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arboreto

Infer gene regulatory networks from expression data using GRNBoost2 or GENIE3.

Updated Aug 23, 2026
Search Tags:#arboreto#gene-regulatory-network#distributed-computing#grn#grnboost2#genie3
One-click install
npx skills add https://github.com/Org-GAgent/result-interpreter --skill arboreto-org-gagent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/Org-GAgent/result-interpreter/tree/main/.skills/scientific-skills/arboreto
Command: npx skills add https://github.com/Org-GAgent/result-interpreter --skill arboreto-org-gagent

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto helps researchers infer gene regulatory networks from gene expression data using scalable algorithms, enabling analysis from small experiments to large-scale studies.

Core Features & Use Cases

  • Scalable GRN inference with GRNBoost2 and GENIE3 on bulk or single-cell RNA-seq data
  • Distributed computing support via Dask for large datasets
  • Output is a TF-target-importance table suitable for downstream regulon analysis

Quick Start

Provide your expression matrix to Arboreto to infer a gene regulatory network and export the results as a TF-target file.

Frequently Asked Questions about arboreto

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?▼

To infer a gene regulatory network from expression data, provide a gene expression matrix as a pandas DataFrame or NumPy array along with an optional transcription factor list to run GRNBoost2 or GENIE3 algorithms.

Can I use distributed computing for large-scale gene regulatory network inference?▼

Yes, gene regulatory network inference supports distributed computing via a Dask cluster, allowing you to scale GRNBoost2 and GENIE3 computations across large bulk or single-cell RNA-seq datasets.

What is the difference between GRNBoost2 and GENIE3 for identifying transcription factor–target relationships?▼

Both GRNBoost2 and GENIE3 infer transcription factor–target gene relationships from expression matrices, outputting a TF-target-importance table suitable for downstream regulon analysis on bulk or single-cell RNA-seq data.

Do I need a Dask cluster to run gene regulatory network inference on small expression datasets?▼

No, a Dask cluster is not required for small datasets; you can run GRNBoost2 or GENIE3 locally. Distributed computing is optional and recommended for scaling to large RNA-seq studies.

What input format is required for gene regulatory network inference?▼

Gene regulatory network inference requires an expression data matrix provided as a pandas DataFrame or NumPy array, supplemented by an optional list of transcription factors to guide the network inference.

What output does gene regulatory network inference produce for downstream analysis?▼

Gene regulatory network inference outputs a TF-target-importance table representing regulatory interactions, which is directly suitable for downstream regulon analysis and identifying transcription factor–target gene relationships.

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