Genomics Inform.
2017 Mar;15(1):11-18. 10.5808/GI.2017.15.1.11.
A ChIP-Seq Data Analysis Pipeline Based on Bioconductor Packages
- Affiliations
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- 1Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.
- 2Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea.
- KMID: 2375580
- DOI: http://doi.org/10.5808/GI.2017.15.1.11
Abstract
- Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. "˜dada2' performs trimming of the high-throughput sequencing data. "˜QuasR' and "˜mosaics' perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, "˜ChIPseeker' performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.
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