vcfclick 0.1.1

Small VCF databases. One per cohort. Embedded ClickHouse engine, embedded DuckDB annotations, MCP natural-language layer.

vcfclick

A modern VCF database for research labs and bioinformatics teams. Embedded chDB (ClickHouse engine, no server) for sample data, embedded DuckDB for reference annotations, and a natural-language query layer that turns plain English into SQL you can read.

Single binary. uv run vcfclick. No Docker, no port, no server, no Gatekeeper dialog. The headline demo runs from a clean git clone.

Status: research preview. Architecture validated against real 1000 Genomes data.

Why

Two complaints heard repeatedly in research bioinformatics:

1. "My cohort grew and bcftools | pandas stopped scaling." When you have 500+ samples, ad-hoc cohort correlation queries become painfully slow. The standard answer is "go install Hail," which is correct and operationally expensive.

2. "I can write the SQL, but I shouldn't have to type the boilerplate every time — and when it's written for me, I want to see it." Bioinformaticians don't want SQL hidden. They want it generated and visible, because trust comes from being able to read what ran.

vcfclick closes both:

• chDB (ClickHouse embedded as a library) handles cohort scale. We've measured ~963 variants/sec single-process ingest, 6% sparse compression vs dense, in-process Native query speed.
• The MCP server lets any LLM client translate plain English into the SQL underneath. The generated SQL is shown alongside the result — it's part of the answer, not a debug trace.

Architecture

┌────────────────────────────────────┐
│  Tiny web UI (separate repo)       │   English in → SQL + result out
└────────────────┬───────────────────┘
                 │
┌────────────────▼───────────────────┐
│  MCP server (Python)               │   Composes the two embedded stores
│  Tools: get_schema, run_sql,       │
│    position_for_gene, gene_at,     │
│    clinvar_lookup                  │
└────┬─────────────────────────┬─────┘
     │                         │
┌────▼──────────────┐  ┌───────▼────────────┐
│  chDB             │  │  DuckDB            │
│  (embedded)       │  │  (embedded)        │
│  sample data      │  │  reference data    │
│  - variants       │  │  - genes (RefSeq)  │
│  - genotypes      │  │  - clinvar_*       │
│  - samples        │  │                    │
│  - ingestions     │  │                    │
└───────────────────┘  └────────────────────┘

Two embedded stores, distinct purposes:

• chDB holds sample data: wide pre-declared schema for VCF 4.3 reserved + common GATK INFO/FORMAT fields, with Map(String, String) overflow for anything else. Same SQL surface, same MergeTree engines, same projections as full ClickHouse — no server. Persistent on disk under .chdb/.
• DuckDB holds reference data: RefSeq genes, ClinVar. Embedded, swappable, monthly refresh. Never touches sample data.

The MCP server composes across them at query time. Annotation lookups happen first (DuckDB), then their results parameterise the sample query (chDB). The chain of reasoning is visible in the UI.

Installation

pip install vcfclick

That installs the vcfclick CLI and the underlying engine. vcfclick itself is pure-Python; its dependencies (cyvcf2, chdb) ship as prebuilt wheels for macOS arm64 and Linux x86_64. Other platforms build from source — cyvcf2 needs htslib headers available on PATH.

Listing: https://pypi.org/project/vcfclick/.

30-second demo

A pre-built 1000 Genomes Phase 3 BRCA1 cohort (3,014 variants × 3,202 samples) ships as a release asset. Three commands from a clean machine:

pip install vcfclick

vcfclick db pull demo \
    https://github.com/nuin/vcfclick/releases/download/v0.1.0/1000g-brca1-demo.tar.gz

vcfclick db query demo \
    "SELECT count(DISTINCT (ingest_id, sample_id)) FROM genotypes
     WHERE chrom='chr17' AND pos BETWEEN 43044295 AND 43170245"

Using vcfclick on your own data

Each cohort / study / VCF lives in its own small database under ~/.vcfclick/dbs/<name>/. The vcfclick CLI manages them.

# Normalise the VCF (one-time per file)
bcftools norm -m - input.vcf.gz | bgzip > normalised.vcf.gz

# Preview which INFO/FORMAT fields will land in typed columns vs the
# overflow Maps — and what DDL would promote an overflow field to typed
vcfclick discover normalised.vcf.gz

# Create a database for this cohort
vcfclick db create my-cohort

# Ingest the VCF into it
vcfclick db ingest my-cohort normalised.vcf.gz \
    --cohort demo --ingest-id batch_a

# Inspect what's in it
vcfclick db info my-cohort

# Run SQL directly
vcfclick db query my-cohort "SELECT count() FROM variants"

# Export the whole database as Parquet (interop with DuckDB,
# Snowflake, BigQuery, Spark, Iceberg)
vcfclick db dump my-cohort --out my-cohort-export/

# Bundle a database as a single tar.gz for sharing
vcfclick db push my-cohort /path/to/my-cohort.tar.gz

# Restore from a bundle — local file or HTTPS URL
vcfclick db pull other-cohort https://example.com/other-cohort.tar.gz

# List, remove
vcfclick db list
vcfclick db rm my-cohort

Each database is a self-contained chDB session — the on-disk format is byte-identical to a full ClickHouse server. Multiple databases sit side by side; each is cheap to create, dump, share, or delete.

