tiledbvcf

Store and query genomic variant data from VCF files using TileDB sparse arrays.

Updated Aug 12, 2026
One-click install
npx skills add https://github.com/Mzane0803/latent-minds-skills-marketplace --skill tiledbvcf-mzane0803
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tiledbvcf
Source: https://github.com/Mzane0803/latent-minds-skills-marketplace/tree/main/plugins/genomics/skills/tiledbvcf
Command: npx skills add https://github.com/Mzane0803/latent-minds-skills-marketplace --skill tiledbvcf-mzane0803

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Working with large collections of VCF/BCF files is slow and storage-intensive, and merging multi-sample variant data for cohort studies requires expensive reprocessing every time new samples arrive. This Skill provides workflows for ingesting, querying, and exporting genomic variant data with TileDB-VCF's sparse array storage. ## Core Features & Use Cases - Incremental VCF Ingestion: Add new single-sample VCF/BCF files to an existing dataset without re-processing or merging existing data. - Parallel Region Queries: Extract variants by genomic region, sample, and attributes (GT, AD, DP, INFO/FORMAT fields) across local or cloud storage (S3, Azure, GCS). - Export and Interoperability: Export dataset subsets back to VCF/BCF or TSV for downstream tools and pipelines. - Use Case: Building a population genomics database for a GWAS cohort—ingest hundreds of single-sample VCFs, then query allele frequencies and genotypes for specific regions like chr13:32396898-32400268 across all samples in one read. ## Quick Start Create a TileDB-VCF dataset from my single-sample VCF files and query the genotypes for chromosome 1 regions across all ingested samples.

Frequently Asked Questions about tiledbvcf

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

FAQPage Schema
How do I ingest VCF files into TileDB-VCF?▼

Create a dataset with tiledbvcf.Dataset(uri, mode="w") and call ds.ingest_samples() with a list of VCF paths. Files must be single-sample VCF/BCF with index files (.csi or .tbi); multi-sample VCFs are not supported.

How to query variants by genomic region in TileDB-VCF?▼

Open the dataset in read mode and call ds.read() with regions like "chr1:1000000-2000000", sample names, and attributes such as fmt_GT. Coordinates are 1-based and inclusive, matching the VCF standard.

Does TileDB-VCF support multi-sample VCF files?▼

No, TileDB-VCF only supports single-sample VCF/BCF files for ingestion, and each file must have an index (.csi from bcftools or .tbi from tabix). Split multi-sample VCFs before ingesting.

Can TileDB-VCF read datasets from S3 or cloud storage?▼

Yes, pass a cloud URI such as s3://bucket/dataset, azure://container/dataset, or gcs://bucket/dataset when opening the dataset. Ensure proper cloud authentication credentials are configured for access.

Why does TileDB-VCF ingestion run out of memory?▼

Memory exhaustion happens when the memory budget is too low for large VCF files or too many files are ingested at once. Set an appropriate memory_budget in ReadConfig and partition large ingestions into batches.

When should I use TileDB-Cloud instead of open source TileDB-VCF?▼

Consider TileDB-Cloud when datasets exceed 1000 samples or 100GB of VCF data, when you need distributed computing, team collaboration, or enterprise security and compliance features.