Motif-Hi-C: A motif-based framework for rapid quality control of Hi-C data

Hi-C technology has become indispensable for studying three-dimensional (3D) genome organization; however, the substantial variability in existing genome analysis tools poses challenges for efficient, accurate data processing. Here, we present Motif-Hi-C, an innovative computational framework that leverages the intrinsic sequence signatures of Hi-C chimeric reads to enable rapid, reliable quality control. Through systematic benchmarking of four mainstream tools (HiC-Pro, HiCUP, Juicer, and HiCExplorer) across diverse Hi-C datasets, we identified critical trade-offs between processing speed and analytical precision. Motif-Hi-C addresses these limitations by implementing a motif-aware classification system that achieves faster processing with more valid interacting rmdup/total read pairs. Key innovations include (1) automated detection of ligation-motif signatures in raw sequencing data, (2) category-specific parallel processing pipelines, and (3) dynamic quality thresholds adapted to different experimental protocols. Validation across multiple cell types and restriction enzymes demonstrated robust performance, with particular advantages for large-scale consortium datasets. This framework establishes a new paradigm for efficient quality assessment in 3D genomics studies, seamlessly integrating with downstream chromatin interaction analyses.