Negre, NicolasLi, QunhuaMieczkowska, Joanna O.Slattery, MatthewKim, Tae-KyungZieba, JenniferRuan, YijunBickel, Peter J.Wold, Barbara J.Lieb, Jason D.Chen, YiwenLiu, TaoZhang, YongHe, Housheng HMyers, Richard M.White, Kevin P.Liu, Xiaole2013-05-072012Chen, Yiwen, Nicolas Negre, Qunhua Li, Joanna O. Mieczkowska, Matthew Slattery, Tao Liu, Yong Zhang, et al. 2012. Systematic evaluation of factors influencing ChIP-seq fidelity. Nature Methods 9(6): 609-614.1548-70911548-7105http://nrs.harvard.edu/urn-3:HUL.InstRepos:10611810We performed a systematic evaluation of how variations in sequencing depth and other parameters influence interpretation of Chromatin immunoprecipitation (ChIP) followed by sequencing (ChIP-seq) experiments. Using Drosophila S2 cells, we generated ChIP-seq datasets for a site-specific transcription factor (Suppressor of Hairy-wing) and a histone modification (H3K36me3). We detected a chromatin state bias, open chromatin regions yielded higher coverage, which led to false positives if not corrected and had a greater effect on detection specificity than any base-composition bias. Paired-end sequencing revealed that single-end data underestimated ChIP library complexity at high coverage. The removal of reads originating at the same base reduced false-positives while having little effect on detection sensitivity. Even at a depth of ~1 read/bp coverage of mappable genome, ~1% of the narrow peaks detected on a tiling array were missed by ChIP-seq. Evaluation of widely-used ChIP-seq analysis tools suggests that adjustments or algorithm improvements are required to handle datasets with deep coverage.en-USgenomicssequencingbioinformaticsepigeneticsJournal Article2013-05-0710.1038/nmeth.1985