaluable in understanding genetic diseases and creating drugs. Nowadays, RNA secondary structure prediction is a field yet to be researched. In this paper, we present a novel method using RNA sequence alignment to predict a consensus RNA secondary structure. In essence, the goal of the method is to give a prediction about whether any two columns of an alignment correspond to a base pair or not, using the information provided by the alignment. The information includes the covariation score, the fraction of complementary nucleotides and the consensus probability matrix of the column pair and those of its neighbours. Then principal component analysis is applied to overcome the problem of over-fitting. A comparison of our method and other consensus RNA secondary structure prediction methods including NeCFold, ELMFold, KnetFold, PFold and RNAalifold, in 47 families from Rfam (version 11.0) is performed. Results show that our method surpasses the other methods in terms of Matthews correlation coefficient, sensitivity and selectivity.