title: HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data.
abstract: BACKGROUND
  Chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research.
  
  
  RESULTS
  We have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. HiChIP incorporates several open source software packages selected based on internal assessments and published comparisons. It also includes a set of tools developed in-house. This workflow enables the analysis of both paired-end and single-end ChIP-Seq reads, with or without replicates for the characterization and annotation of both punctate and diffuse binding sites. The main functionality of HiChIP includes: (a) read quality checking; (b) read mapping and filtering; (c) peak calling and peak consistency analysis; and (d) result visualization. In addition, this pipeline contains modules for generating binding profiles over selected genomic features, de novo motif finding from transcription factor (TF) binding sites and functional annotation of peak associated genes.
  
  
  CONCLUSIONS
  HiChIP is a comprehensive analysis pipeline that can be configured to analyze ChIP-Seq data derived from varying antibodies and experiment designs. Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data.
authors.forename: Huihuang
authors.initials: H
authors.lastname: Yan
authors.forename: Jared
authors.initials: J
authors.lastname: Evans
authors.forename: Mike
authors.initials: M
authors.lastname: Kalmbach
authors.forename: Raymond
authors.initials: R
authors.lastname: Moore
authors.forename: Sumit
authors.initials: S
authors.lastname: Middha
authors.forename: Stanislav
authors.initials: S
authors.lastname: Luban
authors.forename: Liguo
authors.initials: L
authors.lastname: Wang
authors.forename: Aditya
authors.initials: A
authors.lastname: Bhagwate
authors.forename: Ying
authors.initials: Y
authors.lastname: Li
authors.forename: Zhifu
authors.initials: Z
authors.lastname: Sun
authors.forename: Xianfeng
authors.initials: X
authors.lastname: Chen
authors.affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA. Kocher.JeanPierre@mayo.edu.
authors.forename: Jean-Pierre A
authors.initials: JP
authors.lastname: Kocher
mesh_terms: D000818:Animals; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D002874:Chromosome Mapping; D003627:Data Interpretation, Statistical; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D051379:Mice; D058977:Molecular Sequence Annotation; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors