tiledbvcf

Manage genomic variant data with TileDB-VCF for storage, querying, and export.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/yf8578/clawomics --skill tiledbvcf-yf8578
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tiledbvcf
Source: https://github.com/yf8578/clawomics/tree/main/skills/tiledbvcf
Command: npx skills add https://github.com/yf8578/clawomics --skill tiledbvcf-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tiledb-py, tiledbvcf-py, pandas, pyarrow, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides an efficient and scalable solution for storing, querying, and exporting large genomic variant datasets, overcoming the limitations of traditional VCF/BCF file handling.

Core Features & Use Cases

  • Efficient Storage: Utilizes TileDB's sparse array technology for compressed and optimized storage of variant data.
  • Scalable Ingestion: Handles large VCF/BCF files and supports incremental addition of new samples.
  • High-Performance Querying: Enables rapid querying of specific genomic regions and samples.
  • Data Export: Allows exporting subsets of data in standard VCF or TSV formats.
  • Use Case: Analyze allele frequencies across thousands of samples for specific genomic regions in a large cohort study, or quickly retrieve all variants for a particular gene from a multi-terabyte VCF dataset.

Quick Start

Use the tiledbvcf skill to create a new TileDB-VCF dataset named 'my_dataset' and ingest the samples from 'sample1.vcf.gz' and 'sample2.vcf.gz'.

Frequently Asked Questions about tiledbvcf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I store and query large VCF datasets efficiently?

You can store and query large VCF datasets efficiently by using TileDB-VCF, which leverages sparse array technology for compressed storage and enables rapid, parallel querying of specific genomic regions and samples.

Can I incrementally add new samples to an existing TileDB-VCF dataset?

Yes, TileDB-VCF supports scalable ingestion and incremental addition of new samples to an existing dataset, facilitating population genomics workflows and large-scale cohort studies.

What is the best way to export genomic variant data from a TileDB array?

The best way to export genomic variant data from a TileDB array is using the TileDB-VCF Python library, which allows exporting subsets of data in standard VCF or TSV formats.

Do I need specific Python libraries to manage BCF files with TileDB-VCF?

Yes, managing BCF files with TileDB-VCF requires the tiledb-py and tiledbvcf-py Python libraries for data manipulation, alongside pandas, pyarrow, and numpy for processing.

How does TileDB handle variant data for cohort studies compared to traditional VCF files?

TileDB-VCF overcomes traditional VCF file limitations by using sparse array technology for optimized storage, allowing scalable ingestion and high-performance querying across thousands of samples in cohort studies.

Are there limitations when querying multi-terabyte VCF datasets with TileDB?

TileDB-VCF is designed to handle multi-terabyte VCF datasets efficiently through parallel querying, though performance depends on proper configuration of the sparse array and available compute resources.