Tiledbvcf

TileDB-VCF

Scalable genomic variant storage and retrieval using TileDB arrays. Handles population-scale VCF/BCF datasets with parallel queries and cloud storage support (S3, Azure Blob, GCS).

Installation

# Preferred: conda (Python < 3.10 required for full feature set)
conda install -c conda-forge -c tiledb tiledb-vcf

# Docker (recommended for reproducibility)
docker pull tiledb/tiledb-vcf:latest

# pip (limited functionality)
pip install tiledb-vcf

Core Operations

Create a TileDB-VCF Dataset

import tiledbvcf

# Create new dataset
ds = tiledbvcf.Dataset("path/to/my_variants.tdb", mode="w")
ds.create()

# CLI: Create dataset
tiledbvcf create --uri s3://my-bucket/variants/

Ingest VCF/BCF Files

# Python
ds = tiledbvcf.Dataset("path/to/my_variants.tdb", mode="w")
ds.ingest_samples(
    sample_uris=["sample1.vcf.gz", "sample2.bcf", "sample3.vcf.gz"],
    scratch_space_path="/tmp/tiledbvcf_scratch",  # Temp space for sorting
    contigs_prefix_mappings={"chr": ""}  # Strip 'chr' prefix if needed
)

# CLI: Ingest
tiledbvcf store \
    --uri s3://my-bucket/variants/ \
    --samples sample1.vcf.gz sample2.vcf.gz sample3.vcf.gz \
    --scratch-space /tmp/scratch

# Incremental: add more samples later
tiledbvcf store \
    --uri s3://my-bucket/variants/ \
    --samples new_sample.vcf.gz

Query Variants

# Read specific genomic regions
ds = tiledbvcf.Dataset("path/to/my_variants.tdb", mode="r")

# Query by region
df = ds.read(
    attrs=["sample_name", "contig", "pos_start", "pos_end",
           "alleles", "info_DP", "fmt_GT", "fmt_GQ"],
    regions=["chr1:1-100000", "chr7:117559590-117559600"]  # CFTR region
)

# Filter to specific samples
df = ds.read(
    attrs=["sample_name", "pos_start", "alleles", "fmt_GT"],
    regions=["chr17:7571720-7590868"],  # TP53
    samples=["SAMPLE_001", "SAMPLE_002", "SAMPLE_003"]
)

# Export to pandas DataFrame (automatic)
print(df.head())
print(f"Found {len(df)} variant records")

# CLI: Query to VCF
tiledbvcf export \
    --uri s3://my-bucket/variants/ \
    --regions chr1:1-100000 \
    --output-path results/

Parallel Queries

# TileDB-VCF automatically parallelizes across threads
import tiledbvcf

ds = tiledbvcf.Dataset("s3://my-bucket/variants/", mode="r")

# Large cohort query - parallelized internally
df = ds.read(
    attrs=["sample_name", "contig", "pos_start", "alleles", "fmt_GT", "fmt_AF"],
    regions=["chr1:1-248956422"],  # Whole chromosome 1
    samples=None  # All samples
)

# Control parallelism
cfg = tiledbvcf.ReadConfig(
    memory_budget_mb=4096,  # 4GB memory budget
    thread_task_size_mb=64  # Per-thread chunk size
)
df = ds.read(attrs=..., regions=..., config=cfg)

Cloud Storage

import tiledb
import tiledbvcf

# Configure S3 access
tiledb.Config({
    "vfs.s3.aws_access_key_id": "YOUR_KEY",
    "vfs.s3.aws_secret_access_key": "YOUR_SECRET",
    "vfs.s3.region": "us-east-1"
})

# Or use IAM roles (recommended for EC2/ECS)
# Just use s3:// URIs directly

ds = tiledbvcf.Dataset("s3://my-bucket/variants/cohort.tdb", mode="r")
df = ds.read(
    attrs=["sample_name", "pos_start", "alleles"],
    regions=["chr22:1-51304566"]
)

Key Attributes Available

Docker Workflow

# Pull and run
docker run -v /data:/data tiledb/tiledb-vcf:latest \
    tiledbvcf store \
    --uri /data/variants.tdb \
    --samples /data/cohort/*.vcf.gz

# Interactive Python
docker run -it -v /data:/data tiledb/tiledb-vcf:latest python3
>>> import tiledbvcf
>>> ds = tiledbvcf.Dataset("/data/variants.tdb", mode="r")

Downstream Analysis Integration

# With pandas
df = ds.read(attrs=["sample_name", "pos_start", "alleles", "fmt_AF"],
             regions=["chr7:117000000-118000000"])

# With Dask for distributed computation
import dask
ddf = ds.read_dask(attrs=..., regions=...)

# Export to Parquet for downstream tools
df.to_parquet("variants_chr7.parquet")

# Integration with GWAS tools
import pandas as pd
gwas_df = df.pivot_table(index="pos_start", columns="sample_name", values="fmt_GT")

Performance Notes

• Ingestion: ~500 samples/hour on single machine; scale horizontally
• Query: Parallel across regions and samples; 10-100x faster than tabix on large cohorts
• Storage: ~3-5x compression vs gzipped VCF for typical germline data
• Incremental: Add new samples without reprocessing existing data
• Cloud-native: S3/Azure/GCS without downloading data

Use Cases

• Population genomics cohorts (UK Biobank, gnomAD scale)
• Rare variant discovery across thousands of samples
• Variant annotation pipelines requiring fast region queries
• Multi-omics integration with genomic coordinates