Prediction of RNA-RNA interaction structure by centroids in the Boltzmann ensemble

TL;DR

This paper introduces a novel algorithm for predicting RNA-RNA interaction structures by computing the centroid in the Boltzmann ensemble, offering improved efficiency and accuracy over existing methods.

Contribution

The authors develop an efficient algorithm to predict the centroid of the Boltzmann ensemble for RNA-RNA interactions, enhancing structure prediction accuracy and computational speed.

Findings

Centroid prediction outperforms minimum free energy methods.

New sampling algorithm reduces computational complexity.

Implementation in piRNA improves prediction results.

Abstract

New high-throughput sequencing technologies have made it possible to pursue the advent of genome-wide transcriptomics. That progress combined with the recent discovery of regulatory non-coding RNAs (ncRNAs) has necessitated fast and accurate algorithms to predict RNA-RNA interaction probability and structure. Although there are algorithms to predict minimum free energy interaction secondary structure for two nucleic acids, little work has been done to exploit the information invested in the base pair probabilities to improve interaction structure prediction. In this paper, we present an algorithm to predict the Hamming centroid of the Boltzmann ensemble of interaction structures. We also present an efficient algorithm to sample interaction structures from the ensemble. Our sampling algorithm uses a balanced scheme for traversing indices which improves the running time of the Ding-Lawrence sampling algorithm. The Ding-Lawrence sampling algorithm has $O(n^2m^2)$ time complexity whereas our algorithm has $O((n+m)^2\log(n+m))$ time complexity, in which $n$ and $m$ are the lengths of input strands. We implemented our algorithm in a new version of {\tt piRNA} and compared our structure prediction results with competitors. Our centroid prediction outperforms competitor minimum-free-energy prediction algorithms on average.