RNA-RNA interaction prediction and antisense RNA target search
RNA-RNA interaction prediction and antisense RNA target search
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DOI：
10.1089/cmb.2006.13.267
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发表时间：
2006-03-01
影响因子：
1.7
通讯作者：
Zhang, KH
中科院分区：
文献类型：
--
作者：
Alkan, C;Karakoç, E;Zhang, KH
最近的研究表明，在转录后基因调控中存在特殊的非编码“反义”RNA已受到相当大的关注。这些RNA是天然合成的，以控制C中的基因表达。elegans、果蝇和其他生物体;已知它们调节E.大肠杆菌也是。小RNA也被人工构建，以敲除人类和其他生物体中感兴趣的基因，以便更多地了解它们的功能。虽然有许多算法用于预测单个RNA分子的二级结构，但不存在这样的算法用于可靠地预测两个相互作用的RNA分子的联合二级结构或测量这样的联合结构的稳定性。本文描述了反义RNA与其靶mRNA之间的RNA-RNA相互作用预测（RIP）问题，并提出了有效的算法来解决该问题，我们的算法在许多复杂的能量模型下最小化两个RNA分子之间的联合自由能。由于我们最精确的方法所需的计算资源对于长RNA分子是禁止的，我们还描述了如何通过一些启发式方法来加快我们的技术，同时在实验上保持原始的准确性。配备了这种快速的方法，我们应用我们的方法来发现相关基因组序列中任何给定的反义RNA的靶点。
Recent studies demonstrating the existence of special noncoding "antisense" RNAs used in post transcriptional gene regulation have received considerable attention. These RNAs are synthesized naturally to control gene expression in C. elegans, Drosophila, and other organisms; they are known to regulate plasmid copy numbers in E. coli as well. Small RNAs have also been artificially constructed to knock out genes of interest in humans and other organisms for the purpose of finding out more about their functions. Although there are a number of algorithms for predicting the secondary structure of a single RNA molecule, no such algorithm exists for reliably predicting the joint secondary structure of two interacting RNA molecules or measuring the stability of such a joint structure. In this paper, we describe the RNA-RNA interaction prediction (RIP) problem between an antisense RNA and its target mRNA and develop efficient algorithms to solve it. Our algorithms minimize the joint free energy between the two RNA molecules under a number of energy models with growing complexity. Because the computational resources needed by our most accurate approach is prohibitive for long RNA molecules, we also describe how to speed up our techniques through a number of heuristic approaches while experimentally maintaining the original accuracy. Equipped with this fast approach, we apply our method to discover targets for any given antisense RNA in the associated genome sequence.