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Identification of prokaryotic promoters and their strength by integrating heterogeneous features

机译：通过整合异质特征来鉴定原核促进剂及其强度

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摘要

The promoter is a regulatory DNA region and important for gene transcriptional regulation. It is located near the transcription start site (TSS) upstream of the corresponding gene. In the post-genomics era, the availability of data makes it possible to build computational models for robustly detecting the promoters as these models are expected to be helpful for academia and drug discovery. Until recently, developed models focused only on discriminating the sequences into promoter and non-promoter. However, promoter predictors can be further improved by considering weak and strong promoter classification. In this work, we introduce a hybrid model, named iPSW(PseDNC-DL), for identification of prokaryotic promoters and their strength. It combines a convolutional neural network with a pseudo-di-nucleotide composition (PseDNC). The proposed model iPSW(PseDNC-DL) has been evaluated on the benchmark datasets and outperformed the current state-of-the-art models in both tasks namely promoter identification and promoter strength identification. The developed tool iPSW(PseDNC-DL) has been constructed in a web server and made freely available at https://home.jbnu.ac.kr/NSCL/PseDNC-DL.htm
机译：启动子是一种调节性DNA区域，对于基因转录调节很重要。它位于相应基因上游的转录开始部位（TSS）附近。在后基因组学时，数据的可用性使得可以建立鲁棒地检测启动子的计算模型，因为这些模型有助于学术和药物发现。直到最近，开发模型仅集中在鉴别序列和非启动子的序列。然而，通过考虑弱和强大的启动子分类，可以进一步改善启动子预测因子。在这项工作中，我们介绍了一个名为IPSW（PSEDNC-DL）的混合模型，用于鉴定原核启动子及其实力。它将卷积神经网络与伪二核苷酸组合物（PSEDNC）结合起来。所提出的型号IPSW（PSEDNC-DL）已经在基准数据集上进行了评估，并且在两项任务中表现出当前的最先进模型即，即启动子识别和启动子强度鉴定。开发的工具IPSW（PSEDNC-DL）已在Web服务器中构建，并在https://home.jbnu.ac.kr/nscl/psednc-dl.htm上自由使用

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