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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/alphagenome_dataset

AlphaGenome

Model Introduction

AlphaGenome is a DNA sequence model proposed by Google DeepMind. It can take DNA intervals up to 1 Mbp as input and predict multiple classes of genomic functional signals for 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 based on JAX / Flax and supports genomic interval inference, variant effect scoring, track evaluation, and fine-tuning examples. This model package is accompanied by the ModelScope dataset OneScience/alphagenome_dataset, which can be used for quick local verification.

Applicable Scenarios

Scenario	Description
Genomic interval prediction	Input a reference genome FASTA, chromosome, and interval coordinates, and output predicted tracks for ATAC, DNase, CAGE, RNA-seq, ChIP, and other signals
Variant effect scoring	Input a VCF or built-in example variants, compare prediction differences between reference and variant sequences, and generate a variant scoring table
Track prediction evaluation	Use validation data from the AlphaGenome dataset to calculate regression evaluation metrics for different assay bundles
Fine-tuning experiments	Use a custom reference genome, interval CSV, and BigWig signal files to verify the fine-tuning workflow
ModelScope / OneCode runtime	After downloading the model project and accompanying dataset, quickly verify script connectivity in a biology-domain runtime environment

Usage Instructions

1. OneCode Usage

You can experience intelligent one-click AI4S programming through the OneCode online environment:

Click to experience intelligent one-click AI4S programming

2. Manual Installation and Usage

Hardware Requirements

GPU or DCU runtime is recommended.
CPU can be used for import checks and small-configuration connectivity verification, but full training and inference are slow.
DCU users need to install DTK in advance. DTK 25.04.2 or later is recommended, or the OneScience-recommended version matching the current cluster.

Environment Check

NVIDIA GPU:

nvidia-smi

Hygon DCU:

hy-smi

Download the Model Package

modelscope download --model OneScience/alphagenome --local_dir ./alphagenome
cd alphagenome

Install the Runtime Environment

DCU Environment

# First activate DTK and Conda
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# Supports uv installation
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

Training and Inference Data Introduction

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

modelscope download --dataset OneScience/alphagenome_dataset --local_dir ./data

Training Weights

The repository includes weight/alphagenome-all-folds, and all scripts also support specifying model weights through --model_dir.

Prepare Weights

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

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

If running in a shared runtime environment, you can also reuse a unified directory through environment variables:

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

The scripts will preferentially read:

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

If the above environment variables are not set, the defaults under the current model package are read:

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 will be saved to outputs/.

Variant Effect Scoring

bash scripts/run_variant.sh

When specifying VCF input, you can use:

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 will be saved as CSV files.

Track Prediction Evaluation

bash scripts/run_track.sh

You can also explicitly specify the 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

The ModelScope dataset OneScience/alphagenome_dataset is recommended to be downloaded to data/ under the model package. The default structure is as follows:

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

Where:

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 preparing an interval CSV file with column names chromosome,start,end, as well as one or more BigWig signal files.

Official OneScience 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

Citations and License

This repository is based on the AlphaGenome open-source model and provides DCU adaptation. The related source code uses the Apache License 2.0.
For scientific research, cite the original paper: Advancing regulatory variant effect prediction with AlphaGenome.