Introduction

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.

Publications

  1. HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data.
    Cite this
    Yan H, Evans J, Kalmbach M, Moore R, Middha S, Luban S, Wang L, Bhagwate A, Li Y, Sun Z, Chen X, Kocher JP, 2014-01-01 - BMC bioinformatics

Credits

  1. Huihuang Yan
    Developer

  2. Jared Evans
    Developer

  3. Mike Kalmbach
    Developer

  4. Raymond Moore
    Developer

  5. Sumit Middha
    Developer

  6. Stanislav Luban
    Developer

  7. Liguo Wang
    Developer

  8. Aditya Bhagwate
    Developer

  9. Ying Li
    Developer

  10. Zhifu Sun
    Developer

  11. Xianfeng Chen
    Developer

  12. Jean-Pierre A Kocher
    Investigator

    Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, United States of America

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Summary
AccessionBT001006
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl, R
User InterfaceTerminal Command Line
Download Count0
Submitted ByJean-Pierre A Kocher