skills.rest
AlterLab-IEU
AlterLab-IEUCOMMUNITY
Explore all skills by this author →
GitHub

alterlab-arboreto

Infer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.

58|9|Updated Mar 16, 2026
Search Tags:#arboreto#gene-regulatory-network#distributed-computing#single-cell-rna-seq#grnboost2#genie3#bulk-rna-seq
One-click install
npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-arboreto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alterlab-arboreto
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/bioinformatics/alterlab-arboreto
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-arboreto

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto enables scalable inference of gene regulatory networks (GRNs) from gene expression data using parallelized algorithms (GRNBoost2, GENIE3) to identify regulatory relationships between transcription factors and target genes.

Core Features & Use Cases

  • Scale: supports large datasets from bulk and single-cell RNA-seq
  • Algorithms: GRNBoost2 (recommended) and GENIE3 for validation
  • Distributed compute: leverages Dask for local or cluster execution
  • Output: returns TF–target–importance triplets suitable for downstream analyses

Quick Start

Install Arboreto and run GRN inference on your expression matrix to generate a regulatory network.

Frequently Asked Questions about alterlab-arboreto

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

FAQPage Schema
How do I infer gene regulatory networks from single-cell RNA-seq data?▼

To infer gene regulatory networks from single-cell RNA-seq data, you can use Arboreto's GRNBoost2 algorithm to process your pandas DataFrame expression matrix and identify transcription factor–target interactions.

What is the difference between GRNBoost2 and GENIE3 for regulatory network inference?▼

GRNBoost2 and GENIE3 are both tree-based algorithms for regulatory network inference, but GRNBoost2 is the recommended method for scalability, while GENIE3 is typically used for result validation.

Can I use Dask for distributed computing when building large-scale gene regulatory networks?▼

Yes, you can use Dask for distributed computing to build large-scale gene regulatory networks, enabling local or cluster execution to handle extensive bulk and single-cell RNA-seq datasets efficiently.

What input format is required to identify transcription factor and target gene interactions?▼

Identifying transcription factor and target gene interactions requires pandas-compatible inputs, such as a gene expression DataFrame or NumPy array, along with an optional list of transcription factors.

What output format does Arboreto generate for downstream regulatory network analysis?▼

Arboreto generates output as TF–target–importance triplets, providing structured data that represents transcription factor–target relationships and their regulatory importance for downstream analyses.

Related Skills

Explore More Data & Analytics Skills→
Hung-3008Hung-3008
1

arboreto

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

Community
Advanced
JosephWoodallJosephWoodall
1

arboreto

Infer gene regulatory networks from RNA-seq data with GRNBoost2 and GENIE3.

Community
Advanced
RamanEbrahimiRamanEbrahimi
3

arboreto

Infer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.

Community
Advanced
sanand0sanand0
8

arboreto

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

Community
Intermediate