Abstract
Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.
Highlights
Chromatin immunoprecipitation with sequencing (ChIP-seq) is a popular technique for identifying the genomic binding sites of a target protein
differential binding (DB) analyses are easier to perform than their conventional counterparts, as the effect of genomic biases is largely mitigated when counts for different libraries are compared at the same genomic region
Note that that the gax and greg objects are the same as those used in the visualization of the H3k9ac data. Summary This workflow describes the steps of a window-based DB analysis, from read alignment through to visualization of DB regions
Summary
Chromatin immunoprecipitation with sequencing (ChIP-seq) is a popular technique for identifying the genomic binding sites of a target protein. A number of recent studies have focused on the detection of changes in the binding profile between conditions (Pal et al, 2013; Ross-Innes et al, 2012). These differential binding (DB) analyses involve counting reads into genomic intervals, and testing those counts for significant differences between conditions. This defines a set of putative DB regions for further examination. DB regions may be more relevant as the change in binding can be associated with the biological difference between conditions
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