AlphaGenome API

from dr7_alphagenome import AlphaGenome

client = AlphaGenome()

# Load sequence from FASTA file
results = client.predict_from_fasta(
    "input.fa",
    modalities=["CAGE", "ATAC", "DNase", "H3K27ac"],
    output_format="bigwig"
)

# Results are saved as BigWig files for visualization
for track in results.tracks:
    print(f"Saved: {track.output_path}")

from dr7_alphagenome import AlphaGenome

client = AlphaGenome()

# Score a single variant
effect = client.score_variant(
    chromosome="chr17",
    position=7674220,  # Near TP53 gene
    ref="G",
    alt="A",
    window_size=100000  # Context around variant
)

print(f"Overall effect score: {effect.score:.4f}")
print(f"Gene expression change: {effect.expression_delta:.4f}")
print("Top affected cell types:")
for ct in effect.top_cell_types[:5]:
    print(f"  {ct.name}: {ct.score:.4f}")

from dr7_alphagenome import AlphaGenome

client = AlphaGenome()

# Score variants from VCF file
results = client.score_vcf(
    "variants.vcf",
    reference="hg38",
    output_file="scored_variants.tsv",
    include_tracks=["CAGE", "enhancer_activity"],
    parallel=True
)

print(f"Scored {results.total_variants} variants")
print(f"High-impact variants: {results.high_impact_count}")

# Filter for regulatory variants
regulatory = results.filter(
    min_score=0.5,
    affected_elements=["enhancer", "promoter"]
)
regulatory.to_dataframe().to_csv("regulatory_variants.csv")