Abstract
Stochastic context-free grammars (SCFGs) were first established in the context of natural language modelling, and only later found their applications in RNA secondary structure prediction. In this chapter, we discuss the basic SCFG algorithms (CYK and inside–outside algorithms) in an application-centered manner and use the pfold grammar as a case study to show how the algorithms can be adapted to a grammar in a nonstandard form. We extend our discussion to the use of grammars with additional information (such as evolutionary information) to improve the quality of predictions. Finally, we provide a brief survey of programs that use stochastic context-free grammars for RNA secondary structure prediction and modelling.
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Notes
- 1.
The symbol∗ is the Kleene star operator; it is a widely used regular expression and denotes any (possibly empty) string that is produced by concatenating elements drawn from the set. The elements can occur any number of times and in any order in this string.
- 2.
In practice, we need not even store the array of backpointers at all. Starting from P[1, n, S], one can simply do the calculations “backwards” and determine which choice must have been made in each step.
- 3.
They can also be sequence-structure pairs, depending on the model.
- 4.
A similar approach has recently been applied to a thermodynamic model, see [7].
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Acknowledgments
ZS would like to thank Robert Giegerich and Paula Tataru for their comments on the manuscript, and Christine Heitsch and her group at Georgia Tech for useful discussions.
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Sükösd, Z., Andersen, E.S., Lyngsø, R. (2014). SCFGs in RNA Secondary Structure Prediction : A Hands-on Approach. In: Gorodkin, J., Ruzzo, W. (eds) RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods. Methods in Molecular Biology, vol 1097. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-709-9_8
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