arboreto

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

Updated May 17, 2026

Search Tags:#scikit-learn #transcriptomics #regulatory analysis #gene regulatory networks #data inference

Ready to run

npx skills add https://github.com/galeep/plugin-place --skill arboreto-galeep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-bioinformatics-genomics/skills/arboreto
Command: npx skills add https://github.com/galeep/plugin-place --skill arboreto-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dask[complete], distributed, numpy, pandas, scikit-learn, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides scalable algorithms to infer gene regulatory networks (GRNs) from gene expression data, streamlining the process for analysis in large datasets like transcriptomics data.

Core Features & Use Cases

Scalable Algorithms: Supports GRNBoost2 and GENIE3 algorithms for inferencing, designed for handling large-scale datasets.
Transcriptomics Analysis: Ideal for analyzing transcriptomics data such as bulk RNA-seq and single-cell RNA-seq to identify transcription factor-target gene relationships.
Distributed Computation: Facilitates distributed computation to manage and process large datasets efficiently.

Quick Start

Use the arboreto skill to infer gene regulatory networks from 'expression_data.tsv'.

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 data?▼

You can infer gene regulatory networks from single-cell RNA-seq data by applying scalable algorithms like GRNBoost2 and GENIE3 to your gene expression datasets.

Do I need Dask to compute gene regulatory networks for large transcriptomics datasets?▼

Yes, you need Dask and distributed libraries to facilitate distributed computation for efficiently managing and processing large-scale transcriptomics datasets.

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

Both GRNBoost2 and GENIE3 are scalable algorithms used to infer transcription factor-target gene relationships, utilizing scikit-learn and scipy for robust data inference.

Can I use scikit-learn and pandas for gene expression data inference?▼

Yes, gene expression data inference relies on scikit-learn, pandas, numpy, and scipy libraries to process inputs and compute transcription factor-target relationships.

What's the best way to handle large-scale RNA-seq data for network inference?▼

The best way to handle large-scale RNA-seq data is using distributed computation with Dask, applying GRNBoost2 or GENIE3 algorithms for scalable gene regulatory network inference.