寻找水稻DNA编码与非编码区边界方法的比较研究

在分析DNA序列复杂度、预测基因编码区和非编码的DNA边界识别等问题中，以熵为基础构造的离散量度量提供了一种强有力的工具。为改进寻找水稻基因编码与非编码区边界的效率，本文提出了两个新的离散量度量（α-KL离散量与α-Jensen-Shannon 离散量），根据密码子的GC含量对氨基酸对应密码子构建了粗粒化向量。 比较了融合Jensen-Shannon 离散量、Jensen-Renyi 离散量、α-KL离散量和α-Jensen-Shannon 离散量等不同向量所获得的精度，结果表明，在对水稻基因编码区‘终止子’的识别效率上,构建的密码子粗粒化向量融合新引进的度量方法比Bernaola等人的方法(2000)提高了4~5倍。

Comparison Study on the Methods for Finding Borders Between Coding and Non-coding DNA Regions in Rice

Entropy-based divergence measures have provided an impelling tool in evaluating sequence complexity, predicting CpG island, and detecting borders between coding and non-coding DNA regions etc. In this paper, two new divergence measures: the alpha-KL divergence and the alpha-Jensen-Shannon divergence were defined and a coarse-graining vector of amino acids- corresponding codons was proposed according to codons GC-content, in order to improve the computational approach to finding borders between coding and non-coding in rice. A comparison of the accuracies gained by different vectors (the Jensen-Shannon divergence, the Jensen-Renyi divergence, the alpha-KL divergence and the alpha-Jensen -Shannon divergence) showed that recognition efficiency based on the new information measures with the vector coarse-graining increase by 4-5 times than that of Bernaola's method in the 'stop codon' of coding regions in rice.