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CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes

Abstract : Background: Multiple studies rely on ChIP-seq experiments to assess the effect of gene modulation and drug treatments on protein binding and chromatin structure. However, most methods commonly used for the normalization of ChIP-seq binding intensity signals across conditions, e.g., the normalization to the same number of reads, either assume a constant signal-to-noise ratio across conditions or base the estimates of correction factors on genomic regions with intrinsically different signals between conditions. Inaccurate normalization of ChIP-seq signal may, in turn, lead to erroneous biological conclusions. Results: We developed a new R package, CHIPIN, that allows normalizing ChIP-seq signals across different conditions/samples when spike-in information is not available, but gene expression data are at hand. Our normalization technique is based on the assumption that, on average, no differences in ChIP-seq signals should be observed in the regulatory regions of genes whose expression levels are constant across samples/conditions. In addition to normalizing ChIP-seq signals, CHIPIN provides as output a number of graphs and calculates statistics allowing the user to assess the efficiency of the normalization and qualify the specificity of the antibody used. In addition to ChIP-seq, CHIPIN can be used without restriction on open chromatin ATAC-seq or DNase hypersensitivity data. We validated the CHIPIN method on several ChIP-seq data sets and documented its superior performance in comparison to several commonly used normalization techniques. Conclusions: The CHIPIN method provides a new way for ChIP-seq signal normalization across conditions when spike-in experiments are not available. The method is implemented in a user-friendly R package available on GitHub:
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Submitted on : Wednesday, September 1, 2021 - 11:28:52 AM
Last modification on : Tuesday, January 4, 2022 - 6:50:17 AM
Long-term archiving on: : Thursday, December 2, 2021 - 6:51:35 PM


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Lélia Polit, Gwenneg Kerdivel, Sebastian Gregoricchio, Michela Esposito, Christel Guillouf, et al.. CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes. BMC Bioinformatics, BioMed Central, 2021, 22 (1), pp.407. ⟨10.1186/s12859-021-04320-3⟩. ⟨inserm-03330789⟩



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