pydreg 0.2.0

End-to-end dREG peak calling from PRO-seq/GRO-seq bigWig data

pydreg

An inference-only Python port of dREG (Danko Lab) — detects active transcriptional regulatory elements (promoters and enhancers) from PRO-seq/GRO-seq nascent-transcription data.

Given a pair of strand-specific bigWig files, pydreg scores every informative genomic position with a pretrained SVR model, then calls significant peaks with FDR control, mirroring the original R package's recommended run_dREG.R pipeline end to end.

Installation

pip install pydreg[gpu]
pydreg --help

Or with uv — uv tool install if you only need the pydreg CLI (isolated environment, nothing else to manage):

uv tool install pydreg[gpu]
pydreg --help

If you want the Python API (from pydreg import pipeline, see below) available in your own project instead, use uv add pydreg[gpu] there, or uv pip install pydreg[gpu] into an already-active environment.

[gpu] installs CuPy and enables GPU-accelerated scoring on Linux with an NVIDIA GPU (auto-selected whenever one's detected). It is not required, but CPU-only scoring is much slower and so is not generally recommended.

GPU requirement: compute capability ≥3.0 (essentially any CUDA-capable NVIDIA GPU from the last decade-plus, including older Pascal-class cards). Scoring runs pydreg's own RBF kernel implementation directly on a CuPy device array (pydreg.backend._build_cupy_predict_fn) enables broad CUDA compatibility and fp32 support. v0.1.x used cuML, which came with significant GPU restrictions (no Pascal support) and no fp32 support — see docs/OPTIMIZATION.md for the full writeup, including why that library was dropped (real hardware confirmed it silently returned wrong scores below compute capability 7.0).

Pretrained model weights (an RBF-kernel SVR scorer and a small random-forest peak-splitter) are downloaded automatically from adamyhe/pydreg on Hugging Face the first time they're needed, and cached locally by huggingface_hub.

Usage

CLI

pydreg plus.bw minus.bw out_prefix --verbose

• plus.bw/minus.bw: strand-specific bigWig files (3′-mapped, point-mode, unnormalized read counts — the same input format the original dREG expects). See proseq2.0 for the Danko lab's pipeline. minus.bw may be positive- or negative-signed — pydreg takes the absolute value of both strands during feature extraction (matching the original C implementation), so sign convention doesn't affect scoring.
• out_prefix: prefix for all output files (see below).

Options:

flag	default	meaning
--backend {auto,cupy,sklearn,numpy}	auto	Scoring backend. auto uses cupy when a usable CUDA device is detected, otherwise pure NumPy. scikit-learn is selectable explicitly but is not auto-selected (see docs/OPTIMIZATION.md for why). An explicit choice raises if that backend isn't actually usable, rather than silently falling back.
--smoothwidth N	4	Smoothing window used during peak-splitting.
--pv-adjust METHOD	fdr	Multiple-testing correction method (any statsmodels.stats.multitest.multipletests method name).
--pv-threshold P	0.05	Significance threshold applied after correction.
--query-chunk N	backend-specific	Positions scored per batch; defaults to a size tuned per backend (pydreg.backend.DEFAULT_QUERY_CHUNK).
--cupy-sv-chunk N	32768	Support vectors (of 605,187) evaluated per GPU kernel/GEMM call for the cupy backend specifically. The main lever for trading GPU memory for fewer, larger, better-amortized kernel launches — real headroom varies by card, so sweep a few values on your target GPU (see docs/OPTIMIZATION.md).
--peak-calling-cores N	1	Worker processes for the final peak-calling stage (embarrassingly parallel across broad candidate peaks).
--peak-calling-block-width N	100	Candidate broad peaks handed to each peak-calling worker per task; smaller blocks improve load balancing on uneven peak sizes.
--pmv-laplace-cdf-maxpts N	25000	Max integration points for the per-summit p-value's quasi-Monte-Carlo integral; matches R's mvtnorm::pmvnorm()/GenzBretz() default. Only lower this if you want to trade fidelity with R for further speed.
--pmv-laplace-cdf-eps EPS	0.001	Absolute/relative tolerance for the same integral; also matches R's default. Only lower this (i.e. tighten precision) if you specifically want to exceed R's own reference precision, at real speed cost.
--no-progress	off	Disable tqdm progress bars (auto-hidden anyway when stdout isn't a terminal).
-v, --verbose	off	Log progress at INFO level.

