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Reusable, extensible, and modifiable R scripts and Kepler workflows for comprehensive single set ChIP-seq analysis.

Nathan Cormier1, Tyler Kolisnik1, Mark Bieda2

  • 1Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada.

BMC Bioinformatics
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Summary

We developed modular software pipelines for chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. These pipelines offer comprehensive solutions for complex datasets, including data provenance tracking and various graphical outputs.

Keywords:
BioconductorChIP-seq analysisScientific workflowsSoftware packages

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology Techniques

Background:

  • Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used, but its large-scale data analysis is complex.
  • Existing ChIP-seq analysis systems often require custom programming and lack comprehensive outputs or modularity.
  • There is a need for adaptable, modular pipelines that handle common tasks like pathway analysis and generate standard graphical outputs, with data provenance tracking.

Purpose of the Study:

  • To develop a comprehensive and modular set of software tools for ChIP-seq data analysis.
  • To provide both turnkey pipelines and individual modules for flexibility and ease of use.
  • To ensure the pipelines support data provenance tracking and generate standard complex graphical outputs.

Main Methods:

  • Developed 20 software modules for ChIP-seq analysis using the Kepler workflow system.
  • Implemented 18 of these modules as standalone R scripts for broader accessibility.
  • Integrated common R packages and established tools like MACS for peak finding.

Main Results:

  • Created four full turnkey pipelines and 16 component modules for ChIP-seq analysis.
  • Pipelines cover tasks from raw read mapping and peak finding to gene ontology and pathway analysis.
  • Kepler workflows enable data provenance tracking, and outputs include summary statistics and transcription start site (TSS)-centered plots.

Conclusions:

  • The developed pipelines offer comprehensive solutions for ChIP-seq data analysis, ranging from single tasks to full analyses.
  • Pipelines are available as Kepler workflows (with data provenance) and standalone R scripts, enhancing usability.
  • The modular design facilitates easy modification and repurposing for evolving research needs.