Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing ‘Black-box’ approaches that are unable to reveal causal relationships from large amounts of initially encoded features.

中文翻译：

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利用堆叠集成学习框架对大肠杆菌中一般和特定类型的启动子进行计算预测和解释

启动子是 DNA 的短共有序列，负责转录激活或抑制所有基因。细菌中有多种类型的启动子，在启动基因转录中起重要作用。因此，解决启动子识别问题对于提高对其功能的理解具有重要意义。为此，已经建立了针对启动子分类的计算方法；然而，他们的表现仍然不尽如人意。在这项研究中，我们提出了一种新的堆叠集成方法（称为 SELECTOR）来识别启动子及其各自的分类。SELECTOR 组合了k的组成-间隔核酸对、平行相关伪二核苷酸组成、基于单链的位置特异性三核苷酸倾向和 DNA 链特征，并使用五种流行的基于树的集成学习算法来构建堆叠模型。使用基准数据集的 5 倍交叉验证测试和使用新收集的独立测试数据集的独立测试都表明，SELECTOR 在大肠杆菌中的一般和特定类型的启动子预测方面均优于最先进的方法. 此外，这个新颖的框架提供了必要的解释，通过利用强大的 Shapley Additive exPlanation 算法帮助理解模型成功，从而突出了与预测一般和特定类型的启动子相关的最重要的特征，并克服了现有“黑盒”的局限性无法从大量初始编码特征中揭示因果关系的方法。