HTAD: a human-in-the-loop framework for supervised chromatin domain detection

Abstract

Topologically associating domains (TADs) are essential units of genome architecture, influencing transcriptional regulation and diseases. Despite numerous methods proposed for TAD identification, it remains challenging due to complex background and nested TAD structures. We introduce HTAD, a human-in-the-loop TAD caller that combines machine learning with human supervision to achieve high accuracy. HTAD begins with feature extraction for potential TAD border pairs, followed by an interactive labeling process through active learning. Performance assessments using public curation and synthetic datasets demonstrate HTAD’s superiority over other state-of-the-art methods and reveal highly hierarchical TAD structures, offering a human-in-the-loop solution for detecting complex genomic patterns.