ContextTAD: Context-aware boundary learning for TAD calling from Hi-C contact maps

Abstract

MotivationReliable topologically associating domain (TAD) calling from Hi-C contact maps remains difficult at high resolution and realistic sequencing depth. A central reason is that many callers learn boundary evidence largely from local signals, while domain compatibility is handled mainly during downstream decoding, so the learned boundary scores are not explicitly optimized for the TAD assembly step that ultimately determines the final calls. ResultsWe present ContextTAD, a deep-learning TAD caller that learns boundary evidence from broader local Hi-C windows that capture TAD-scale structural context. Instead of treating boundary prediction as an isolated per-bin classification problem, ContextTAD uses a context-aware representation to produce left- and right-boundary tracks that are explicitly optimized for downstream TAD assembly. Concretely, the model combines multiscale feature extraction from 2D Hi-C windows with a pair objective that rewards compatible boundary combinations and a count objective that regularizes window-level boundary evidence. Due to the limited availability of high-quality TAD annotations, supervised deep-learning methods for TAD calling remain rare. To address this bottleneck, we construct improved training annotations by integrating high-coverage Hi-C structure with complementary boundary-associated genomic signals, thereby providing more reliable supervision for model training. We benchmarked ContextTAD against a broad panel of alternative TAD callers across standard comparative evaluation, sequencing-depth robustness analysis, and cross-cell-type transfer settings, and found that it performed strongly against alternative tools across this wide range of settings, with the best overall recovery of biologically supported TADs. Availabilityhttps://github.com/ai4nucleome/ContextTAD Contactyanlinzhang@hkust-gz.edu.cn