Counting, Generating and Sampling Tree Alignments
Pairwise ordered tree alignment are combinatorial objects that appear in RNA secondary structure comparison. However, the usual representation of tree alignments as supertrees is ambiguous, i.e. two distinct supertrees may induce identical sets of matches between identical pairs of trees. This ambiguity is uninformative, and detrimental to any probabilistic analysis. In this work, we consider tree alignments up to equivalence. Our first result is a precise asymptotic enumeration of tree alignments, obtained from a context-free grammar by means of basic analytic combinatorics. Our second result focuses on alignments between two given ordered trees. By refining our grammar to align specific trees, we obtain a decomposition scheme for the space of alignments, and use it to design an efficient dynamic programming algorithm for sampling alignments under the Gibbs-Boltzmann probability distribution. This generalizes existing tree alignment algorithms, and opens the door for a probabilistic analysis of the space of suboptimal RNA secondary structures alignments.
KeywordsTree alignment RNA secondary structure Dynamic programming
- 3.Chauve, C., Courtiel, J., Ponty, Y.: Counting, generating and sampling tree alignments. In: ALCOB - 3rd International Conference on Algorithms for Computational Biology - 2016. Trujillo, Spain, Jun 2016. https://hal.inria.fr/hal-01154030
- 5.Dress, A., Morgenstern, B., Stoye, J.: The number of standard and of effective multiple alignments. Appl. Math. Lett. 11(4), 43–49 (1998). http://www.sciencedirect.com/science/article/pii/S0893965998000548MathSciNetCrossRefGoogle Scholar
- 8.Höchsmann, M., Töller, T., Giegerich, R., Kurtz, S.: Local similarity in RNA secondary structures. Proc. Ieee Comput. Soc. Bioinform Conf. 2, 159–168 (2003)Google Scholar
- 14.Vingron, M., Argos, P.: Determination of reliable regions in protein sequence alignments. Protein Eng. 3(7), 565–569 (1990). http://peds.oxfordjournals.org/content/3/7/565.abstractCrossRefGoogle Scholar
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