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Analysis of ChIP-seq Data in R/Bioconductor.

Ines de Santiago1, Thomas Carroll2

  • 1Li Ka Shing Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. inesdesantiago@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|October 14, 2017
PubMed
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This guide details R/Bioconductor tools for analyzing ChIP-sequencing (chromatin immunoprecipitation sequencing) data. It covers essential steps like data alignment, peak calling, and quality control for gene regulation studies.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput sequencing methods like ChIP-sequencing (chromatin immunoprecipitation sequencing) offer powerful tools for studying gene regulation.
  • Effective quality control and data analysis are crucial for extracting meaningful insights from ChIP-seq data.
  • The R/Bioconductor project provides a suite of open-source tools for processing and analyzing ChIP-seq data.

Purpose of the Study:

  • To provide an overview of available methods for analyzing ChIP-seq data.
  • To demonstrate computational procedures for ChIP-seq data analysis using R/Bioconductor.
  • To enable researchers to construct their own analysis pipelines for ChIP-seq data.

Main Methods:

  • Data alignment and quality control.
Keywords:
BioconductorChIP-seqComputational analysisData analysisSequencingWorkflow

Related Experiment Videos

  • Peak calling and data visualization.
  • Identification of differentially bound regions and functional annotation of regulatory regions.

    • Main Results:

    • Demonstration of basic and advanced ChIP-seq analysis steps using publicly available datasets.
    • Illustrations of routine computational procedures in R/Bioconductor for ChIP-seq data.
    • Guidance on constructing custom ChIP-seq analysis pipelines.

    Conclusions:

    • R/Bioconductor offers a comprehensive environment for ChIP-seq data analysis.
    • The described methods facilitate robust gene regulation studies.
    • Researchers can leverage these tools to build reproducible and scalable ChIP-seq analysis workflows.