Alphagenome Single Variant Analysis

# AlphaGenome API Reference

Pip package: `alphagenome`

## Setup and Imports

Standard imports for AlphaGenome workflows:

```python
from alphagenome.data import gene_annotation
from alphagenome.data import genome
from alphagenome.data import track_data
from alphagenome.data import transcript as transcript_utils
from alphagenome.interpretation import ism
from alphagenome.models import dna_client
from alphagenome.models import variant_scorers
from alphagenome.visualization import plot_components
import matplotlib.pyplot as plt
import pandas as pd
```

### Client Initialization

The API key is automatically loaded by `dotenv` from the
`.env` file in the agent configuration dir.

To initialize a client:

```python
from alphagenome.models import dna_client

dna_model = dna_client.create(
    api_key=os.environ.get('ALPHAGENOME_API_KEY'),
    address='dns:///gdmscience.googleapis.com:443',
)
```

## Core Data Types

### genome.Interval

0-based half-open interval (includes `start`, excludes `end`).

```python
interval = genome.Interval(chromosome='chr1', start=1_000, end=1_010)

interval.center()     # Returns center position (int)
interval.width        # Returns 10
interval.resize(100)  # Resizes around center
interval.overlaps(other_interval)
interval.contains(other_interval)
interval.intersect(other_interval)
```

### genome.Variant

Position is **1-based** (VCF-compatible).

```python
variant = genome.Variant(
    chromosome='chr22',
    position=36201698,  # 1-based!
    reference_bases='A',
    alternate_bases='C',
)
# Get interval around variant
interval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)
```

## Predictions

### Predict from DNA Sequence

```python
output = dna_model.predict_sequence(
    sequence='GATTACA'.center(dna_client.SEQUENCE_LENGTH_1MB, 'N'),
    requested_outputs=[dna_client.OutputType.DNASE],
    ontology_terms=['UBERON:0002048'],  # Lung
)
# Access predictions
print(output.dnase.values.shape)  # (sequence_length, num_tracks)
print(output.dnase.metadata)      # Track metadata DataFrame
```

### Predict from Genome Interval

```python
interval = genome.Interval('chr1', 1000000, 1000001)
interval = interval.resize(dna_client.SEQUENCE_LENGTH_1MB)

output = dna_model.predict_interval(
    interval=interval,
    requested_outputs=[dna_client.OutputType.RNA_SEQ],
    ontology_terms=['UBERON:0001114'],  # Right liver lobe
)
```

### Mouse Predictions

Specify `organism=dna_client.Organism.MUS_MUSCULUS` for mouse models.

```python
output = dna_model.predict_sequence(
    ...,
    organism=dna_client.Organism.MUS_MUSCULUS,
)
```

## Variant Analysis

### Predict Variant Effects (Raw Tracks)

Compare predictions for Reference (REF) vs Alternate (ALT) alleles.

```python
variant_output = dna_model.predict_variant(
    interval=interval,
    variant=variant,
    requested_outputs=[dna_client.OutputType.RNA_SEQ],
    ontology_terms=['UBERON:0001157'],  # Colon - Transverse
)

ref_tracks = variant_output.reference.rna_seq
alt_tracks = variant_output.alternate.rna_seq
```

### Score Variants (Aggregated Scores)

Get aggregated scores using recommended scorers.

```python
scorer = variant_scorers.RECOMMENDED_VARIANT_SCORERS['RNA_SEQ']

variant_scores_list = dna_model.score_variant(
    interval=interval,
    variant=variant,
    variant_scorers=[scorer],
)
scores = variant_scores_list[0]

# Tidy scores to DataFrame
df = variant_scorers.tidy_scores([scores], match_gene_strand=True)
print(df[['gene_symbol', 'raw_score', 'quantile_score']])
```

**Available recommended scorers:** `ATAC`, `CAGE`, `DNASE`, `PROCAP`, `RNA_SEQ`,
`CHIP_TF`, `CHIP_HISTONE`, `SPLICE_SITES`, `SPLICE_SITE_USAGE`,
`SPLICE_JUNCTIONS`, `POLYADENYLATION`, `CONTACT_MAPS`

### Batch Variant Scoring

```python
# Parse variants from VCF-like DataFrame
for _, row in vcf_df.iterrows():
    variant = genome.Variant(
        chromosome=str(row.CHROM),
        position=int(row.POS),
        reference_bases=row.REF,
        alternate_ba