Abstract
BackgroundHigh-throughput sequencing technology has become popular and widely used to study protein and DNA interactions. Chromatin immunoprecipitation, followed by sequencing of the resulting samples, produces large amounts of data that can be used to map genomic features such as transcription factor binding sites and histone modifications.MethodsOur proposed statistical algorithm, BayesPeak, uses a fully Bayesian hidden Markov model to detect enriched locations in the genome. The structure accommodates the natural features of the Solexa/Illumina sequencing data and allows for overdispersion in the abundance of reads in different regions. Moreover, a control sample can be incorporated in the analysis to account for experimental and sequence biases. Markov chain Monte Carlo algorithms are applied to estimate the posterior distributions of the model parameters, and posterior probabilities are used to detect the sites of interest.ConclusionWe have presented a flexible approach for identifying peaks from ChIP-seq reads, suitable for use on both transcription factor binding and histone modification data. Our method estimates probabilities of enrichment that can be used in downstream analysis. The method is assessed using experimentally verified data and is shown to provide high-confidence calls with low false positive rates.
Highlights
High-throughput sequencing technology has become popular and widely used to study protein and DNA interactions
In this paper we focus on a method of analyzing chromatin immunoprecipitation (ChIP)-seq data to identify protein-binding locations and the presence of specific histone modifications in the genome
Algorithm During chromatin immunoprecipitation, the proteins are cross-linked with the DNA, the cells are lysed, and the DNA is randomly sheared
Summary
High-throughput sequencing technology has become popular and widely used to study protein and DNA interactions. The most widely used method for mapping these genomic locations is chromatin immunoprecipitation (ChIP) This process involves shearing the DNA and isolating the fragments to which proteins have bound [1], after which various methods can be used to identify those protein-bound fragments. Direct sequencing is a reliable and efficient technique that is gradually replacing microarray hybridization for determining the contents of the immunoprecipitated samples [2]. These two procedures are widely known as ChIP-seq and ChIP-chip respectively, and both present their own statistical challenges. These models are not directly applicable to ChIP-seq data
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