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grn-tools

Infer gene regulatory networks from transcriptomics data using GRNBoost2 and GENIE3.

126|8|Updated Mar 10, 2026
Search Tags:#bioinformatics#gene regulatory network#arboreto#transcriptomics#grn#grnboost2#genie3
One-click install
npx skills add https://github.com/DrugClaw/DrugClaw --skill grn-tools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grn-tools
Source: https://github.com/DrugClaw/DrugClaw/tree/main/skills/genomics/grn-tools
Command: npx skills add https://github.com/DrugClaw/DrugClaw --skill grn-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, arboreto, distributed, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the inference of gene regulatory networks (GRNs) from gene expression data, helping researchers understand complex biological interactions.

Core Features & Use Cases

  • GRN Inference: Build GRNs using algorithms like GRNBoost2 and GENIE3.
  • TF-Target Link Identification: Identify potential transcription factor (TF) to target gene relationships.
  • Network Filtering: Restrict inference to a curated TF list and filter edges by importance.
  • Use Case: Analyze RNA-seq data to discover which transcription factors regulate a specific set of genes involved in a disease pathway.

Quick Start

Run the GRN inference script using the provided expression matrix and save the network to a TSV file.

Frequently Asked Questions about grn-tools

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

FAQPage Schema
How do I infer a gene regulatory network from RNA-seq expression data?▼

Yes, you can build gene regulatory networks from single-cell expression matrices using this Skill, which applies Arboreto-based algorithms like GRNBoost2 and GENIE3 to identify and score transcription factor-target links.

Do I need a specific Python environment to run GRNBoost2 and GENIE3?▼

You need Python 3 with the pandas and arboreto libraries installed to run the GRN inference. Optional distributed computing support is available through the distributed library for handling larger datasets.

Can I restrict gene regulatory network inference to a curated list of transcription factors?▼

Yes, you can restrict gene regulatory network inference by providing a curated transcription factor list, which limits the algorithm to only evaluate potential TF-target relationships for those specific factors.

What is the difference between GRNBoost2 and GENIE3 for identifying TF-target links?▼

Both GRNBoost2 and GENIE3 are tree-based methods integrated via Arboreto to infer TF-target links and score regulatory edges from transcriptomics data. GRNBoost2 is generally optimized for faster computation on large expression matrices.

How do I filter gene regulatory network edges by importance?▼

After generating the network, you can filter regulatory edges by their importance scores assigned by the GRNBoost2 or GENIE3 algorithms, allowing you to retain only the most significant transcription factor-target relationships.

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