Journal
PLOS ONE
Volume 6, Issue 2, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0016432
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Funding
- National Institutes of Health (NIH) [R01-HG005692]
- NIH, National Institute of Environmental Health Sciences [ES101765-05]
- Network of Applied Genetic Medicine (RMGA)
- Genome Canada
- Genome British Columbia
- Heart and Stroke Foundation of BC/Yukon
- British Columbia Knowledge Development Fund
- Juvenile Diabetes Research Foundation
- Vancouver Foundation
- BC Cancer Foundation
- Michael Smith Foundation for Health Research
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ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data.
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