iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters
iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters
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DOI：
10.1093/bioinformatics/btaa609
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发表时间：
2020-10-01
期刊：
影响因子：
5.8
通讯作者：
Shatabda, Swakkhar
中科院分区：
文献类型：
--
作者：
Amin, Ruhul;Rahman, Chowdhury Rafeed;Shatabda, Swakkhar
目的：启动子是DNA中负责启动特定基因转录的一个短区域。开发用于自动识别启动子的计算工具是高需求的。根据功能的不同，启动子可以是不同的类型。启动子在共有序列方面可以具有类内和类间变异和相似性。结果：我们提出了iPromoter-BnCNN方法，对sigma（24），sigma（28），sigma（32），sigma（38），sigma（54），sigma（70）六种启动子进行了识别和准确分类。它是一种基于CNN的分类器，通过使用平行分支将与单体核苷酸序列、三聚体核苷酸序列、二聚体结构性质和三聚体结构性质相关的局部特征组合在一起。我们在基准数据集上进行了实验，并与六种最先进的工具进行了比较，以显示我们在5倍交叉验证方面的优势。此外，我们在独立的测试数据集上测试了我们的分类器。
Motivation: Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge.Results: We present iPromoter-BnCNN for identification and accurate classification of six types of promoters-sigma(24), sigma(28), sigma(32), sigma(38), sigma(54), sigma(70). It is a CNN-based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset.