pydreg

dREG pretrained models

Pretrained weights for dREG (Danko Lab), a method for detecting active transcriptional regulatory elements (promoters and enhancers) from PRO-seq/GRO-seq nascent-transcription data.

These are the original dREG model parameters, extracted from the R package's distributed .RData/.RDS files and repacked as framework-agnostic safetensors -- no retraining was performed. See config.json for the full parameter listing.

Used by pydreg, a from-scratch Python port of dREG's inference pipeline. DREGModel.from_pretrained() / DREGPeakSplitForest.from_pretrained() download and load these directly.

Files

Both .safetensors.zst files are zstandard-compressed safetensors -- pydreg's loader decompresses them transparently, or use zstd -d manually.

Provenance

• SVR -- extracted from asvm.gdm.6.6M.20170828.rdata (Zenodo), the pretrained model bundled with dREG. The saved object (class gtsvm, trained via Rgtsvm) is field-compatible with e1071's standard RBF epsilon-SVR dual form -- no re-training, only a format conversion.
• Peak-split forest -- extracted from rf-model-201803.RDS, bundled with the dREG R package (dREG/inst/extdata/), a randomForest regression forest used only in peak_calling_rf.R's find_rf_peaks()/split_peak().

Both files contain the exact weights the original R package ships and uses at inference time -- this repo hosts a format conversion, not a retrained or fine-tuned model.

Usage

from pydreg.models import DREGModel, DREGPeakSplitForest

svr = DREGModel.from_pretrained()            # downloads svm.model.safetensors.zst
rf = DREGPeakSplitForest.from_pretrained()   # downloads rf.model.safetensors.zst

scores = svr.predict(X)   # X: (n_queries, 360) features from pydreg.features

See pydreg's documentation for the full pipeline: informative-position scanning -> multi-scale feature extraction -> SVR scoring -> RF-assisted peak calling -> FDR filtering.

Citation

If you use these models via pydreg, please cite the pydreg preprint:

He, A. Y., & Danko, C. G. (2026). pydreg: a fast Python package for identifying active cis-regulatory elements from nascent transcription. bioRxiv. https://doi.org/10.64898/2026.09.06.745329

Since these are dREG's own pretrained weights, please also cite the original dREG papers:

Danko, C. G., Hyland, S. L., Core, L. J., Martins, A. L., Waters, C. T., Lee, H. W., Cheung, V. G., Kraus, W. L., Lis, J. T., & 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

as well as the version number of pydreg that you used.

License

GPL-3.0, matching the original dREG R package (License: GPL-3 in its DESCRIPTION). These files are a format conversion of dREG's publicly distributed pretrained model weights, not a redistribution of its source code, but are licensed the same way as the rest of this port.