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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011
摘要
Motivation: Several dynamic programming algorithms for predicting RNA structures with pseudoknots have been proposed that differ dramatically from one another in the classes of structures considered. Results: Here, we use the natural topological classification of RNA structures in terms of irreducible components that are embeddable in the surfaces of fixed genus. We add to the conventional secondary structures four building blocks of genus one in order to construct certain structures of arbitrarily high genus. A corresponding unambiguous multiple context-free grammar provides an efficient dynamic programming approach for energy minimization, partition function and 隨機 sampling. It admits a topology-dependent parametrization of pseudoknot penalties that increases the sensitivity and positive
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