dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data

Abstract

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) has been successfully used for genome-wide profiling of transcription factor binding sites, histone modifications, and nucleosome occupancy in many model organisms and humans. Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs, applications of ChIP-Seq in this domain have not reached their full potential. Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events. We generated single-end tag (SET) and paired-end tag (PET) ChIP-Seq data for factor in Escherichia coli (E. coli). Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events, standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data. To address this problem, we developed dPeak as a high resolution binding site identification (deconvolution) algorithm. dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays. For SET data, dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS, GPS, and GEM. When coupled with PET data, dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods. Experimental validations of a subset of dPeak predictions from PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as to resolution. Applications of dPeak to ChIP-Seq data in E. coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data.

Identifying and mitigating bias in next-generation sequencing methods for chromatin biology

“…Paired-end sequencing in ChIP-seq produces data in which both ends of every fragment are known and no inference of fragment size is necessary. dPeak 101 resolves complex paired-end ChIP-seq peak regions into multiple loci with a higher accuracy than single end analyses. The model used in dPeak takes non-specific binding into account and allows shift distributions to be non-uniform across all binding sites.…”

Defining bacterial regulons using ChIP-seq

“…Some bacterial TFs bind to multiple, closely spaced locations within a promoter region (less than 100 bp separation) [46]. The ability to distinguish between these sites yields greater mechanistic insight into the transcriptional control mediated by the TF of interest.…”

“…However, most standard peak finding algorithms are unable to resolve closely spaced binding sites, instead misidentifying these regions as a single binding site with the wrong genomic coordinates. To test for closely spaced binding sites, peak deconvolution algorithms such as CSDeconv [20,47], GEM [48], PICS [49] or dPeak [46] can be used. We found that dPeak was able to identify regions containing multiple TF binding sites, most of which were missed with standard peak finding algorithms [46].…”

“…To test for closely spaced binding sites, peak deconvolution algorithms such as CSDeconv [20,47], GEM [48], PICS [49] or dPeak [46] can be used. We found that dPeak was able to identify regions containing multiple TF binding sites, most of which were missed with standard peak finding algorithms [46]. Furthermore, dPeak was designed to leverage the greater resolution provided by paired-end sequencing data to provide high-resolution ChIP-seq binding site locations in bacteria [46].…”

Decoding ChIP-seq with a double-binding signal refines binding peaks to single-nucleotides and predicts cooperative interaction

“…However, the application of csdeconv in data sets with a large number of enriched regions has not been practical due to its high computational cost (Wilbanks and Facciotti 2010). Other methods have also been proposed to identify multiple binding sites inside the same region (Guo et al 2010(Guo et al , 2012Zhang et al 2011;Chung et al 2013).…”