ChIPbinner: an R package for analyzing broad histone marks binned in uniform windows from ChIP-Seq or CUT&RUN/TAG data

Abstract Background The decreasing costs of sequencing, along with the growing understanding of epigenetic mechanisms driving diseases, have led to the increased application of chromatin immunoprecipitation (ChIP), Cleavage Under Targets & Release Using Nuclease (CUT&RUN) and Cleavage Under Targets and Tagmentation (CUT&TAG) sequencing—which are designed to map DNA or chromatin-binding proteins to their genome targets—in biomedical research. Existing software tools, namely peak-callers, are available for analyzing data from these technologies, although they often struggle with diffuse and broad signals, such as those associated with broad histone post-translational modifications (PTMs). Results To address this limitation, we present ChIPbinner, an open-source R package tailored for reference-agnostic analysis of broad PTMs. Instead of relying on pre-identified enriched regions from peak-callers, ChIPbinner divides (bins) the genome into uniform windows. Thus, users are provided with an unbiased method to explore genome-wide differences between two samples using scatterplots, principal component analysis (PCA), and correlation plots. It also facilitates the identification and characterization of differential clusters of bins, allowing users to focus on specific genomic regions significantly affected by treatments or mutations. We demonstrated the effectiveness of this tool through simulated datasets and a case study assessing H3K36me2 depletion following NSD1 knockout in head and neck squamous cell carcinoma, highlighting the advantages of ChIPbinner in detecting broad histone mark changes over existing software. Conclusions Binned analysis provides a more holistic view of the genomic landscape, allowing researchers to uncover broader patterns and correlations that may be missed when solely focusing on individual peaks. ChIPbinner offers researchers a convenient tool to perform binned analysis. It improves on previously published software by providing a clustering approach that is independent of each bin’s differential enrichment status and more precisely identifies differentially bound regions for broad histone marks, while also offering additional features for downstream analysis of these differentially enriched bins.