TFBSnet: A deep learning-based tool for predicting transcription factor binding site from DNA sequences

Transcription factors (TFs) are crucial proteins that regulate gene transcription by binding to specific sites on DNA, known as transcription factor binding sites (TFBSs). Identifying TFBSs enables the design of drugs to modulate gene expression, making it important for drug design and gene therapy. While deep learning-based methods have been proposed for predicting TFBSs, there is room for improvement. This study introduces TFBSnet, a novel deep learning-based technique that accurately predicts TFBSs by extracting diverse feature data from DNA sequences and utilizing a convolutional neural network (CNN) combined with SKNet. Experimental results show that TFBSnet outperforms existing methods. It also demonstrates accurate prediction of TF binding sites in human cells without label data and exceptional performance in predicting TFBSs in plant cells using 265 TFs in Arabidopsis. Ablation analysis highlights the integration of different features and advanced feature extraction by SKNet as contributors to TFBSnet's superior predictive capability.