RT Journal Article
SR Electronic
T1 Novel Quantitative ChIP-seq Methods Measure Absolute Fold-Change in ER Binding Upon Fulvestrant Treatment
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 182261
DO 10.1101/182261
A1 Michael J Guertin
A1 Florian Markowetz
A1 Andrew N Holding
YR 2017
UL http://biorxiv.org/content/early/2017/08/30/182261.abstract
AB ChIP-seq (Chromatin ImmunoPrecipitation combined with massively parallel DNA Sequencing) maps protein/DNA interactions genome-wide with high resolution.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 initially developed for transcriptomic data and these methods 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. Misapplication of these methods to ChIP-seq data results in the suppression of the biological signal or erroneous measurement of differential occupancy. The challenges in normalisation are confounded by experimental variability introduced during sample preparation and processing. Current experimental methodologies do not fully control for important variables, such as the efficiency of DNA recovery by immunoprecipitation.We present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. We compare our approach to normalisation by total read depth and two alternative methods that utilise external controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the changes in Estrogen Receptor-alpha (ER) binding upon fulvestrant-mediated degradation of ER, which compromises ER binding genome-wide. Additionally, we developed an adaptable pipeline to normalise and quantify differential transcription factor binding genome-wide and generate metrics for differential binding at individual sites.Key ResultsChIP-seq data can be normalised by utilising a second control antibodyOur implementation avoids downsampling of the normalised dataWe developed a customisable pipeline to undertake the analysis, implementing both DESeq2 and DiffBindWe provide a robust, normalised dataset that quantifies the effect of fulvestrant on ER binding