Does this skill support multi-sample VCFs?

No, TileDB-VCF requires single-sample VCFs for ingestion. However, it organizes them into a unified dataset that allows multi-sample querying.

How does it handle memory management?

The skill provides patterns for configuring a memory budget to optimize performance and prevent system exhaustion during large-scale data ingestions or queries.

Can I use TileDB-VCF with cloud storage?

Yes, it supports URI-based access for AWS S3, Azure Blob Storage, and Google Cloud Storage, making it ideal for cloud-native genomics pipelines.

TileDB-VCF is a high-performance library built on TileDB sparse arrays designed for the efficient storage and retrieval of genomic variant-call data (VCF/BCF).

Which coordinate system does this skill use?

It follows the VCF standard using 1-based genomic coordinates where ranges are inclusive on both ends (e.g., chr1:1000-2000 includes 1001 bases).

TileDB-VCF Genomic Storage

Name: TileDB-VCF Genomic Storage
Author: Yezez9

byYezez9

0•

数据科学与机器学习

Manages high-performance storage and retrieval of genomic variant data using TileDB's sparse array technology.

This skill provides specialized guidance for using TileDB-VCF, a C++ library with Python interfaces designed for scalable population genomics. It helps users manage large-scale VCF/BCF datasets by enabling incremental sample ingestion without costly merge operations, facilitating efficient parallel queries across genomic regions, and integrating seamlessly with cloud storage providers like AWS, Azure, and GCP. Whether prototyping local pipelines or building production-grade variant databases, this skill ensures best practices for memory management, coordinate systems, and data export for downstream bioinformatics analysis.

主要功能

01Lossless data export to standard VCF and TSV formats

02Scalable ingestion of VCF/BCF files into compressed sparse arrays

03Incremental sample addition without re-processing existing datasets

04Native integration with AWS S3, Google Cloud Storage, and Azure Blob Storage

050 GitHub stars

06High-performance parallel querying across genomic regions and samples

使用场景

01Preparing and filtering datasets for Genome-Wide Association Studies (GWAS)

02Building large-scale population genomics databases for cohort studies

03Querying specific genomic regions across thousands of samples stored in the cloud

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yezez9/research-agent tiledbvcf

For use in Claude.ai and ChatGPT