i*. Note that a small value is added to *Y~i~* for the logarithm.

Adding epigenetic effects
-------------------------

Discrete values representing epigenetic states of a gene *i* are added to the regression model

where *H* is the type of histone mark (neither mark=1.0, H3K27me3=2.0, bivalent mark=3.0, H3K4me3=4.0), *M* is the DNA methylation (no annotation=1.0, methylation=2.0, unmethylation=3.0), *C* is the CpG island (absence=1.0, presence=2.0), and *α*, *β*, *γ* are the regression coefficients for *H*, *M*, *C*, respectively.

Fitting and reducing regression models
--------------------------------------

Explanatory variables in a regression model are log-transformed and quantile-normalized. 10 runs of 10-fold cross validation (CV) measure the average correlation coefficient (CV-*R*) and the average proportion of variation explained by the model (CV-*R*^2^). The stepwise model selection is done by stepAIC in R language with the backward and forward procedure. The regression model with higher-order interactions are reduced by a pipeline developed in house; ANOVA in R language first diagnoses the significance of each explanatory variable in the given saturated model. Next, significant variables (*p* \< 0.05 in F-test) are gathered. Finally, the best model is constructed by adding and removing the collected variables one by one in increasing order of *p*-value until CV-*R*^2^ is not improved anymore.

Competing interests
===================

The authors declare that they have no competing interests.

Supplementary Material
======================

###### Additional file 1

**Extended analysis of ChIP-seq data** This file provides tables including the summary of tag mapping (Table S1), the fold change of remapped tags over the original data (Table S2), the number of peaks in five datasets (Table S3), and the thresholds used to detect significant peaks (Table S4).

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Click here for file

###### Additional file 2

**Comprehensive analysis of gene regulation in mouse ESC** This file provides figures inclu