MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing
MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing
复制标题
DOI：
10.1093/bioinformatics/btm525
复制
发表时间：
2007-12-15
期刊：
影响因子：
5.8
通讯作者：
Krogh, Anders
中科院分区：
文献类型：
--
作者：
Lindgreen, Stinus;Gardner, Paul P.;Krogh, Anders
动机：随着更多非编码 RNA 的发现，RNA 分析方法的重要性也随之增加。由于 ncRNA 的结构与分子的功能密切相关，因此 RNA 结构预测程序是这个不断发展的研究领域的必要工具。此外，众所周知，RNA 结构在进化上通常比序列更保守。然而，现有的方法很少能够同时考虑多重序列比对和结构预测。结果：我们针对同时结构预测和RNA序列多重比对的问题提出了一种新的解决方案。 MASTR（结构 RNA 多重比对）算法在模拟退火框架中使用马尔可夫链蒙特卡罗，迭代改进一组 RNA 序列的序列比对和结构预测。这是通过最小化考虑序列保守、共变和碱基配对概率的组合成本函数来完成的。结果表明，该方法在准确性和计算效率方面与当今可用的类似程序相比都非常有竞争力。
Motivation: As more noncoding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction.Result: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency.