# Tiledbvcf

Efficient storage and retrieval of genomic variant data using TileDB.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Engineering
- License: MIT license
- Last updated: 2026-07-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill tiledbvcf -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill tiledbvcf -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill tiledbvcf -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill tiledbvcf -g -a gemini-cli -y`

## Description

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# TileDB-VCF

## Overview

TileDB-VCF is a high-performance C++ library with Python and CLI interfaces for efficient storage and retrieval of genomic variant-call data. Built on TileDB's sparse array technology, it enables scalable ingestion of VCF/BCF files, incremental sample addition without expensive merging operations, and efficient parallel queries of variant data stored locally or in the cloud.

## When to Use This Skill

This skill should be used when:
- Learning TileDB-VCF concepts and workflows
- Prototyping genomics analyses and pipelines
- Working with small-to-medium datasets (< 1000 samples)
- Need incremental addition of new samples to existing datasets
- Require efficient querying of specific genomic regions across many samples
- Working with cloud-stored variant data (S3, Azure, GCS)
- Need to export subsets of large VCF datasets
- Building variant databases for cohort studies
- Educational projects and method development
- Performance is critical for variant data operations

## Quick Start

### Installation

**Preferred Method: Conda/Mamba**
```bash
# Enter the following two lines if you are on a M1 Mac
CONDA_SUBDIR=osx-64
conda config --env --set subdir osx-64

# Create the conda environment
conda create -n tiledb-vcf "python<3.10"
conda activate tiledb-vcf

# Mamba is a faster and more reliable alternative to conda
conda install -c conda-forge mamba

# Install TileDB-Py and TileDB-VCF, align with other useful libraries
mamba install -y -c conda-forge -c bioconda -c tiledb tiledb-py tiledbvcf-py pandas pyarrow numpy
```

**Alternative: Docker Images**
```bash
docker pull tiledb/tiledbvcf-py     # Python interface
docker pull tiledb/tiledbvcf-cli    # Command-line interface
```

### Basic Examples

**Create and populate a dataset:**
```python
import tiledbvcf

# Create a new dataset
ds = tiledbvcf.Dataset(uri="my_dataset", mode="w",
                      cfg=tiledbvcf.ReadConfig(memory_budget=1024))

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/05-生物信息与基因组学/tiledbvcf/SKILL.md