hvantk:resource-msigdb

Convert MSigDB GMT files into structured Hail Tables for genomic analysis.

Updated Feb 2, 2024

Search Tags:#bioinformatics #genomics #gene-sets #multiomics #msigdb #hail

Ready to run

npx skills add https://github.com/bigbio/hvantk --skill hvantk-resource-msigdb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hvantk:resource-msigdb
Source: https://github.com/bigbio/hvantk/tree/main/hvantk/skills/msigdb
Command: npx skills add https://github.com/bigbio/hvantk --skill hvantk-resource-msigdb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hail, requests, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the transformation of raw MSigDB GMT gene-set files into structured, queryable Hail Tables, eliminating manual parsing and ensuring consistency for downstream multiomics analysis.

Core Features & Use Cases

Standardized Parsing: Converts variable-width GMT files into a uniform Hail Table format keyed by gene-set name.
Integration Ready: Enables seamless joining of gene-set membership data with variant or expression datasets for enrichment and burden analyses.
Use Case: Researchers can quickly ingest the latest C2 Canonical Pathways collection to perform pathway-level enrichment testing across large-scale genomic cohorts.

Quick Start

Run the hvantk reprocess msigdb:genesets command pointing to your local directory containing the unzipped GMT file to generate the processed Hail Table.

Frequently Asked Questions about hvantk:resource-msigdb

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

FAQPage Schema

How do I convert MSigDB GMT files into Hail Tables for genomic enrichment?▼

To convert MSigDB GMT files into Hail Tables, this Skill parses variable-width gene-set files and maps set names and gene arrays into a structured, queryable distributed computing format. This standardization eliminates manual parsing and ensures schema provenance for downstream analysis.

What is the best way to parse MSigDB gene-set collections for multiomics integration?▼

The best way to parse MSigDB gene-set collections for multiomics integration is to ingest GMT files into a uniform Hail Table keyed by gene-set name. This approach creates integration-ready data structures for seamless joining with variant or expression datasets.

Do I need Hail installed to process MSigDB GMT files into queryable formats?▼

Yes, you need Hail installed to process MSigDB GMT files, as the processing logic relies entirely on it for data transformation. The Skill uses Hail to map raw gene-set collections into high-performance distributed tables for reproducible multiomics workflows.

Can I use these parsed Hail Tables for pathway-level burden analysis across large genomic cohorts?▼

Yes, you can use the parsed Hail Tables for pathway-level burden analysis across large genomic cohorts. The standardized tables enable seamless joining of gene-set membership data with large-scale variant datasets to perform enrichment testing.

How does transforming GMT files into Hail Tables improve multiomics analysis?▼

Transforming GMT files into Hail Tables improves multiomics analysis by converting raw gene-set data into a high-performance distributed format with strict schema provenance. This enables consistent, reproducible integration with variant or expression datasets for burden testing.

What are the limitations of using Hail for MSigDB gene-set processing?▼

The primary limitation of using Hail for MSigDB gene-set processing is the strict dependency on the Hail environment and the requests library for ingestion. Users must operate within this specific distributed computing framework to parse GMT files into structured tables.