N4: A precise and highly sensitive promoter predictor using neural network fed by nearest neighbors
N4: A precise and highly sensitive promoter predictor using neural network fed by nearest neighbors
复制标题
DOI：
10.1266/ggs.84.425
复制
发表时间：
2009-12-01
影响因子：
1.1
通讯作者：
Purmasjedi, Malihe
中科院分区：
文献类型：
--
作者：
Askary, Amjad;Masoudi-Nejad, Ali;Purmasjedi, Malihe
启动子是位于转录起始位点（TSS）附近的基因组区域，通过作为RNA聚合酶II复合物的直接对接平台，在决定转录起始速率方面发挥关键作用。在后基因组时代，正确的基因预测已成为基因组注释的最大挑战之一。非物种依赖性启动子预测工具在元基因组学中也是有用的，因为转录数据将无法用于未培养的微生物。原核基因组的启动子预测由于其组织特性而提出了独特的挑战。目前已经发展了几种预测原核生物基因组启动子区的方法，包括序列基序识别算法、人工神经网络算法和基于基因组结构的算法。然而，它们都不能同时满足灵敏度和精确度的标准。在这项工作中，我们提出了一个改进的人工神经网络喂养的最近邻的基础上DNA双链体的稳定性，命名为N4，它可以预测大肠杆菌的转录起始位点的灵敏度和精度都在94%以上，优于大多数现有的算法。
Promoters, the genomic regions proximal to the transcriptional start sites (TSSs) play pivotal roles in determining the rate of transcription initiation by serving as direct docking platforms for the RNA polymerase II complex. In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Species-independent promoter prediction tools could also be useful in meta-genomics, since transcription data will not be available for microorganisms which are not cultivated. Promoter prediction in prokaryotic genomes presents unique challenges owing to their organizational properties. Several methods have been developed to predict the promoter regions of genomes in prokaryotes, including algorithms for recognition of sequence motifs, artificial neural networks, and algorithms based on genome's structure. However, none of them satisfies both criteria of sensitivity and precision. In this work, we present a modified artificial neural network fed by nearest neighbors based on DNA duplex stability, named N4, which can predict the transcription start sites of Escherichia coli with sensitivity and precision both above 94%, better than most of the existed algorithms.