PyPeakRankR: Reproducible Peak-Level Feature Extraction for Regulatory Element Ranking

Quantitative Biology > Genomics [Submitted on 16 Jun 2026] Title:PyPeakRankR: Reproducible Peak-Level Feature Extraction for Regulatory Element Ranking View PDF HTML (experimental)Abstract:High-throughput chromatin accessibility assays such as ATAC-seq generate thousands of candidate regulatory elements (peaks), yet no standardized tool exists for assembling the diverse quantitative features needed to prioritize peaks for functional validation. Here we present PyPeakRankR, an open-source Python package that extracts peak-level features, namely BigWig signal summaries, GC content, PhyloP conservation scores, distribution moments (kurtosis, skewness, bimodality), and cell-type specificity rankings, into a single reproducible peak by feature matrix stored as a tab-separated values (TSV) file. PyPeakRankR separates deterministic feature extraction from downstream ranking, enabling transparent benchmarking of prioritization strategies on the same upstream data. The package provides both a command-line interface and a matching Python API, supports cross-assembly scoring via liftOver, and runs in minutes on thousands of peaks. PyPeakRankR was validated in the Brain Initiative Cell Census Network (BICCN) community challenge, where its predecessor PeakRankR ranked among the top 3 of 16 methods for cell-type specific enhancer prediction. In a recent basal ganglia study, PyPeakRankR was used within the Cross-species Enhancer Ranking Pipeline (CERP) to identify enhancer-AAV tools achieving greater than 70% on-target specificity across cell types. PyPeakRankR is freely available under the MIT license at this https URL. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.