title: 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 σ⁷⁰ 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 σ⁷⁰ PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as 2 to 21 bp resolution. Applications of dPeak to σ⁷⁰ 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.
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
authors.forename: Dongjun
authors.initials: D
authors.lastname: Chung
authors.forename: Dan
authors.initials: D
authors.lastname: Park
authors.forename: Kevin
authors.initials: K
authors.lastname: Myers
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Grass
authors.forename: Patricia
authors.initials: P
authors.lastname: Kiley
authors.forename: Robert
authors.initials: R
authors.lastname: Landick
authors.forename: Sündüz
authors.initials: S
authors.lastname: Keleş
mesh_terms: D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D004269:DNA, Bacterial / Q000032:analysis / Q000737:chemistry / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000737:chemistry / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D004269:DNA, Bacterial; D029968:Escherichia coli Proteins; D014157:Transcription Factors