| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - biology |
| - genomics |
| - gene-regulatory-network |
| - protein-protein-interaction |
| - soybean |
| - plant-genomics |
| pretty_name: SoyGRN — soybean GRN models, inferred networks, and expression data |
| --- |
| |
| # SoyGRN: trained models and inferred networks for soybean transcriptome-scale GRN inference |
|
|
| Reusable data and model release for the paper *"Transcriptome-scale gene regulatory network |
| inference in soybean: GPU-scalable tree ensembles versus a masked-expression foundation model"* |
| (Genome Biology). This is the deposited resource referenced by the manuscript's *Availability of |
| data and materials* declaration and Supplementary Tables S1–S2. **Analysis code is on GitHub: |
| https://github.com/k821209/soygrn** |
|
|
| All identifiers are Wm82.a6 (Phytozome Gmax_880_v6.0) gene loci for soybean and Araport11 for |
| Arabidopsis. Protein embeddings throughout are mean-pooled ESM-2 `esm2_t12_35M_UR50D` (480-d). |
|
|
| ## Trained models |
|
|
| ### `grn_fm_W.npz` — SoyGRN-FM (the network *is* the weight tensor) |
| The trained masked-expression foundation model. NumPy archive: |
| - `W` — float32, shape **(3419 TF × 48358 target)**: the signed regulator→target weight tensor; this single tensor is the entire GRN (no other reg→target path in the model). |
| - `tf_idx` — int64 (3419): row → index into `genes` for each transcription factor. |
| - `genes` — <U15 (48358): gene locus IDs indexing the columns of `W` (and `tf_idx`). |
| |
| Read out the network as the top-30 regulators per target by `|W[:,j]| · std(ψ(activity))`, sign from |
| `sign(W)`; or apply the invariance re-ranking (Supplementary Source Code S1, `grn_fm_invar.py`, |
| γ=3) to reproduce `edges_fm_invar.tsv`. |
| |
| ```python |
| import numpy as np |
| z = np.load("grn_fm_W.npz", allow_pickle=True) |
| W, tf_idx, genes = z["W"], z["tf_idx"], z["genes"] |
| tf_names = genes[tf_idx] # 3419 TF locus IDs |
| # top-10 regulators of a target gene j: |
| j = list(genes).index("Glyma.01G000100") |
| top = np.argsort(-np.abs(W[:, j]))[:10] |
| for r in top: print(tf_names[r], W[r, j]) # weight sign = activation(+)/repression(-) |
| ``` |
| |
| ### `ppi_fm_intact.pt` — PPI foundation model (PyTorch) |
| Symmetric Siamese network over ESM-2 embeddings, trained on **all 27,422** Arabidopsis IntAct |
| experimental-physical interactions (no holdout). This is the model applied **zero-shot to soybean** |
| in the paper (AUROC 0.953 on the ortholog-transferred gold). `dict` with `state_dict` + `config`. |
| Order-invariant: `sigmoid(model(emb_a, emb_b))` = interaction probability. Architecture in |
| `code/grn_ppi_fm_final.py` (class `PPIFM`). |
| |
| ### `binding_promcnn.pt` — TF→promoter binding model (PyTorch) |
| The best binding recoverer (leave-TF-out AUROC 0.825): promoter 1D-CNN (max+mean pool) + ESM-2 TF |
| embedding, trained on **all 15,000** Arabidopsis FunTFBS binding pairs. Input = 10-channel 500 bp |
| promoter (4 one-hot ACGT + 6 z-scored dinucleotide DNA-shape: twist, roll, propeller, slide, rise, |
| tilt) and the TF ESM-2 embedding. `dict` with `state_dict` + `config`. Architecture in |
| `code/grn_binding_cnn_final.py` (class `PromCNN`). |
| |
| ## Inferred soybean networks (TF, target, score[, sign]; tab-separated, header row) |
| |
| | File | Method | Edges | Signed | |
| |---|---|---|---| |
| | `edges_clr.tsv` | CLR | 680,381 | no | |
| | `edges_grnboost_all.tsv` | GRNBoost2 | 4,000,000 | no | |
| | `edges_fm_invar.tsv` | SoyGRN-FM (invariance re-ranked, top-30/target) | 1,450,740 | yes (+1 activation / −1 repression) | |
| |
| ## Protein embeddings & ortholog map |
| |
| - `gmax_prot_esm.npz` — soybean proteome ESM-2 embeddings (`emb`, `genes`); inputs to the PPI/binding models for soybean. |
| - `glyma_to_athal_rbh.tsv` — soybean↔Arabidopsis reciprocal-best-hit ortholog map (12,939 pairs; columns: glyma, athal, bitscore), used for cross-species PPI/binding transfer. |
| |
| ## Large primary inputs (deposited separately as Zenodo files; not in this folder due to size) |
| |
| - `gmax_gene_tpm.npz` — *G. max* gene-level TPM, 48,358 × 8,177 (1.5 GB) + `gmax_sample_meta.csv` (sample→SRA study). |
| - `athal_gene_tpm.npz` — *A. thaliana* compendium, 27,572 × 29,089 (3.2 GB) + `athal_sample_meta.csv`. |
| - `athal_prot_esm.npz` — Arabidopsis proteome ESM-2 embeddings (training inputs for PPI/binding models). |
| |
| Per-sample SRA run accessions and study (BioProject) IDs are in the `*_sample_meta.csv` tables. |
| Public input resources (PlantTFDB/PlantRegMap FunTFBS & GO, IntAct, STRING v12, JASPAR, the Wm82.a6 |
| reference) are obtained from their original providers under their own licences and are not |
| re-deposited here. See Supplementary Table S1 for the full inventory. |
| |
| ## Code |
| |
| All analysis code (35 Python scripts: inference, evaluation, GO coherence, binding & PPI recovery, |
| the two model-training scripts above, figures) is at **https://github.com/k821209/soygrn**. See |
| Supplementary Table S2 for per-model hyperparameters. `grn_fm_W.npz` is produced by `grn_fm.py`; |
| `ppi_fm_intact.pt` by `grn_ppi_fm_final.py`; `binding_promcnn.pt` by `grn_binding_cnn_final.py`. |
| |
| ## Manifest |
| |
| SHA-256 checksums for all deposited files are in `SHA256SUMS.txt`. |
| |
| ## Citation |
| |
| If you use these models or networks, please cite the paper (DOI on publication) and this deposit. |
| |
