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Algorithms for Molecular Biology

, 6:24

First Online: 12 October 2011Received: 20 April 2011Accepted: 12 October 2011

Abstract

BackgroundRandom biological sequences are a topic of great interest in genome analysis since, according to a powerful paradigm, they represent the background noise from which the actual biological information must differentiate. Accordingly, the generation of random sequences has been investigated for a long time. Similarly, random object of a more complicated structure like RNA molecules or proteins are of interest.

ResultsIn this article, we present a new general framework for deriving algorithms for the non-uniform random generation of combinatorial objects according to the encoding and probability distribution implied by a stochastic context-free grammar. Briefly, the framework extends on the well-known recursive method for uniform random generation and uses the popular framework of admissible specifications of combinatorial classes, introducing weighted combinatorial classes to allow for the non-uniform generation by means of unranking. This framework is used to derive an algorithm for the generation of RNA secondary structures of a given fixed size. We address the random generation of these structures according to a realistic distribution obtained from real-life data by using a very detailed context-free grammar that models the class of RNA secondary structures by distinguishing between all known motifs in RNA structure. Compared to well-known sampling approaches used in several structure prediction tools such as SFold ours has two major advantages: Firstly, after a preprocessing step in time O n 2 Open image in new window for the computation of all weighted class sizes needed, with our approach a set of m random secondary structures of a given structure size n can be computed in worst-case time complexity O m ⋅ n ⋅ log n Open image in new window while other algorithms typically have a runtime in O m ⋅ n 2 Open image in new window. Secondly, our approach works with integer arithmetic only which is faster and saves us from all the discomforting details of using floating point arithmetic with logarithmized probabilities.

ConclusionA number of experimental results shows that our random generation method produces realistic output, at least with respect to the appearance of the different structural motifs. The algorithm is available as a webservice at http:-wwwagak.cs.uni-kl.de-NonUniRandGen and can be used for generating random secondary structures of any specified RNA type. A link to download an implementation of our method in Wolfram Mathematica can be found there, too.

KeywordsRandom generation stochastic context-free grammars RNA secondary structures Electronic supplementary materialThe online version of this article doi:10.1186-1748-7188-6-24 contains supplementary material, which is available to authorized users.

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Autor: Markus E Nebel - Anika Scheid - Frank Weinberg

Fuente: https://link.springer.com/







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