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

• Main Authors: Boeva, V. (Author), Esposito, M. (Author), Gregoricchio, S. (Author), Guillouf, C. (Author), Kerdivel, G. (Author), Polit, L. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Algorithm; Binding intensity; Biochemistry; ChIP-seq; chromatin; Chromatin; chromatin immunoprecipitation; Chromatin Immunoprecipitation; Chromatin Immunoprecipitation Sequencing; Chromatin structure; Correction factors; Density profiles; DNA sequence; Expression levels; Gene expression; Gene Expression Data; Normalization; Open chromatin; protein binding; Protein binding; Protein Binding; R package; Regulatory regions; Sequence Analysis, DNA; Signal normalization; Signal to noise ratio
• Online Access: View Fulltext in Publisher

 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: https://github.com/BoevaLab/CHIPIN. © 2021, The Author(s).