Design and analysis of ChIP-seq experiments for DNA-binding proteins

Abstract

Critical considerations in the design and analysis of ChIP-seq experiments include how to align sequenced tags to the genome, how to detect binding sites and how to estimate the number of tags needed to confidently determine where a protein binds DNA. Using data set for three transcription factors, Kharchenko et al. address these considerations by comparing three novel algorithms with published computational methods. Recent progress in massively parallel sequencing platforms has enabled genome-wide characterization of DNA-associated proteins using the combination of chromatin immunoprecipitation and sequencing (ChIP-seq). Although a variety of methods exist for analysis of the established alternative ChIP microarray (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein-binding positions with high accuracy. Using previously reported data sets for three transcription factors, we illustrate methods for improving tag alignment and correcting for background signals. We compare the sensitivity and spatial precision of three peak detection algorithms with published methods, demonstrating gains in spatial precision when an asymmetric distribution of tags on positive and negative strands is considered. We also analyze the relationship between the depth of sequencing and characteristics of the detected binding positions, and provide a method for estimating the sequencing depth necessary for a desired coverage of protein binding sites.