Nucleic Acids ResearchGenomicsPeer-reviewed journal article
Novel Quantitative ChIP-seq Methods Measure Absolute Fold-Change in ER Binding Upon Fulvestrant Treatment
Michael J. GuertiniD, Amy E CulleniD, Florian M. MarkowetziD, Andrew N. HoldingiD
Abstract
A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes in occupancy. Analysis-based normalisation methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor binding, these assumptions do not hold true. The challenges in normalisation are confounded by experimental variability during sample preparation, processing, and recovery.\n\nWe present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalisation. We compare our approach to normalisation by total read depth and two alternative methods that utilise external experimental controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in Patient-Derived Xenographs. This is supported by an adaptable pipeline to normalise and quantify differential transcription factor binding genome-wide and generate metrics for differential binding at individual sites.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC=\"FIGDIR/small/182261_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@177285borg.highwire.dtl.DTLVardef@1fa7d79org.highwire.dtl.DTLVardef@43d17forg.highwire.dtl.DTLVardef@6eff8d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG
- organism: human
- assay: ChIP-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- Citations
- 35
- FWCI
- 1.16
- Field percentile
- top 23%
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