Python API

from pydreg import pipeline

result = pipeline.run("plus.bw", "minus.bw", "out_prefix", backend_name=None)
# result: {"dense_infp": ..., "raw_peak": ..., "peak_bed": ..., "min_score": ...}

Pass write_outputs=False to get the result dict back without writing files, if you just want to work with the DataFrames directly.

Output files

Given out_prefix, pydreg writes:

file	contents
{out_prefix}.dREG.infp.bed.gz (+.tbi), .bw	Every informative position and its raw dREG score.
{out_prefix}.dREG.raw.peak.bed.gz (+.tbi)	All candidate peaks before FDR filtering.
{out_prefix}.dREG.peak.full.bed.gz (+.tbi)	Significant peaks: chrom, start, end, score, p-value, center.
{out_prefix}.dREG.peak.score.bed.gz/.bw (+.tbi)	Significant peaks' scores only.
{out_prefix}.dREG.peak.prob.bed.gz/.bw (+.tbi)	Significant peaks' 1 - p-value.

.bed.gz files are bgzipped and tabix-indexed; .bw files are standard bigWig tracks.

How it works

1. Informative-position scan — tiles the genome looking for positions with any transcriptional signal on either strand, to avoid scoring silent regions.
2. Feature extraction — for each informative position, bins nearby read counts into multiple nested window sizes ("zoom levels") per strand, producing a fixed-length feature vector.
3. Scoring — an RBF-kernel SVR (605,187 support vectors, trained on the original dREG data) maps each feature vector to a dREG score in ~[0, 1].
4. Peak calling — merges scored positions into broad candidate regions, refines local maxima with a small random-forest model to decide where to split adjacent peaks, computes a per-peak p-value, and applies FDR control to select significant peaks.

See docs/METHODS.md for a plain-language walkthrough of each stage, and docs/OPTIMIZATION.md for the performance design choices layered on top without changing any of the above. docs/PLANNING.md/docs/PERF_LOG.md are the underlying comprehensive design/research records (full algorithmic spec, every upstream R quirk and why it's kept, every benchmark) for anyone going deeper.

This is validated directly against the original: on real test data, pydreg's called peaks agree with real dREG's at a >0.999 Jaccard index.

Caveats

• minus.bw's sign doesn't matter: both informative-position detection and feature extraction take the absolute value of the minus-strand signal (the latter matching the original C's bigwig_readi(..., abs=1, ...) read call, which strips sign from both strands before any binning) — see docs/PLANNING.md for the sourced trace.
• A handful of upstream R bugs/quirks are faithfully replicated rather than fixed, because the pretrained model's expected behavior was produced by that exact code (e.g. a mean()-argument-binding bug in the p-value calculation, an off-by-one in broad-peak merging that drops the last group per chromosome, and others) — see docs/PLANNING.md for the full list and reasoning.
• Peak-calling p-values have small inherent run-to-run noise (the per-summit p-value's underlying quasi-Monte-Carlo integral is unseeded, matching the original R implementation's mvtnorm::pmvnorm, which is also unseeded) — this doesn't affect which peaks are called significant in practice, and is reflected in the 0.999728 (not exactly 1.0) Jaccard index above.

Contributing

See CONTRIBUTING.md for development setup, running tests, and what to read before making algorithmic or performance changes.

License

GPL-3.0 (matching the original dREG R package, which is GPL-3-licensed).

Citation

If you use this package, please cite the original dREG papers:

Danko, C. G., Hyland, S. L., Core, L. J., Martins, A. L., Waters, C. T., Lee, H. W., Baranello, L., Yang, Z., Wong, S. E., Setola, V., Lee, S. K., ... & Siepel, A. (2015). Identification of active transcriptional regulatory elements from GRO-seq data. Nature Methods, 12(5), 433-438. https://doi.org/10.1038/nmeth.3329

Wang, Z., Chu, T., Choate, L. A., & Danko, C. G. (2018). Identification of regulatory elements from nascent transcription using dREG. Genome Research, 29, 293–303. https://doi.org/10.1101/gr.238279.118

Please also cite the version number of this port to improve reproducibility.