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arboreto

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

21|2|Updated Dec 8, 2025
Search Tags:#dask#transcriptomics#single-cell rna-seq#bulk rna-seq#grn inference#gene regulation#tf-target networks
One-click install
npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill arboreto-silverstein
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/silverstein/claude-scientific-skills-desktop/tree/main/corpus/arboreto
Command: npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill arboreto-silverstein

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

Arboreto helps you infer gene regulatory networks (GRNs) by identifying likely transcription factor (TF) to target gene relationships from gene expression measurements.

Core Features & Use Cases

  • GRN inference from expression matrices: Compute TF–target regulatory links with importance scores using GRNBoost2 or GENIE3.
  • TF-restricted network construction: Limit regulators to a provided transcription factor list to improve interpretability and runtime.
  • Scalable distributed computation: Use Dask (local multicore or remote clusters) to handle large bulk RNA-seq or single-cell RNA-seq datasets.

Quick Start

Run GRNBoost2 on your expression TSV by providing a transcription factor list and saving the resulting TF-target-importance table.

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 bulk RNA-seq or single-cell RNA-seq data?▼

Gene regulatory network inference from RNA-seq expression matrices is performed by computing transcription factor to target gene importance scores using GRNBoost2 or GENIE3 algorithms, outputting a TF-target-importance table.

Do I need to provide a transcription factor list to restrict network construction?▼

Providing a transcription factor list is optional but recommended to restrict network construction, which improves interpretability of the resulting TF-target network and reduces runtime.

Can I use Dask for scalable gene regulatory network inference on large datasets?▼

Yes, Dask supports scalable gene regulatory network inference by enabling local multicore or remote cluster execution to handle large bulk RNA-seq or single-cell RNA-seq datasets efficiently.

What is the difference between GRNBoost2 and GENIE3 for TF-target network inference?▼

GRNBoost2 and GENIE3 are both supported algorithms for TF-target network inference that compute importance scores from expression data, with GRNBoost2 often preferred for Dask integration and GENIE3 providing a standard baseline.

How do I ensure reproducibility when inferring gene regulatory networks?▼

You can ensure reproducibility in gene regulatory network inference by setting a deterministic random seed when running GRNBoost2 or GENIE3 on your expression matrix.

Does this gene regulation inference support multi-condition comparisons?▼

Yes, the gene regulatory network inference workflow supports multi-condition comparisons, allowing you to analyze TF-target networks across different experimental states from your expression data.

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