lib-tiledbvcf

Name: lib-tiledbvcf
Availability: InStock
Author: biomaps-infra

Store, query, and retrieve genomic variant data using TileDB-VCF.

Updated Mar 13, 2026

Search Tags:#genomics #vcf #bcf #tiledb #variant data #population genomics

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-tiledbvcf

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lib-tiledbvcf
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-tiledbvcf
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-tiledbvcf

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides an efficient and scalable solution for storing, querying, and analyzing 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 VCF/BCF data.
Scalable Ingestion: Handles incremental addition of samples without costly merging.
Parallel Queries: Enables fast, parallel querying across genomic regions and samples.
Use Case: Analyze population genomics data by quickly querying allele frequencies for specific regions across thousands of samples stored in a TileDB-VCF dataset.

Quick Start

Install the necessary libraries using conda and activate the environment.

Frequently Asked Questions about lib-tiledbvcf

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

FAQPage Schema

How do I store and query large VCF datasets without merging files?▼

TileDB-VCF enables scalable storage and retrieval of genomic variant data by ingesting VCF and BCF files into a compressed sparse array. This allows parallel querying across genomic regions and incremental sample addition without costly file merging.

How does TileDB-VCF handle incremental sample addition for population genomics?▼

TileDB-VCF supports incremental sample addition by ingesting new VCF/BCF files directly into the existing TileDB sparse array. This eliminates costly file merging and enables scalable population genomics datasets.

Do I need Python and TileDB-Py installed to work with genomic variant data arrays?▼

Yes, working with genomic variant data arrays requires Python, TileDB-Py, and TileDB-VCF. These dependencies provide the necessary environment for compressed storage and parallel querying of VCF and BCF datasets.

What is the best way to query allele frequencies across thousands of samples?▼

The best way to query allele frequencies across thousands of samples is using TileDB-VCF's parallel querying capabilities. It enables fast retrieval of variant data from compressed storage across specified genomic regions and samples.

Can I export queried variant data from a TileDB-VCF dataset to pandas or pyarrow?▼

Yes, you can export queried variant data to pandas or pyarrow. The Skill supports export capabilities, leveraging these dependencies alongside numpy to facilitate downstream analysis of the retrieved genomic variants.