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iPTT(2L)-CNN:一种基于卷积神经网络的两层预测器,用于识别植物基因组中的启动子及其类型。

iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.

机构信息

Jing-De-Zhen Ceramic Institute, Jingdezhen, China.

出版信息

Comput Math Methods Med. 2021 Jan 5;2021:6636350. doi: 10.1155/2021/6636350. eCollection 2021.

DOI:10.1155/2021/6636350
PMID:33488763
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7803414/
Abstract

A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulation. Because of their importance in the process of biological transcriptional regulation, there is an urgent need to develop in silico tools to identify promoters and their types timely and accurately. A number of prediction methods had been developed in this regard; however, almost all of them were merely used for identifying promoters and their strength or sigma types. Owing to that TATA box region in TATA promoter that influences posttranscriptional processes, in the current study, we developed a two-layer predictor called iPTT(2L)-CNN by using the convolutional neural network (CNN) for identifying TATA and TATA-less promoters. The first layer can be used to identify a given DNA sequence as a promoter or nonpromoter. The second layer is used to identify whether the recognized promoter is TATA promoter or not. The 5-fold crossvalidation and independent testing results demonstrate that the constructed predictor is promising for identifying promoter and classifying TATA and TATA-less promoter. Furthermore, to make it easier for most experimental scientists get the results they need, a user-friendly web server has been established at http://www.jci-bioinfo.cn/iPPT(2L)-CNN.

摘要

启动子是位于起始密码子附近的短 DNA 序列,负责启动基因组中特定基因的转录。准确识别启动子对于更好地理解转录调控具有重要意义。由于它们在生物转录调控过程中的重要性,迫切需要开发能够及时、准确识别启动子及其类型的计算工具。在这方面已经开发了许多预测方法;然而,几乎所有这些方法都仅仅用于识别启动子及其强度或 sigma 类型。由于 TATA 启动子中的 TATA 区影响转录后过程,因此在本研究中,我们使用卷积神经网络(CNN)开发了一种称为 iPTT(2L)-CNN 的两层预测器,用于识别 TATA 和无 TATA 启动子。第一层可用于识别给定的 DNA 序列是启动子还是非启动子。第二层用于识别识别出的启动子是否为 TATA 启动子。五重交叉验证和独立测试结果表明,所构建的预测器在识别启动子和分类 TATA 和无 TATA 启动子方面具有很大的潜力。此外,为了使大多数实验科学家更容易获得他们需要的结果,我们在 http://www.jci-bioinfo.cn/iPPT(2L)-CNN 上建立了一个用户友好的网络服务器。

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