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arXiv 1204.0993q-bio.BMcond-mat.soft
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McGenus:一种预测具有伪结的RNA二级结构的Monte Carlo算法

McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots

M. Bon, C. Micheletti, H. Orland

摘要:

我们提出了McGenus,一个算法来预测RNA二级结构的伪结。该方法是基于RNA结构的分类,根据其拓扑属。McGenus可以处理多达1000个碱基的序列,并对它们的最小自由能结构进行高级随机搜索,允许非平凡的伪结拓扑。具体而言,McGenus采用了多马尔可夫链计划,用于最小化一般评分函数,其中不仅包括自由能对堆叠的贡献,循环罚款等,但也是一个现象学的惩罚的属的配对图。随机搜索策略的良好性能针对TT2NE被成功地验证,TT2NE使用相同的自由能参数化并执行穷举或部分穷举结构搜索,尽管对于短得多的序列(高达200个碱基)。接下来,将该方法应用于其他RNA集,包括广泛的tmRNA数据库,产生与现有算法竞争的结果。最后,它表明,McGenus强调可能的限制,在自由能评分功能。该算法可在http://ipht.cea.fr/rna/mcgenus.php作为网络服务器获得。

英文摘要:

We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free energy structure allowing for non trivial pseudoknot topologies. Specifically, McGenus employs a multiple Markov chain scheme for minimizing a general scoring function which includes not only free energy contributions for pair stacking, loop penalties, etc. but also a phenomenological penalty for the genus of the pairing graph. The good performance of the stochastic search strategy was successfully validated against TT2NE which uses the same free energy parametrization and performs exhaustive or partially exhaustive structure search, albeit for much shorter sequences (up to 200 bases). Next, the method was applied to other RNA sets, including an extensive tmRNA database, yielding results that are competitive with existing algorithms. Finally, it is shown that McGenus highlights possible limitations in the free energy scoring function. The algorithm is available as a web-server at http://ipht.cea.fr/rna/mcgenus.php .

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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