The proper use of spike-in normalization in ChIP-seq improves sensitivity for detecting genome-wide changes between conditions, but improper use is common, calling some biological conclusions into question. A survey of public datasets generates guidelines for implementation of spike-in normalization for future ChIP-seq experiments.
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Data availability
Data generated in this study (Fig. 1b, Fig. 4, and Supplementary Figs. 2–6 and 8–14) are at GSE273915. Public data used to generate Fig. 1a are from GSE60104. UCSC Genome Browser sessions are available for Fig. 1b, Fig. 4 human (target) data and Fig. 4 yeast (spike-in) data.
Code availability
Code is available on a Github repository.
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Acknowledgements
Research reported in this publication was supported in part by US National Institutes of Health/National Institute of Mental Health grant R01MH127077 (A.G. and C.B.) and National Science Foundation grant 2003358 (A.G.). We thank I. Simon for providing conceptual guidance and feedback on the manuscript, R. Wachs for helping with illustrations and the Pillus lab at UCSD for providing S. cerevisiae cells and guidance on yeast culture and ChIP-seq.
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L.A.P., Y.C., C.B. and A.G. are inventors on related patent applications.
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Patel, L.A., Cao, Y., Mendenhall, E.M. et al. The Wild West of spike-in normalization. Nat Biotechnol 42, 1343–1349 (2024). https://doi.org/10.1038/s41587-024-02377-y
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DOI: https://doi.org/10.1038/s41587-024-02377-y
