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arboreto

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

3|Updated Apr 17, 2026
Search Tags:#single-cell RNA-seq#gene regulatory network#RNA-seq#GENIE3#GRNBoost2
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill arboreto-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/arboreto
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill arboreto-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dask, numpy, pandas, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Analyzing gene expression data and inferring complex gene regulatory networks is a challenging task, often requiring significant expertise and computational resources.

Core Features & Use Cases

  • GRN Inference: Infer gene regulatory networks from bulk RNA-seq and single-cell RNA-seq data using GRNBoost2 and GENIE3 algorithms.
  • Scalable: Handle large-scale datasets with distributed computation capabilities.
  • Distributed Computing: Run on single machines or clusters, adapting to available resources.
  • Use Case: If you have a single-cell RNA-seq dataset and need to infer gene regulatory networks, this skill is perfect for you.

Quick Start

Use the 'arboreto' skill to infer the gene regulatory network from the RNA-seq data 'single_cell_data.rna_seq'.

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?▼

To infer gene regulatory networks from RNA-seq data, this Skill automates the process using GRNBoost2 and GENIE3 algorithms. It is specifically built to handle both bulk and single-cell RNA-seq datasets.

Does gene regulatory network inference support large-scale datasets?▼

Yes, gene regulatory network inference supports large-scale datasets through distributed computation. You can run the analysis on single machines or clusters, adapting to available resources using dask.

What is the best way to run GRNBoost2 and GENIE3 for RNA-seq analysis?▼

The best way to run GRNBoost2 and GENIE3 for RNA-seq analysis is using this Skill, which automates the algorithms for inferring gene regulatory networks. It handles the distributed computation required for scalable processing.

Do I need dask and scipy to infer gene regulatory networks from RNA-seq data?▼

Yes, you need dask, numpy, pandas, and scipy to infer gene regulatory networks from RNA-seq data. These dependencies are required for distributed computation and data processing during network inference.

Can I use distributed computing for gene regulatory network inference on a cluster?▼

Yes, you can use distributed computing for gene regulatory network inference on a cluster. The Skill adapts to available resources, allowing you to run the GRNBoost2 and GENIE3 algorithms across single machines or clusters.

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