A ChIP-Seq Data Analysis Pipeline Based on Bioconductor Packages.

Author: Seung Jin PARK ¹ ; Jong Hwan KIM ; Byung Ha YOON ; Seon Young KIM
Author Information

1. Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.
Publication Type:Original Article
Keywords: chromatin immunoprecipitation; data analysis; next-generation sequencing; statistical
MeSH: Chromatin; Chromatin Immunoprecipitation; Genome; Quality Control; Statistics as Topic*
From:Genomics & Informatics 2017;15(1):11-18
CountryRepublic of Korea
Language:English
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.