tiledbvcf

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

13|3|Updated Jun 10, 2026

Search Tags:#data management #genomics #population genomics #TileDB-VCF #genomic variant data

Ready to run

npx skills add https://github.com/tassiovale/claude-code-kit --skill tiledbvcf-tassiovale

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tiledbvcf
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/tiledbvcf
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill tiledbvcf-tassiovale

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 simplifies the storage, retrieval, and analysis of genomic variant data, making it easier to manage and process large-scale genomic datasets.

Core Features & Use Cases

Scalable Storage: Store and retrieve VCF/BCF files with TileDB's sparse array technology for efficient handling of large datasets.
Incremental Sample Addition: Add new samples without reprocessing existing data, suitable for evolving datasets.
Efficient Queries: Perform high-performance queries across genomic regions and samples.
Data Export: Export subsets of data in various formats for downstream analysis.
Use Case: Use this Skill to build a variant database for a cohort study, allowing for efficient querying and analysis of variant data.

Quick Start

Install TileDB-VCF and ingest your first VCF file into a new dataset.

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-scale genomic variant data efficiently?▼

Store and query large-scale genomic variant data efficiently using TileDB-VCF, which uses sparse array technology to handle VCF/BCF files and enables high-performance queries across genomic regions and samples.

Can I add new samples to a variant database without reprocessing existing data?▼

Yes, you can add new samples to a variant database without reprocessing existing data. TileDB-VCF supports incremental sample addition, making it suitable for evolving datasets and population genomics cohort studies.

What is the best way to manage VCF files for a population genomics pipeline?▼

Manage VCF files for a population genomics pipeline using TileDB-VCF for scalable storage, incremental sample addition, efficient querying, and data export in various formats for downstream analysis.

Do I need Python and specific packages to ingest VCF files into TileDB-VCF?▼

Yes, you need Python and specific packages to ingest VCF files into TileDB-VCF. The environment requires tiledb-py, tiledbvcf-py, pandas, pyarrow, and numpy to manage and process genomic variant data.

Does TileDB-VCF work with NumPy and Pandas for genomic data analysis?▼

Yes, TileDB-VCF works with NumPy and Pandas for genomic data analysis. The implementation depends on tiledb-py, tiledbvcf-py, pandas, pyarrow, and numpy to manage variant data and export subsets for downstream analysis.

How do I export subsets of variant data for downstream analysis?▼

Export subsets of variant data for downstream analysis using TileDB-VCF's data export feature, which allows you to retrieve queried genomic variants from the sparse array storage in various formats.