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

• DOI: 10.1007/s40484-020-0225-2
• Publication Date: 2020-11-20T14:06:13Z
• Authors: Chongzhi Zang; Yiren Wang; Weiqun Peng
• Keywords: 0303 health sciences; 03 medical and health sciences

BackgroundHistone 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.MethodsHere we present RECOGNICER (Recursive coarse‐ grai ning 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.ResultsWe 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.ConclusionRECOGNICER can be a useful bioinformatics tool for next‐generation sequencing data analysis in epigenomics research.