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license: apache-2.0
tasks:
  - genomic-sequence-modeling
frameworks:
  - jax
language:
  - en
  - zh
tags:
  - OneScience
  - Life Sciences
  - Genomics
  - DNA Sequence Model
  - Variant Effect Prediction
  - AlphaGenome
datasets:
  - OneScience-Sugon/alphagenome_dataset

AlphaGenome

Model Introduction

AlphaGenome is a DNA sequence model developed by Google DeepMind. It accepts DNA intervals of up to 1 Mbp as input and predicts a range of functional genomic signals for genomic track prediction and regulatory variant-effect scoring.

Paper: Advancing regulatory variant effect prediction with AlphaGenome
https://www.nature.com/articles/s41586-025-10014-0

Model Description

AlphaGenome is implemented in JAX / Flax and supports genomic interval inference, variant effect scoring, track evaluation, and example fine-tuning workflows. This model package is accompanied by the Hugging Face dataset OneScience-Sugon/alphagenome_dataset, which enables rapid local validation.

Use Cases

Scenario	Description
Genomic interval prediction	Takes a reference-genome FASTA file, a chromosome, and genomic interval coordinates as input and outputs predicted tracks such as ATAC, DNase, CAGE, RNA-seq, and ChIP
Variant effect scoring	Takes either a VCF file or built-in example variants as input, compares predictions for the reference and variant sequences, and produces a variant scoring table
Track prediction evaluation	Uses validation data from the AlphaGenome dataset to compute regression evaluation metrics across different assay bundles
Fine-tuning experiments	Validates a fine-tuning workflow using custom reference genomes, interval CSVs, and BigWig signal files
Hugging Face / OneCode execution	After downloading the model project and accompanying dataset, quickly verifies that the scripts run correctly in a life-sciences runtime environment

Usage Guide

1. OneCode Usage

Try one-click AI4S development in the OneCode online environment:

Try one-click AI4S development

2. Manual Installation and Usage

Hardware Requirements

GPU or DCU is recommended.
A CPU can be used for import checks and lightweight configuration tests; full training and inference will be slow.
DCU users must install DTK in advance. DTK 25.04.2 or later is recommended, or a OneScience-recommended version matching the current cluster.

Environment Check

NVIDIA GPU:

nvidia-smi

Hygon DCU:

hy-smi

Download the Model Package

hf download --model OneScience-Sugon/alphagenome --local-dir ./alphagenome
cd alphagenome

Install the Runtime Environment

DCU Environment

# Activate DTK and CONDA first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation supported
pip install onescience[bio-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

After installation, return to the model package directory:

cd ./alphagenome

Data for Inference, Evaluation, and Fine-Tuning

The OneScience community has uploaded the data required for AlphaGenome inference, evaluation, and fine-tuning to Hugging Face: OneScience-Sugon/alphagenome_dataset. After downloading, place the data in the data/ directory under the model package.

hf download --dataset OneScience-Sugon/alphagenome_dataset --local-dir ./data

Model Weights

The repository already includes weight/alphagenome-all-folds, and all scripts allow the model weights directory to be specified via --model_dir.

Preparing Weights

If using local weights, place the AlphaGenome Orbax checkpoint in the following directory:

weight/
  alphagenome-all-folds/
    _CHECKPOINT_METADATA
    _METADATA
    ...

When running in a shared environment, you can also reuse centrally managed model and dataset directories via environment variables:

export ONESCIENCE_MODELS_DIR=/path/to/onescience/models
export ONESCIENCE_DATASETS_DIR=/path/to/onescience/datasets

Scripts look in the following locations first:

${ONESCIENCE_MODELS_DIR}/AlphaGenome/alphagenome-all-folds
${ONESCIENCE_DATASETS_DIR}/AlphaGenome

If the above environment variables are not set, the scripts fall back to the following paths under the current model package:

weight/alphagenome-all-folds
data/

Interval Inference

bash scripts/inference.sh

Equivalent Python command example:

python scripts/run_inference.py \
  --fasta_path ./data/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa \
  --model_dir ./weight/alphagenome-all-folds \
  --chromosome chr19 \
  --start 10587331 \
  --end 11635907 \
  --output_dir ./outputs

Inference results are saved to outputs/.

Variant Effect Scoring

bash scripts/run_variant.sh

To specify a VCF input:

python scripts/run_variant_scoring.py \
  --vcf_path ./data/example.vcf \
  --fasta_path ./data/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa \
  --model_dir ./weight/alphagenome-all-folds \
  --output_dir ./outputs_variant

Scoring results are saved as a CSV file.

Track Prediction Evaluation

bash scripts/run_track.sh

You can also explicitly specify data and output paths:

python scripts/run_track_prediction_eval.py \
  --model_dir ./weight/alphagenome-all-folds \
  --model_version ALL_FOLDS \
  --data_dir ./data/v1/train \
  --output_path ./outputs_track/eval_results.csv

Fine-Tuning Example

python scripts/run_finetuning.py \
  --fasta_path ./data/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa \
  --regions_csv ./data/finetune_regions.csv \
  --bigwig_paths ./data/sample_atac.bw \
  --output_dir ./finetuned_model \
  --num_steps 1000 \
  --batch_size 2

Data Format

It is recommended to download the Hugging Face dataset OneScience-Sugon/alphagenome_dataset to data/ under the model package. The default directory structure is as follows:

data/
  reference/
    HOMO_SAPIENS/
      GRCh38.p13.genome.fa
      GRCh38.p13.genome.fa.fai
  v1/
    train/
      ...

In this structure:

reference/HOMO_SAPIENS/GRCh38.p13.genome.fa is the human reference genome FASTA.
.fai is the FASTA index file.
v1/train/ is the data directory used for track prediction evaluation.
Custom fine-tuning also requires an interval CSV file with column names chromosome,start,end, as well as one or more BigWig signal files.

OneScience Official Information

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation & License

This repository is adapted from the open-source AlphaGenome model to support DCUs. The source code is licensed under Apache License 2.0.
For scientific use, please cite the original paper: Advancing regulatory variant effect prediction with AlphaGenome.