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



Onokpasa, Evarista, Wild, Sebastian ORCID: 0000-0002-6061-9177 and Wong, Prudence WH ORCID: 0000-0001-7935-7245
(2023) RNA secondary structures: from ab initio prediction to better compression, and back. In: 2023 Data Compression Conference (DCC), 2023-3-21 - 2023-3-24.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 12 Jun 2023 15:10
Last Modified: 09 Jun 2024 12:52
DOI: 10.1109/DCC55655.2023.00036
Open Access URL: https://arxiv.org/pdf/2302.11669.pdf
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3170952