Abstract
Dual graphs have been applied to model RNA secondary structures with pseudoknots, or intertwined base pairs. In a previous work, a linear-time algorithm was introduced to partition dual graphs into maximal topological components called blocks and determine whether each block contains a pseudoknot or not. This characterization allowed us to efficiently isolate smaller RNA fragments and classify them as pseudoknotted or pseudoknot-free regions, while keeping these sub-structures intact. In this paper we extend the partitioning algorithm by classifying a pseudoknot as either recursive or non-recursive. A pseudoknot is recursive if it contains independent regions or fragments. Each of these regions can be also identified by the modified algorithm, continuing with our current research in the development of a library of building blocks for RNA design by fragment assembly. Partitioning and classification of RNAs using dual graphs provide a systematic way for study of RNA structure and prediction.
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Acknowledgments
We would like to thank first the referees for improving the content of the paper. NIGMS support from award R35GM122562 to T. S. is gratefully acknowledged. The work of L. P. was supported by PSC-CUNY Award \(\#\) 61249-00-49 of the City University of New York.
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Appendix
Appendix
Let (a, b) represents an edge of a dual graph with end-vertices a and b.
We are next illustrating the output of the partitioning algorithm tested on the tRNA-like-structure dual graph (see Fig. 6).
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Petingi, L., Schlick, T. (2019). Graph-Theoretic Partitioning of RNAs and Classification of Pseudoknots. In: Holmes, I., Martín-Vide, C., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2019. Lecture Notes in Computer Science(), vol 11488. Springer, Cham. https://doi.org/10.1007/978-3-030-18174-1_5
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