A Simple, Practical and Complete O(\frac{n3}{ \log n) -Time Algorithm for RNA Folding Using the Four-Russians Speedup

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Abstract

The problem of computationally predicting the secondary structure (or folding) of RNA molecules was first introduced more than thirty years ago and yet continues to be an area of active research and development. The basic RNA-folding problem of finding a maximum cardinality, non-crossing, matching of complimentary nucleotides in an RNA sequence of length n, has an O(n 3)-time dynamic programming solution that is widely applied. It is known that an o(n 3) worst-case time solution is possible, but the published and suggested methods are complex and have not been established to be practical. Significant practical improvements to the original dynamic programming method have been introduced, but they retain the O(n 3) worst-case time bound when n is the only problem-parameter used in the bound. Surprisingly, the most widely-used, general technique to achieve a worst-case (and often practical) speed up of dynamic programming, the Four-Russians technique, has not been previously applied to the RNA-folding problem. This is perhaps due to technical issues in adapting the technique to RNA-folding. In this paper, we give a simple, complete, and practical Four-Russians algorithm for the basic RNA-folding problem, achieving a worst-case time-bound of O(n 3/log(n)). We show that this time-bound can also be obtained for richer nucleotide matching scoring-schemes, and that the method achieves consistent speed-ups in practice. The contribution is both theoretical and practical, since the basic RNA-folding problem is often solved multiple times in the inner-loop of more complex algorithms, and for long RNA molecules in the study of RNA virus genomes. © 2009 Springer Berlin Heidelberg.

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Frid, Y., & Gusfield, D. (2009). A Simple, Practical and Complete O(\frac{n3}{ \log n) -Time Algorithm for RNA Folding Using the Four-Russians Speedup. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5724 LNBI, pp. 97–107). https://doi.org/10.1007/978-3-642-04241-6_9

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