UnionLoops: a workflow for calling chromatin loops across related Hi-C datasets with improved specificity, precision, and sensitivity.

Chromatin loop calling from Hi-C data often exhibits substantial variability across related samples, limiting reproducibility and complicating comparative biological analyses. Conventional loop callers such as HiCCUPS are optimized for single-sample loop detection and are not designed for consistent comparison of loop positions across multiple datasets, e.g., across conditions or time points. Here, we present UnionLoops, a computational workflow for reproducible chromatin loop calling across multiple related samples. UnionLoops integrates information across datasets to determine positions and dataset-specificity of looping interactions. It constructs a unified candidate loop set, applies consistent filtering and aggregation, and evaluates loop support across samples to distinguish shared looping interactions from dataset-specific loop calls. Using time-course Hi-C datasets, we demonstrate that UnionLoops increases sensitivity for detecting shared chromatin loops, reduces spurious sample-specific calls, and improves concordance with independent genomic features, including CTCF and cohesin occupancy. These improvements support more reliable downstream analyses and enable improved biological interpretation of chromatin loop organization and dynamics across related experimental conditions.