# RNA secondary structures: from ab initio prediction to better compression, and back

## TL;DR

This paper leverages biological knowledge and stochastic models to enhance RNA secondary structure prediction and compression, demonstrating that compression ratios can evaluate model quality effectively.

## Contribution

It introduces a novel approach combining stochastic context-free grammars with compression techniques to improve RNA structure prediction and evaluation.

## Key findings

- Improved compression ratios with expert stochastic models.
- Compression ratio correlates with prediction quality.
- Grammar features significantly impact compression performance.

## Abstract

In this paper, we use the biological domain knowledge incorporated into stochastic models for ab initio RNA secondary-structure prediction to improve the state of the art in joint compression of RNA sequence and structure data (Liu et al., BMC Bioinformatics, 2008). Moreover, we show that, conversely, compression ratio can serve as a cheap and robust proxy for comparing the prediction quality of different stochastic models, which may help guide the search for better RNA structure prediction models. Our results build on expert stochastic context-free grammar models of RNA secondary structures (Dowell & Eddy, BMC Bioinformatics, 2004; Nebel & Scheid, Theory in Biosciences, 2011) combined with different (static and adaptive) models for rule probabilities and arithmetic coding. We provide a prototype implementation and an extensive empirical evaluation, where we illustrate how grammar features and probability models affect compression ratios.