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4516781 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | # Copyright 2026 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""AlphaGenome inference example script.
This script demonstrates how to use AlphaGenome for multimodal prediction on genomic intervals, including:
- Gene expression (RNA-seq, CAGE)
- Chromatin accessibility (ATAC-seq, DNase-seq)
- Transcription factor binding (ChIP-seq)
- Hi-C contact maps
- Splice sites
Usage:
# Automatically download the model using Kaggle Hub (default mode)
python run_inference.py
# Specify a local reference genome
python run_inference.py \
--fasta_path /path/to/GRCh38.fa \
--chromosome chr19 \
--start 10587331 \
--end 11635907 \
--output_dir ./outputs
"""
import pathlib
import sys
_PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[1]
_SRC_DIR = _PROJECT_ROOT / "src"
if str(_SRC_DIR) not in sys.path:
sys.path.insert(0, str(_SRC_DIR))
from absl import app
from absl import flags
from absl import logging
from alphagenome._sdk.data import genome
from alphagenome.model import dna_model as dna_model_types
import numpy as np
from alphagenome.model.dna_model import (
create,
create_from_kaggle,
OrganismSettings,
)
FLAGS = flags.FLAGS
flags.DEFINE_string(
'fasta_path',
None,
'Reference genome FASTA path. A .fai index is required.',
)
flags.DEFINE_string(
'model_dir',
None,
'Local AlphaGenome checkpoint directory. If unset, Kaggle Hub is used.',
)
flags.DEFINE_string('chromosome', 'chr1', 'Chromosome name, for example chr1.')
flags.DEFINE_integer('start', 1_000_000, 'Interval start, 0-based.')
flags.DEFINE_integer('end', 2_048_576, 'Interval end, exclusive.')
flags.DEFINE_string('output_dir', './outputs', 'Output directory.')
flags.DEFINE_enum(
'organism', 'HOMO_SAPIENS',
['HOMO_SAPIENS', 'MUS_MUSCULUS'],
'Target organism.',
)
flags.DEFINE_enum(
'model_version', 'FOLD_0',
['FOLD_0', 'FOLD_1', 'FOLD_2', 'FOLD_3', 'FOLD_4', 'all_folds'],
'Model version, fold, or all_folds ensemble.',
)
def main(_):
output_dir = pathlib.Path(FLAGS.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
organism = dna_model_types.Organism[FLAGS.organism]
model_version = FLAGS.model_version
interval = genome.Interval(
chromosome=FLAGS.chromosome,
start=FLAGS.start,
end=FLAGS.end,
)
logging.info('Loading model weights...')
if FLAGS.model_dir:
organism_settings = None
if FLAGS.fasta_path:
organism_settings = {
organism: OrganismSettings(
fasta_path=FLAGS.fasta_path,
),
}
alphagenome_model = create(
checkpoint_path=FLAGS.model_dir,
organism_settings=organism_settings,
)
else:
alphagenome_model = create_from_kaggle(model_version)
logging.info('Running inference: %s', interval)
if FLAGS.fasta_path or not FLAGS.model_dir:
predictions = alphagenome_model.predict_interval(
interval,
organism=organism,
requested_outputs={
dna_model_types.OutputType.ATAC,
dna_model_types.OutputType.DNASE,
dna_model_types.OutputType.CAGE,
dna_model_types.OutputType.RNA_SEQ,
dna_model_types.OutputType.CHIP_TF,
dna_model_types.OutputType.CHIP_HISTONE,
},
ontology_terms=None,
)
else:
logging.warning(
'No fasta_path was provided; using a random sequence for demo.'
)
rng = np.random.default_rng(42)
bases = np.array(['A', 'C', 'G', 'T'])
seq_len = interval.end - interval.start
dna_sequence = ''.join(rng.choice(bases, size=seq_len))
predictions = alphagenome_model.predict_sequence(
dna_sequence,
organism=organism,
requested_outputs={
dna_model_types.OutputType.ATAC,
dna_model_types.OutputType.DNASE,
dna_model_types.OutputType.RNA_SEQ,
},
ontology_terms=None,
interval=interval,
)
logging.info('Saving predictions to %s', output_dir)
for attr_name in ['atac', 'dnase', 'cage', 'rna_seq', 'chip_tf',
'chip_histone', 'contact_maps', 'procap']:
output = getattr(predictions, attr_name, None)
if output is not None:
output_path = output_dir / f'{attr_name}.npy'
np.save(output_path, np.array(output.values))
logging.info(' saved %s: shape=%s', attr_name,
np.array(output.values).shape)
logging.info('Inference finished.')
if __name__ == '__main__':
app.run(main) |