IDEAS an integrative and discriminative epigenome annotation system

Yu Zhang, Lin An, Feng Yue, Ross C. Hardison. Jointly characterizing epigenetic dynamics across multiple human cell types. Nucleic Acids Res. (2016) 44(14):6721-31. doi: 10.1093/nar/gkw278  


The software package contains the free IDEAS method for jointly and quantitatively characterizing multivariate epigenetic landscapes in many cell types, tissues or conditions. The method accounts for position dependent epigenetic events and detects local cell type relationships, which not only help to improve the accuracy of annotating functional classes of DNA sequences, but also reveal cell type constitutive and specific loci. The method utilizes Bayesian non-parametric techniques to automatically identify the best model size fitting to the data, therefore the users do not have to specify the number of states. On the other hand, the users can still specify the number of states if needed.



The IDEAS software package can be downloaded from GitHub at

The program can currently handle either a user-provided data matrix, or work on BAM or BigWig files. Currently the package is only compiled for Linux.



A 36-state model has been generated by IDEAS for 6 ENCODE cell types (GM12878, H1-hESC, HeLa-S3, HepG2, HUVEC, K562) using 13 chromatin marks (H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K27ac, H3K27me3, H3K36me3, H4K20me1, POL2RA, CTCF, Duke DNase, UW DNase, FAIRE) plus one control. The data set was originally analyzed in Hoffman et al. Nucleic Acids Res. (2013). The annotation (IDEAS36) can be accessed under the "Regulation" group at genome browser

A 20-state model for the 127 epigenomes in RoadMap Epigenomics project has also been produced by IDEAS using 5 histone marks (H3keme1, H3k4me3, H3k36me3, H3k9me3, H3k27me3), and is viewable by adding the following URL to the hub of UCSC genome browser.

The corresponding paper for RoadmapEpigenomics annotation by IDEAS is published here:

Yu Zhang, Ross C. Hardison. Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation. Nucleic Acids Res. (2017) doi: 10.1093/nar/gkw659  



For support of using IDEAS, please write to the corresponding author at yzz2 at psu dot edu.