arboreto

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

1|2|Updated Apr 29, 2026

Search Tags:#distributed-computing #single-cell-rna-seq #grnboost2 #genie3 #gene-regulatory-networks #expression-data

Ready to run

npx skills add https://github.com/fuzzy-dynamics/strings --skill arboreto-fuzzy-dynamics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/fuzzy-dynamics/strings/tree/main/packages/skills/arboreto
Command: npx skills add https://github.com/fuzzy-dynamics/strings --skill arboreto-fuzzy-dynamics

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto enables researchers to infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2 and GENIE3), revealing transcription factor–target relationships in large datasets.

Core Features & Use Cases

Scalable GRN inference for single-cell and bulk RNA-seq data using GRNBoost2 (default) or GENIE3
Support for distributed computing with Dask to handle large datasets
TF filtering and integration with pySCENIC workflows for regulon analysis
Reproducible pipelines with clear input/output formats and examples
Easy integration with Python ecosystems for downstream analyses

Quick Start

Install Arboreto, prepare an expression matrix, and run grnboost2 to infer a regulatory network.

Frequently Asked Questions about 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 expression data?▼

To infer gene regulatory networks from single-cell RNA-seq data, you can use GRNBoost2 or GENIE3 algorithms. You need to provide an expression matrix of observations by genes and optionally a list of transcription factor names to filter the network inference.

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

Yes, gene regulatory network inference can be scaled using distributed computing with Dask. This allows you to process large-scale bulk or single-cell RNA-seq datasets by moving from local multicore workstations to distributed clusters.

What is the difference between GRNBoost2 and GENIE3 for transcription factor-target analysis?▼

GRNBoost2 and GENIE3 are both algorithms used to infer gene regulatory networks from expression data. GRNBoost2 is the default method used for scalable network inference, while GENIE3 serves as an alternative algorithm option within the same pipeline.

Do I need a specific Python environment to run gene regulatory network inference?▼

Yes, you need a Python environment with Arboreto and its dependencies installed, including pandas. The environment must support Dask if you plan to use distributed computing for large-scale network inference.

How does gene regulatory network inference integrate with downstream regulon analysis?▼

Gene regulatory network inference integrates directly with pySCENIC workflows for regulon analysis. The inferred transcription factor-target relationships output by GRNBoost2 can be used as input for downstream regulon identification and analysis.

What input data format is required to infer gene regulatory networks from expression data?▼

Inferring gene regulatory networks requires a matrix of expression data formatted as observations by genes. You can optionally provide a list of transcription factor names to include in the network inference process.