RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data

Abstract

Background: Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors. Some histone modifications, such as H3K27me3 and H3K9me3, usually mark broad domains in the genome ranging from kilobases (kb) to megabases (Mb) long, resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation. While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features, a principled method to identify scale-free board domains has been lacking.

Methods: Here we present RECOGNICER (Recursive coarse-graining identification for ChIP-seq enriched regions), a computational method for identifying ChIP-seq enriched domains on a large range of scales. The algorithm is based on a coarse-graining approach, which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales.

Results: We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data, and validate the results based on H3K27me3’s association with repressive gene expression. We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces.

Conclusion: RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.

Graphical abstract

Keywords

coarse-graining / ChIP-seq / peak calling / histone modification

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AVAILABILITY

RECOGNICER is implemented in Python and the source code is openly available at https://github.com/zanglab/recognicer.

ACKNOWLEDGEMENTS

The authors would like to thank Drs. Keji Zhao and Dustin E. Schones for helpful discussions and members of the Zang laboratory for testing the software. This work was partially supported by the U.S. National Institutes of Health (NIH) R35GM133712 to C.Z., and R01 AI121080 and R01AI139874 to W.P.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Chongzhi Zang, Yiren Wang and Weiqun Peng declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature