| --- |
| license: gpl-3.0 |
| tags: |
| - genomics |
| - bioinformatics |
| - pro-seq |
| - gro-seq |
| - transcription |
| - regulatory-elements |
| - svm |
| - random-forest |
| --- |
| |
| # dREG pretrained models |
|
|
| Pretrained weights for [dREG](https://github.com/Danko-Lab/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](https://github.com/huggingface/safetensors) -- no retraining was performed. See [`config.json`](./config.json) for the full parameter listing. |
|
|
| Used by [`pydreg`](https://github.com/adamyhe/pydreg), a from-scratch Python port of dREG's inference pipeline. `DREGModel.from_pretrained()` / `DREGPeakSplitForest.from_pretrained()` download and load these directly. |
|
|
| ## Files |
|
|
| | file | model | description | |
| |---|---|---| |
| | `svm.model.safetensors.zst` | dREG SVR | RBF-kernel epsilon-SVR that scores a genomic position's regulatory potential (~[0, 1]) from a 360-dim multi-scale feature vector. 605,187 support vectors. | |
| | `rf.model.safetensors.zst` | peak-split forest | 500-tree random forest regression model used only during peak calling, to decide whether two adjacent local score maxima should be merged into one peak or split into two. | |
| | `config.json` | both | Human-readable model configuration (mirrors the metadata embedded in each safetensors file's header). | |
|
|
| 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](https://zenodo.org/records/10113379)), 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 |
|
|
| ```python |
| 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, 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. *bioRxiv*, 321539. https://doi.org/10.1101/321539 |
|
|
| ## 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. |
|
|
