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MOTIVATION: RNAs play an important role in many biological processes and
knowing their structure is important in understanding their function. Due
to difficulties in the experimental determination of RNA secondary
structure, the methods of theoretical prediction for known sequences are
often used. Although many different algorithms for such predictions have
been developed, this problem has not yet been solved. It is thus necessary
to develop new methods for predicting RNA secondary structure. The
most-used at present is Zuker's algorithm which can be used to determine
the minimum free energy secondary structure. However many RNA secondary
structures verified by experiments are not consistent with the minimum free
energy secondary structures. In order to solve this problem, a method used
to search a group of secondary structures whose free energy is close to the
global minimum free energy was developed by Zuker in 1989. When considering
a group of secondary structures, if there is no experimental data, we
cannot tell which one is better than the others. This case also occurs in
combinatorial and heuristic methods. These two kinds of methods have
several weaknesses. Here we show how the central limit theorem can be used
to solve these problems. RESULTS: An algorithm for predicting RNA secondary
structure based on helical regions distribution is presented, which can be
used to find the most probable secondary structure for a given RNA
sequence. It consists of three steps. First, list all possible helical
regions. Second, according to central limit theorem, estimate the
occurrence probability of every helical region based on the Monte Carlo
simulation. Third, add the helical region with the biggest probability to
the current structure and eliminate the helical regions incompatible with
the current structure. The above processes can be repeated until no more
helical regions can be added. Take the current structure as the final RNA
secondary structure. In order to demonstrate the confidence of the program,
a test on three RNA sequences: tRNAPhe, Pre-tRNATyr, and Tetrahymena
ribosomal RNA intervening sequence, is performed. AVAILABILITY: The program
is written in Turbo Pascal 7.0. The source code is available upon request.
CONTACT: Wujj@nic.bmi.ac.cn or Liwj@mail.bmi.ac.cn
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microRNA(miRNA)是一类不编码蛋白的调控小分子RNA,在真核生物中发挥着广泛而重要的调控功能.由于miRNA的表达具有时空特异性,因而通过计算方法预测miRNA而后有针对性的实验验证是miRNA发现的一条重要途径.降低假阳性率是miRNA预测方法面临的重要挑战.本研究采用集成学习方法构建预测miRNA前体的分类器SVMbagging,对训练集、测试集和独立测试集的结果表明,本研究的方法性能稳健、假阳性率低,具有很好的泛化能力,尤其是当阈值取0.9时,特异性高达99.90%,敏感性在26%以上,适合于全基因组预测.采用SVMbagging在人全基因组中预测miRNA前体,当取阈值0.9时,得到14933个可能的miRNA前体.通过与高通量小RNA测序数据的比较,发现其中4481个miRNA前体具有完全匹配的小RNA序列,与理论估计的真阳性数值非常接近.最后,对32个可能的miRNA进行实验验证,确定其中2条为真实的miRNA. 相似文献
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