School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212100, PR China.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, PR China.
Anal Biochem. 2022 Nov 1;656:114878. doi: 10.1016/j.ab.2022.114878. Epub 2022 Aug 29.
Accurate prediction of DNA-protein binding (DPB) is of great biological significance for studying the regulatory mechanism of gene expression. In recent years, with the rapid development of deep learning techniques, advanced deep neural networks have been introduced into the field and shown to significantly improve the prediction performance of DPB. However, these methods are primarily based on the DNA sequences measured by the ChIP-seq technology, failing to consider the possible partial variations of the motif sequences and errors of the sequencing technology itself. To address this, we propose a novel computational method, termed MSDenseNet, which combines a new fault-tolerant coding (FTC) scheme with the dense connectional deep neural networks. Three important factors can be attributed to the success of MSDenseNet: First, MSDenseNet utilizes a powerful feature representation approach, which transforms the raw DNA sequence into fusion coding using the fault-tolerant feature sequence; Second, in terms of network structure, MSDenseNet uses a multi-scale convolution within the dense layer and the multi-scale convolution preceding the dense block. This is shown to be able to significantly improve the network performance and accelerate the network convergence speed, and third, building upon the advanced deep neural network, MSDenseNet is capable of effectively mining the hidden complex relationship between the internal attributes of fusion sequence features to enhance the prediction of DPB. Benchmarking experiments on 690 ChIP-seq datasets show that MSDenseNet achieves an average AUC of 0.933 and outperforms the state-of-the-art method. The source code of MSDenseNet is available at https://github.com/csbio-njust-edu/msdensenet. The results show that MSDenseNet can effectively predict DPB. We anticipate that MSDenseNet will be exploited as a powerful tool to facilitate a more exhaustive understanding of DNA-binding proteins and help toward their functional characterization.
准确预测 DNA-蛋白质结合（DPB）对于研究基因表达的调控机制具有重要的生物学意义。近年来，随着深度学习技术的飞速发展，先进的深度神经网络被引入该领域，并显著提高了 DPB 的预测性能。然而，这些方法主要基于 ChIP-seq 技术测量的 DNA 序列，未能考虑到基序序列的可能部分变异和测序技术本身的误差。针对这一问题，我们提出了一种新的计算方法，称为 MSDenseNet，它将一种新的容错编码（FTC）方案与密集连接的深度神经网络相结合。MSDenseNet 成功的三个重要因素是：首先，MSDenseNet 利用了强大的特征表示方法，该方法使用容错特征序列将原始 DNA 序列转换为融合编码；其次，在网络结构方面，MSDenseNet 在密集层内和密集块之前使用多尺度卷积。事实证明，这可以显著提高网络性能并加速网络收敛速度，第三，在先进的深度神经网络的基础上，MSDenseNet 能够有效地挖掘融合序列特征内部属性之间隐藏的复杂关系，从而增强 DPB 的预测能力。在 690 个 ChIP-seq 数据集上的基准实验表明，MSDenseNet 的平均 AUC 为 0.933，优于最先进的方法。MSDenseNet 的源代码可在 https://github.com/csbio-njust-edu/msdensenet 上获得。结果表明，MSDenseNet 可以有效地预测 DPB。我们预计 MSDenseNet 将被用作一种强大的工具，以促进对 DNA 结合蛋白的更详尽的理解，并有助于对其功能进行表征。
