iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features
iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features
复制标题
iPromoter-2L2.0：结合平滑切割窗口算法和基于序列的特征来识别启动子及其类型
DOI：
10.1016/j.omtn.2019.08.008
复制
发表时间：
2019-12-06
影响因子：
8.8
通讯作者：
Li, Kai
中科院分区：
文献类型：
--
作者：
Liu, Bin;Li, Kai
启动子是位于DNA序列特定位置的短片段，在基因转录调控中起着重要作用。它们可以分为六种类型（sigma（24）; sigma（28）; sigma（32）; sigma（38）; sigma（54）; sigma（70））。最近，一个名为“iPromoter-2L”的预测器被构建用于预测启动子及其六种类型，这是第一个预测所有六种类型的启动子的方法。然而，其预测质量仍需要进一步提高，以满足实际应用的需求。在这项研究中，我们提出了平滑切割窗口算法来寻找窗口片段的DNA序列的基础上的保守性得分捕获启动子的序列模式。对于每个窗口片段，通过使用kmer和PseKNC提取区分特征。结合支持向量机，构建不同的预测器，然后根据它们的距离聚类成几个组。最后，构建了一个新的预测器iPromoter-2L 2. 0，用于识别启动子及其六种类型，该预测器是基于从聚类组中选择的关键预测器通过集成学习开发的。结果表明，iPromoter-2L 2. 0在启动子预测和6种启动子类型鉴定方面均优于现有方法，表明iPromoter-2L 2. 0将有助于基因组学分析。
Promoters are short regions at specific locations of DNA sequences, which are playing key roles in directing gene transcription. They can be grouped into six types (sigma(24); sigma(28); sigma(32); sigma(38); sigma(54); sigma(70)). Recently, a predictor called "iPromoter-2L" was constructed to predict the promoters and their six types, which is the first approach to predict all the six types of promoters. However, its predictive quality still needs to be further improved for real-world application requirement. In this study, we proposed the smoothing cutting window algorithm to find the window fragments of the DNA sequences based on the conservation scores to capture the sequence patterns of promoters. For each window fragment, the discriminative features were extracted by using kmer and PseKNC. Combined with support vector machines (SVMs), different predictors were constructed and then clustered into several groups based on their distances. Finally, a new predictor called iPromoter-2L2.0 was constructed to identify the promoters and their six types, which was developed by ensemble learning based on the key predictors selected from the cluster groups. The results showed that iPromoter-2L2.0 outperformed other existing methods for both promoter prediction and identification of their six types, indicating that iPromoter-2L2.0 will be helpful for genomics analysis.