MotifPeeker: Benchmarking epigenomic profiling methods using motifs

Summary MotifPeeker benchmarks epigenomic profiling methods targeting transcription factors (TFs) where no “gold standard” reference exists, using motif enrichment as a key metric. With minimal input, users can analyse their data in a single function and receive an intuitive HTML report. Availability and Implementation MotifPeeker is available on Bioconductor (≥ v3.21) at https://bioconductor.org/packages/MotifPeeker. The complete source code is available on GitHub at https://github.com/neurogenomics/MotifPeeker, with full documentation provided at https://neurogenomics.github.io/MotifPeeker. Additionally, the MotifPeeker Docker image is hosted on GitHub at https://github.com/neurogenomics/MotifPeeker/pkgs/container/motifpeeker. Competing Interest Statement The authors have declared no competing interest. Footnotes Clarifications on scope and applicability: The manuscript now explicitly states that MotifPeeker is designed for benchmarking epigenomic profiling methods targeting transcription factors (TFs), where datasets must profile the same TF across different methods. We clarified that MotifPeeker is not suitable for broader assays such as ATAC-Seq. Enhanced differentiation from existing tools: We expanded the discussion of ChIPComp and DiffBind to better distinguish MotifPeeker's motif-centred approach from peak-level comparison tools. We added a direct comparison with MEME-ChIP, clarifying that whilst MEME-ChIP focuses on comprehensive motif analysis within single datasets, MotifPeeker is tailored for cross-method benchmarking using motif enrichment as a quality metric. Extended genome and motif format support: The manuscript now documents support for mouse reference genomes (mm10, mm39) alongside human genomes (hg19, hg38), and clarifies that any reference genome can be used via BSgenome objects from Bioconductor. We explicitly noted support for multiple motif formats (JASPAR, MEME, TRANSFAC) and custom motifs via universalmotif objects. Statistical robustness improvements: We implemented a bootstrapping procedure for summit-to-motif distance analysis, allowing users to assess the precision of peak localisation and statistically compare distributions across datasets. Users can customise both sample size and resampling iterations. We explained why bootstrapping was not implemented for AME-based enrichment analysis, given the algorithm's inherent robustness and computational intensity. Technical specifications: We added details on supported peak file formats (MACS and SEACR outputs) and corrected terminology throughout (e.g. 'gold dataset' to 'gold standard dataset'). Updated references and links: The Bioconductor link now points to the stable release channel. Enhanced documentation: Figure 1 has been expanded to include the basic comparison scheme and workflow of inputs within the package, improving visualisation of MotifPeeker's analytical pipeline.