Computational prediction and characterization of cell-type-specific and shared binding sites

Abstract

Cell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TFs intrinsic sequence preferences, cooperative interactions with cofactors, cell-type-specific chromatin landscapes, and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied. In this paper, we propose two computational approaches for predicting and characterizing cell-type-specific and shared binding sites by integrating multiple types of features, in which one is based on XGBoost and another is based on convolutional neural network (CNN). To validate the performance of our proposed approaches, ChIP-seq datasets of 10 binding factors were collected from the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines, each of which was further categorized into cell-type-specific (GM12878-specific and K562-specific) and shared binding sites. Then, multiple types of features for these binding sites were integrated to train the XGBoost-based and CNN-based models. Experimental results show that our proposed approaches significantly outperform other competing methods on three classification tasks. To explore the contribution of different features, we performed ablation experiments and feature importance analysis. Consistent with previous studies, we find that chromatin features are major contributors in which chromatin accessibility is the best predictor. Moreover, we identified independent feature contribution for cell-type-specific and shared sites through SHAP values, observing that chromatin features play a main role in the cell-type-specific sites while motif features play a main role in the shared sites. Beyond these observations, we explored the ability of the CNN-based model to predict cell-type-specific and shared binding sites by excluding or including DNase signals, showing that chromatin accessibility significantly improves the prediction performance. Besides, we investigated the generalization ability of our proposed approaches to different binding factors in the same cellular environment or to the same binding factors in the different cellular environments.