On an enhancement of RNA probing data using Information Theory

TL;DR

This paper introduces an information-theoretic framework using an ensemble tree approach to enhance RNA secondary structure identification from chemical probing data, achieving over 90% accuracy in target detection.

Contribution

It presents a novel hierarchical querying method based on information entropy to improve RNA structure prediction from probing data.

Findings

Leaves of the ensemble tree contain highly probable sub-structures.

The method correctly identifies the target structure with over 90% probability.

The approach effectively relates local and global probing data using RNA modularity.

Abstract

Identifying the secondary structure of an RNA is crucial for understanding its diverse regulatory functions. This paper focuses on how to enhance target identification in a Boltzmann ensemble of structures via chemical probing data. We employ an information-theoretic approach to solve the problem, via considering a variant of the R\'{e}nyi-Ulam game. Our framework is centered around the ensemble tree, a hierarchical bi-partition of the input ensemble, that is constructed by recursively querying about whether or not a base pair of maximum information entropy is contained in the target. These queries are answered via relating local with global probing data, employing the modularity in RNA secondary structures. We present that leaves of the tree are comprised of sub-samples exhibiting a distinguished structure with high probability. In particular, for a Boltzmann ensemble incorporating probing data, which is well established in the literature, the probability of our framework correctly identifying the target in the leaf is greater than $90\%$.