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Open Access Highly Accessed Research article

ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

Joshua WK Ho12, Eric Bishop23, Peter V Karchenko125, Nicolas Nègre4, Kevin P White4 and Peter J Park125*

Author Affiliations

1 Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA

2 Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA

3 Program in Bioinformatics, Boston University, Boston, MA, USA

4 Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA

5 Informatics Program, Children's Hospital, Boston, MA, USA

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BMC Genomics 2011, 12:134  doi:10.1186/1471-2164-12-134

Published: 28 February 2011

Abstract

Background

Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of Drosophila melanogaster.

Results

Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis.

Conclusions

Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.