As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identify TF binding sites and predict their affinities. Recently, deep learning (DL) algorithms have achieved promising results in the prediction of DNA-TF binding, however, various deep learning architectures have not been systematically compared, and the relative merit of each architecture remains unclear. To address this problem, we applied four different deep learning architectures to SELEX-seq and HT-SELEX data, covering three species and 35 families. We evaluated and compared the performance of different deep neural models using 10-fold cross-validation. Our results indicate that the hybrid CNN + DNN model shows the best performances. We expect that our study will be broadly applicable to modeling and predicting TF binding specificity when more high-throughput affinity data are available.

作为基因调控的关键组成部分，转录因子（TFs）在许多生物过程中发挥着重要作用。因此，为了充分了解 TF 介导的基因调控的潜在机制，准确识别 TF 结合位点并预测它们的亲和力至关重要。最近，深度学习（DL）算法在预测 DNA-TF 结合方面取得了可喜的成果，然而，各种深度学习架构尚未系统比较，每种架构的相对优点仍不清楚。为了解决这个问题，我们将四种不同的深度学习架构应用于 SELEX-seq 和 HT-SELEX 数据，涵盖三个物种和 35 个科。我们使用 10 倍交叉验证评估和比较了不同深度神经模型的性能。我们的结果表明混合 CNN + DNN 模型显示出最佳性能。我们预计，当有更多的高通量亲和力数据可用时，我们的研究将广泛适用于建模和预测 TF 结合特异性。