ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

Department of Genetics, Stanford University, Stanford, California 94305, USA
Genome Research (Impact Factor: 14.63). 09/2012; 22(9):1813-31. DOI: 10.1101/gr.136184.111
Source: PubMed


Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE ( and modENCODE ( portals.

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    • "ChIP assay was performed in duplicate using 50 μg of chromatin as previously described and according to ENCODE's guideline (Kelly et al. 2010; Landt et al. 2012). The following antibodies were used: H2A.Z (Abcam, ab4174); H3K4me3 (Active Motif, 39160); H3K4me1 (Active Motif, 39298); H3K27ac (Active Motif, 39297); and H3K27me3 (Active Motif, 39155). "
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    Genome Research 03/2015; 25(4). DOI:10.1101/gr.183368.114 · 14.63 Impact Factor
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    • "Interactions amongst TFs and TFBSs regulate the transcription of genes differentially on various developmental stages and tissue types [54]. Chromatin Immunoprecipitation followed by high-throughput DNA Sequencing (ChIP-Seq) has been widely used to detect TF-DNA interactions [55]. It can locate DNA regions bound by a certain TF. "
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    02/2015; 36(1). DOI:10.1016/j.bdr.2015.02.005
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    • "Moreover , additional variables, such as variations in genome fragmentation , immunoprecipitation efficiency, or other experimental steps, frequently confound analysis. Efforts to correct for these variables have produced in silico normalization strategies , but an empirical method to enable direct and quantitative comparisons among epigenomic ChIP-seq data sets is still lacking (Bardet et al., 2012; Landt et al., 2012; Liang and Keles x, 2012; Liu et al., 2013; Nair et al., 2012). Because of the experimental and analytical restrictions of ChIP-seq, a robust normalization methodology is needed to quantify epigenome differences among varying cell populations, treatments, and genomic states. "
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