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.

翻译：本文利用融入随机模型中的生物学领域知识，用于RNA二级结构的从头预测，以提升RNA序列与结构数据联合压缩的最新水平（Liu等，BMC Bioinformatics，2008）。此外，我们表明，反之亦然，压缩比可作为评估不同随机模型预测质量的廉价且稳健的代理指标，这可能有助于指导寻找更优的RNA结构预测模型。我们的研究基于专家级RNA二级结构随机上下文无关文法模型（Dowell & Eddy，BMC Bioinformatics，2004；Nebel & Scheid，Theory in Biosciences，2011），并结合不同（静态与自适应）规则概率模型及算术编码。我们提供了原型实现与广泛的经验评估，展示了文法特征及概率模型如何影响压缩比。