Abstract
To model spatial changes of chromatin mark peaks over time we develop and apply ChromTime, a computational method that predicts peaks to be either expanding, contracting, or holding steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provide information about gene expression changes beyond localized signal density changes. ChromTime detects asymmetric expansions and contractions, which for some marks associate with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.
Highlights
Genome-wide mapping of histone modifications (HMs) and related chromatin marks using chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and DNA accessibility through assays for DNase I hypersensitivity (DNase-seq) or transposase-accessible chromatin (ATAC-seq) assays have emerged as a powerful approach to annotate genomes and study cell states [1,2,3,4,5]
Nearby bins that show significant enrichment are joined into continuous intervals, which subsequently are grouped into blocks if they overlap across time points
We showed that ChromTime predictions associate with relevant genomic features such as changes in gene expression and transcription factor (TF) binding
Summary
Genome-wide mapping of histone modifications (HMs) and related chromatin marks using chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and DNA accessibility through assays for DNase I hypersensitivity (DNase-seq) or transposase-accessible chromatin (ATAC-seq) assays have emerged as a powerful approach to annotate genomes and study cell states [1,2,3,4,5]. While many mapping efforts have largely focused on single or unrelated cell and tissue types [3, 6], a growing number of biological processes have been studied with temporal epigenomic data using assays such as ChIP-seq, ATAC-seq, or DNase-seq over a time course, which map chromatin marks at consecutive stages during the particular biological process Such datasets have been generated for a wide range of biological settings, In the context of time course chromatin data, only a few methods have been proposed that consider temporal dependencies between samples. GATE [30], produces a Fiziev and Ernst Genome Biology (2018) 19:109 genome annotation based on clustering fixed-length genomic loci that can be modeled with the same switch from one chromatin state to another over time
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