Predicting in-vitro Transcription Factor Binding Sites Using DNA Sequence + Shape.

Abstract

Discovery of transcription factor binding sites (TFBSs) is essential for understanding the underlying binding mechanisms and cellular functions. Recently, Convolutional neural network (CNN) has succeeded in predicting TFBSs from the primary DNA sequences. In addition to DNA sequences, several evidences suggest that protein-DNA binding is partly mediated by properties of DNA shape. Although many methods have been proposed to jointly account for DNA sequences and shape properties in predicting TFBSs, they ignore the power of the combination of deep learning and DNA sequence + shape. Therefore we develop a deep-learning-based sequence + shape framework (DLBSS) in this paper, which appropriately integrates DNA sequences and shape properties, to better understand protein-DNA binding preference. This method uses a shared CNN to find their common patterns from DNA sequences and their corresponding shape features, which are then concatenated to compute a predicted value. Using 66 in-vitro datasets derived from universal protein binding microarrays (uPBMs), we show that our proposed method DLBSS significantly improves the performance of predicting TFBSs. In addition, we explain the reason why we should use the shared CNN, and explore the performance of DLBSS when using a deeper CNN, through a series of experiments.