What happened
Background: Active promoters and enhancers generate characteristic patterns of RNA transcription that can be measured through nascent RNA sequencing. dREG is a leading method that uses these patterns to identify active cis-regulatory elements across the genome, allowing regulatory activity and gene transcription to be profiled in the same experiment. However, its reference implementation was developed around an R-based workflow and a legacy GPU-accelerated support vector machine library that have become increasingly difficult to maintain and deploy. Findings: To improve future usability of dREG, we developed pydreg, a Python port of dREG. pydreg preserves the original pretrained models and peak calling procedure from dREG while using contemporary numerical libraries for CPU and GPU computation. pydreg achieves 4.5 and 5.4-fold reductions in runtime and peak host memory, respectively, compared to dREG while producing near identical peak calls. Conclusions: pydreg reduces practical barriers to running dREG locally, improves runtime and memory usage, integrates readily with Python-based genomics workflows, and provides a maintainable foundation on modern computing infrastructure. Availability and Implementation: pydreg is implemented in Python 3.11+ and is freely available under the GPL-3 license at https://github.com/adamyhe/pydreg and from PyPI via pip install pydreg[gpu] (for CUDA acceleration) or pip install pydreg[mlx] (for Apple Metal acceleration).