A Combinatorial Framework for Designing (Pseudoknotted) RNA Algorithms

TL;DR

This paper introduces a unified hypergraph-based framework for RNA folding algorithms, simplifying existing methods and enabling new applications for pseudoknotted structures with comparable complexity to traditional approaches.

Contribution

It extends hypergraph representation to unify RNA folding algorithms, introduces a combinatorial methodology for unambiguous decomposition, and proposes new algorithms for generalized moments in pseudoknotted structures.

Findings

Framework simplifies RNA folding algorithms with pseudoknots.

New algorithms for generalized moments in weighted distributions.

Applications to secondary structures, pseudoknots, and kissing hairpins.

Abstract

We extend an hypergraph representation, introduced by Finkelstein and Roytberg, to unify dynamic programming algorithms in the context of RNA folding with pseudoknots. Classic applications of RNA dynamic programming energy minimization, partition function, base-pair probabilities...) are reformulated within this framework, giving rise to very simple algorithms. This reformulation allows one to conceptually detach the conformation space/energy model -- captured by the hypergraph model -- from the specific application, assuming unambiguity of the decomposition. To ensure the latter property, we propose a new combinatorial methodology based on generating functions. We extend the set of generic applications by proposing an exact algorithm for extracting generalized moments in weighted distribution, generalizing a prior contribution by Miklos and al. Finally, we illustrate our full-fledged programme on three exemplary conformation spaces (secondary structures, Akutsu's simple type pseudoknots and kissing hairpins). This readily gives sets of algorithms that are either novel or have complexity comparable to classic implementations for minimization and Boltzmann ensemble applications of dynamic programming.