Target prediction and a statistical sampling algorithm for RNA-RNA interaction

TL;DR

This paper introduces rip2.0, a program that predicts RNA-RNA interaction sites by combining energetic target prediction with a statistical sampling method to characterize the Boltzmann ensemble of structures.

Contribution

It presents a novel algorithm integrating energetic target prediction with statistical sampling for RNA-RNA interactions, improving understanding of structural ensembles.

Findings

Predicts energetically favorable RNA interaction sites.

Provides statistical characterization of RNA interaction ensembles.

Implemented an efficient dynamic programming algorithm.

Abstract

It has been proven that the accessibility of the target sites has a critical influence for miRNA and siRNA. In this paper, we present a program, rip2.0, not only the energetically most favorable targets site based on the hybrid-probability, but also a statistical sampling structure to illustrate the statistical characterization and representation of the Boltzmann ensemble of RNA-RNA interaction structures. The outputs are retrieved via backtracing an improved dynamic programming solution for the partition function based on the approach of Huang et al. (Bioinformatics). The $O(N^6)$ time and $O(N^4)$ space algorithm is implemented in C (available from \url{http://www.combinatorics.cn/cbpc/rip2.html})