The ingester prints a classification of the VCF's INFO/FORMAT fields on startup — what landed in typed columns vs. the overflow Maps. That log line is the "adapts to any VCF" claim made literally visible.

Per-ingestion identity inside a database. Every row carries ingest_id. Rows are NOT merged across uploads — the same (chrom, pos, ref, alt) observed in two different VCFs is two rows, because annotations and QC origin can differ. Re-running with the same --ingest-id is idempotent (silently replaces prior rows via ReplacingMergeTree). Using a new --ingest-id appends.

Parallel ingestion is the default; pass --serial to force the single-process loader. The parallel splitter does a single-pass count of variants per 100Kb position bucket via the tabix .tbi index (~1 ms) and greedy-splits each contig into ranges of approximately equal variant count — so dense subregions (gene panels, exomes) don't leave N–1 workers idle.

Pointing the MCP server at a specific database

In your Claude Desktop / MCP-client config, set VCFCLICK_DB_NAME to the database you want the LLM to talk to:

"vcfclick": {
  "command": "/path/to/vcfclick/.venv/bin/python",
  "args": ["-m", "vcfclick_mcp.server"],
  "cwd": "/path/to/vcfclick",
  "env": {
    "PYTHONPATH": "/path/to/vcfclick",
    "VCFCLICK_DB_NAME": "my-cohort"
  }
}

Register multiple vcfclick-<dbname> entries if you want the LLM to be able to switch between cohorts in a single Claude Desktop session.

Worked example with real SQL and real outputs: see examples/brca1-cohort.md — five canonical questions against the demo bundle, the MCP tools the LLM calls for each, the SQL it generates, and verbatim chDB results.

Annotation reference store

The MCP server's annotation tools (position_for_gene, gene_at, clinvar_lookup) read from the embedded DuckDB. Two one-time loads after installing:

# Gene coordinates (GENCODE v45 — ~60 MB, ~61,000 genes).
# Required for position_for_gene / gene_at.
vcfclick annotations load

# Pathogenic / benign variant calls (NCBI ClinVar weekly release —
# ~80 MB compressed, ~3M variants). Required for clinvar_lookup.
vcfclick annotations load-clinvar

GENCODE updates yearly; ClinVar updates weekly. Re-run either command to refresh. Both default to downloading the canonical source; pass --gff or --vcf to load from a local file instead.

Layout

• schema/ — ClickHouse DDL (chDB applies it unchanged).
• storage/db.py — chDB session singleton; apply_schema() helper.
• ingest/vcf_load.py — serial cyvcf2-based ingester.
• ingest/parallel.py — multi-process variant; Parquet staging.
• ingest/_arrow.py — pyarrow schemas matching the ClickHouse tables.
• export/parquet.py — table → Parquet export CLI.
• annotations/db.py — DuckDB annotation API (gene, ClinVar).
• annotations/transcripts.py — transcript/exon/CDS API stubs (Phase 2).
• vcfclick_mcp/server.py — MCP server (chDB + DuckDB tool surface). Renamed from mcp/ so the directory does not shadow the upstream mcp Python SDK.
• data/ — VCF inputs (gitignored).

Validated against real data

Workload	Vars	Samples	Calls stored	Throughput
BRCA1 region (1000G 30x)	1,863	3,202	369,776	small-VCF baseline
10 Mb chr17 (1000G 30x) — serial	235,768	3,202	44,986,737	952 v/s
10 Mb chr17 (1000G 30x) — parallel 4 workers	235,768	3,202	44,986,737	1,983 v/s (2.1×)
10 Mb chr17 (1000G 30x) — parallel 8 workers	235,768	3,202	44,986,737	2,466 v/s (2.6×)

Parallel speedup comes from the variant-count-aware splitter — each worker gets approximately equal work regardless of where the data actually lives along the chromosome. Sparse-table compression empirically 6.2% of dense theoretical max.

TileDB-VCF comparison

End-to-end on the same 235k-variant / 3,202-sample workload, native arm64 (vcfclick) vs Rosetta-emulated linux/amd64 (TileDB-VCF Docker):

	vcfclick	TileDB-VCF
Source VCF format	joint VCF ingested directly	per-sample VCFs only ("Combined VCFs are currently not supported")
Pre-processing	none	bcftools +split + tabix × 3,202 ≈ 8+ min
Source VCF disk	114 MB	15.1 GB (132× inflation)
Ingest, best stable config	69 s (parallel-8)	~79 min projected (single-thread, multi-thread failed)
End-to-end	~1 min	~87 min

Full methodology, caveats (including the Rosetta penalty), and reproduction commands: bench/BENCHMARK.md.

License

Apache License 2.0. Full text in LICENSE; rationale in LICENSING.md.

Open work

• Phase 2: transcript / exon / CDS hierarchy + corresponding MCP tools.
• End-to-end MCP integration test with a real LLM client — the SCHEMA_DESCRIPTION prompt is theoretical until it's stress-tested.