Al-Murphy/alphagenome-encoder-ft

AlphaGenome Encoder — fine-tuned MPRA / STARR-seq checkpoints

Fine-tuned AlphaGenome encoder checkpoints for massively parallel reporter assays. The AlphaGenome transformer is bypassed: a regression head is trained on the raw encoder output (128 bp resolution), which is both far cheaper and — on these short-sequence reporter tasks — more accurate than using the full model.

Four benchmarks, in both JAX (Haiku) and PyTorch where available:

• lentiMPRA (Agarwal et al.) — K562, HepG2, WTC11
• lentiMPRA (Gosai et al.) — K562, HepG2, SKNSH
• Drosophila STARR-seq (DeepSTARR; de Almeida et al.) — developmental + housekeeping
• Plant STARR-seq (Jores et al. 2021) — tobacco leaf and maize protoplast, 3 data modes

Code: Al-Murphy/alphagenome_FT_MPRA

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⚠️ Licence

These are fine-tuned derivatives of AlphaGenome. The model parameters, their outputs, and any derivatives thereof remain subject to Google DeepMind's AlphaGenome Model Terms, including the restriction to non-commercial use. The base parameters were created by Google DeepMind and are the property of Google LLC.

Only the fine-tuning code is Apache-2.0. We are not relicensing the weights.

Loading also requires the base AlphaGenome weights (google/alphagenome-all-folds), which are access-gated — accept the terms there and huggingface-cli login first.

Usage

pip install git+https://github.com/Al-Murphy/alphagenome_FT_MPRA

from alphagenome_ft_mpra.hub import list_pretrained, load_pretrained

list_pretrained()

model = load_pretrained('plant-starrseq-leaf-combined')   # JAX, fine-tuned
model = load_pretrained('mpra_K562')                      # PyTorch
preds = model.predict_sequences(['ACGT...'], construct_mode='promoter_barcode')

load_pretrained reads each checkpoint's config.json to build the right head at the right width — see the repo's docs/model_weights.md for the manual path and the gotchas.

Contents

stage1 = frozen encoder (head only trained); stage2 = encoder fine-tuned.

torch/ — test Pearson r

Checkpoint	Task	frozen	fine-tuned
mpra_K562	lentiMPRA K562	0.8580	0.8785
mpra_HepG2	lentiMPRA HepG2	0.8688	0.8876
mpra_WTC11	lentiMPRA WTC11	0.8278	0.8344
starrseq_drosophila	DeepSTARR (dev + hk)	0.6184	0.7468

Drosophila is the mean of the two tasks (fine-tuned: dev 0.7193, hk 0.7744).

jax/ — plant STARR-seq (Jores 2021), test Pearson r

Every value below was re-verified by loading the released checkpoint and re-running inference.

Tissue	Mode	probe (stage1)	fine-tuned (stage2)
leaf	combined	0.7821	0.8899
leaf	enhancer	0.7660	0.8749
leaf	promoter_only	0.6876	0.7802
proto	combined	0.7884	0.8795
proto	enhancer	0.6870	0.8036
proto	promoter_only	0.7015	0.7683

Plus jax/{mpra-K562,mpra-HepG2,mpra-WTC11,deepstarr}-optimal (see the paper for their metrics).

jax/ — Gosai et al. lentiMPRA (Gosai-<cell>-optimal)

Cell-averaged Pearson r (from the paper's 4-panel figure):

Panel	Probing (S1)	Fine-tuned (S2)
Genomic Reference	0.877	0.910
High-Activity Designed	0.709	0.776
SNV Effects (Ref−Alt)	0.364	0.400

Reproduced here: loading Gosai-K562-optimal/stage2 and scoring the held-out chr7+13 K562 set gives Pearson 0.9195.

Notes

• Plant stage1 is a ridge probe, not a model. It carries no encoder weights — the features come from the unmodified pretrained encoder. Use load_plant_probe().
• Plant head width varies per cell (4096 / 2048 / 1024). It's recorded in each checkpoint's config.json; don't assume a default.
• Inputs are reporter constructs, not bare genomic sequence — each checkpoint expects the construct it was trained on (promoter+barcode, library adapters, or 35S enhancer + core promoter + 5′ UTR + barcode). See the repo docs.
• Plant test constructs use a random barcode per row, so Pearson reproduces to ~±0.0001; quote plant numbers to 3 decimals.

Citation

A paper is in preparation. Until it is available, please cite the blog post:

Murphy, A., Durán, A., & Koo, P. K. (2026). Adapting AlphaGenome to MPRA data. Genomics x AI Blog, 20 February 2026. https://genomicsxai.github.io/blogs/2026-002/. https://doi.org/10.5281/zenodo.20272900

@article{Murphy2026,
  author  = {Murphy, Alan and Dur{\'a}n, Alejandra and Koo, Peter K.},
  title   = {Adapting AlphaGenome to MPRA data},
  journal = {Genomics x AI Blog},
  year    = {2026},
  month   = {February},
  day     = {20},
  url     = {https://genomicsxai.github.io/blogs/2026-002/},
  doi     = {10.5281/zenodo.20272900}
}

Please also cite AlphaGenome (Google DeepMind) and the underlying datasets (Agarwal et al.; Gosai et al.; de Almeida et al.; Jores et al. 2021).