Eukaryotic promoter prediction based on relative entropy and positional information
Eukaryotic promoter prediction based on relative entropy and positional information
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DOI：
10.1103/physreve.75.041908
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发表时间：
2007-04-01
影响因子：
2.4
通讯作者：
Yan, Hong
中科院分区：
文献类型：
--
作者：
Wu, Shuanhu;Xie, Xudong;Yan, Hong
真核生物启动子预测是DNA序列分析中最重要的问题之一，也是一个非常困难的问题。虽然已经提出了许多算法，但它们的性能仍然受到低灵敏度和高误报的限制。我们提出了一种提高启动子区域预测性能的方法。我们专注于DNA序列中不同功能区域的最有效特征的选择。我们的特征选择算法是基于相对熵或Kullback-Leibler分歧，并结合位置特异性信息的启动子区域预测系统的开发。对大基因组序列的测试结果以及与PromoterInspector和DragonPromoter的比较表明，该算法在预测启动子区域方面具有较高的灵敏度和特异性。
The eukaryotic promoter prediction is one of the most important problems in DNA sequence analysis, but also a very difficult one. Although a number of algorithms have been proposed, their performances are still limited by low sensitivities and high false positives. We present a method for improving the performance of promoter regions prediction. We focus on the selection of most effective features for different functional regions in DNA sequences. Our feature selection algorithm is based on relative entropy or Kullback-Leibler divergence, and a system combined with position-specific information for promoter regions prediction is developed. The results of testing on large genomic sequences and comparisons with the PromoterInspector and Dragon Promoter Finder show that our algorithm is efficient with higher sensitivity and specificity in predicting promoter regions.