fp-tools: A Reproducible Platform for ATAC-seq Footprinting and Regulatory Motif Analysis

Background/Objectives: ATAC-seq footprinting can infer transcription-factor (TF) occupancy across the genome at near-base-pair resolution. However, its broad adoption is limited by high computational demands, complex command-line workflows, and fragmented support for bulk data with biological replicates and single-cell data. We developed fp-tools, a Python package that extends the TOBIAS framework into an integrated and reproducible platform for TF footprinting and motif discovery. Methods: fp-tools provides command-line and graphical workflows for Tn5 bias correction, footprint scoring, motif scanning, de novo motif discovery, replicate-aware differential analysis, scaled motif aggregation, and pseudobulk processing of single-cell ATAC-seq data. It compares TF occupancy across conditions using corrected cut-site profiles and motif-centered footprint scores. The package also produces interactive HTML reports, editable figures, and reusable YAML configurations. Results: In analyses of ENCODE ATAC-seq replicates from seven cancer cell lines, fp-tools recovered expected cell-type-associated TF programs, including erythroid and hepatocyte-lineage regulators. Validation against matched ChIP-seq data from four lines yielded a median area under the receiver operating characteristic curve (AUROC) of 0.765. In a public single-cell peripheral blood mononuclear cell (PBMC) ATAC-seq dataset, the pseudobulk workflow identified cell-type-specific footprint signatures across immune cell populations. fp-tools also discovered de novo motifs from candidate footprints that did not match known motif databases. In runtime benchmarking, fp-tools completed analyses faster and used less peak memory than the matched TOBIAS workflow tested in this study. Conclusions: fp-tools makes TOBIAS-style ATAC-seq footprinting more accessible and computationally efficient for bulk and single-cell studies. Its command-line tools, graphical interface, and interactive reports support reproducible analysis of TF occupancy in public and user-generated ATAC-seq datasets. Source code, examples, and documentation are available through the project’s GitHub repository and documentation website.