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

BMC Genomics

Research Article

Eric Bishop¹  Joshua WK Ho²  Peter J Park³  Peter V Karchenko³  Nicolas Nègre⁴  Kevin P White⁴ 

[1] Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA;Program in Bioinformatics, Boston University, Boston, MA, USA;Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA;Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA;Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA;Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA;Informatics Program, Children's Hospital, Boston, MA, USA;Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA;

关键词: Sequencing Depth;    Enrichment Region;    Peak Calling;    Peak Caller;    ChIP Profile;   

DOI  :  10.1186/1471-2164-12-134

 received in 2011-01-04, accepted in 2011-02-28,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundChromatin 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.ResultsBoth 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Ho et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.