prehensive recent advances in our understanding. In this study, we focus on predicting the gene expression in ESCs with the massive parallel sequencing data. Although a previous study successfully applied a regression model to the prediction \[[@B21]\], the model is based on a generalized weighting scheme to prepare predictors (explanatory variables). Intuitively, such weighting scheme cannot reflect the nature of the spatial rearrangement of TF-binding.

Here, we propose a density-based approach that uses the genome-wide bona fide TF binding sites. First, a publicly available ChIP-seq data \[[@B9]\] is reanalyzed. Then, density profiles of TFs estimated from the ChIP-seq data are adopted as predictors in a simple linear regression model to predict the genome-wide gene expression. Predictors are also combined with epigenetic data, such as H3K4me3, H3K27me3, DNA methylation, and CpG island \[[@B16],[@B17]\]. Furthermore, we analyze the regulatory effects of TFs, epigenetic states, and their higher-order interactions by using a pipeline developed in house. We demonstrate the predictive power of the density-based regression model and discuss our findings.

Results
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ChIP-seq data is reproduced and extended
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To minimize artifacts, we refined the binding signals of 12 core TFs in mouse ESC publicly available \[[@B9]\] (see *methods*). The ChIP-seq peak datasets generated by various tools are hereafter denoted as FP4_Bowtie, FP4_MAQ, and FP4_Soap2. Also, tag positions mapped by Eland \[[@B9]\] are used for the peak detection (FP4_Eland), and the peak data of Chen et al. is involved (Chen Eland). Thus, we prepared five peak datasets in total.

Differences in numbers and positions between the remapped data and the original data were investigated. As a result, relatively larger number of uniquely mapped tags and peaks were gained compared to the original data (Table S1-S3 in Additional file [1](#S1){ref-type="supplementary-material"}). In regard to peaks (Table [1](#T1){ref-