HiFAA: a high-confidence framework for transcription factor footprinting with ATAC-seq

Accurate inference of transcription factor (TF) binding from chromatin accessibility remains a major challenge, as conventional methods often miss true binding events, yield unstable motif rankings, or fail to resolve dynamic changes. Here we present high-confidence transcription factor footprint analysis using ATAC-seq (HiFAA), a framework that integrates topologically associating domain–restricted analysis, single-base-resolution footprint construction, and footprint ranking based on footprint bottom height. HiFAA markedly improves sensitivity while maintaining high specificity, enabling detection of binding events overlooked by existing analyses. It also ranks truly bound TFs with higher motif scores, enhancing predictive accuracy, and maintains consistently high geometric mean (G-mean) values, providing a stable balance between sensitivity and specificity. Furthermore, its high-resolution footprints capture subtle structural variations, allowing precise detection of developmental stage–specific TF-binding. By uniting sensitivity, specificity, and dynamic resolution, HiFAA establishes a robust paradigm for reliable TF footprinting from ATAC-seq data.