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

• 作者: Chongzhi Zang Yiren Wang Weiqun Peng
• 来源期刊: 定量生物学（英文版）
• 年，卷（期）: 2020,（4）
• 所属期刊栏目: METHODS
• 页码范围: 359-368
• 页数: 10页
• 语种: 英文

摘要

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.