King-Fung Wong Thomas, Wing-Yan Cheung Brenda, Lam Tak-Wah, Yiu Siu-Ming
Department of Computer Science, The University of Hong Kong, Hong Kong.
BMC Proc. 2011 May 28;5 Suppl 2(Suppl 2):S2. doi: 10.1186/1753-6561-5-S2-S2.
Predicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps.
In this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem.
Based on an experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.
预测一个家族的新非编码RNA（ncRNA）可以通过将潜在候选序列与家族中具有已知序列和二级结构的成员进行比对来实现。现有工具要么只考虑序列相似性，要么无法处理带空位的局部比对。
在本文中，我们考虑了使用仿射空位罚分模型在查询RNA序列（具有已知二级结构）和目标序列（具有未知二级结构）之间找到最优局部结构比对的问题。我们提供了解决该问题的算法。
基于一项实验，我们表明，在某些ncRNA家族中，与使用全局比对或不带空位罚分模型的局部比对相比，考虑带空位罚分模型的局部结构比对能更有效地识别真正的匹配。
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