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arboreto

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

557|98|Updated Nov 7, 2025
Search Tags:#bioinformatics#gene regulatory network#rna-seq#transcriptomics#grn#network inference
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill arboreto-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/arboreto
Command: npx skills add https://github.com/jimmc414/Kosmos --skill arboreto-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the complex process of inferring gene regulatory networks (GRNs) from gene expression data, helping researchers understand gene interactions and biological pathways.

Core Features & Use Cases

  • GRN Inference: Uses scalable algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target gene relationships.
  • Data Analysis: Supports analysis of transcriptomics data (RNA-seq) for both single-cell and bulk samples.
  • Distributed Computing: Scales computation from single machines to multi-node clusters for large datasets.
  • Use Case: A biologist has single-cell RNA-seq data and wants to understand how specific transcription factors control gene expression in different cell types. They can use this Skill to generate a network highlighting these regulatory interactions.

Quick Start

Use the arboreto skill to infer a gene regulatory network from the expression data in 'expression_data.tsv' and save the output to 'network.tsv'.

Frequently Asked Questions about arboreto

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

FAQPage Schema
How do I infer a gene regulatory network from RNA-seq data?▼

To infer a gene regulatory network from RNA-seq data, use scalable algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target gene relationships from your expression data.

What algorithms are used for GRN inference from single-cell transcriptomics data?▼

GRN inference from single-cell transcriptomics data utilizes algorithms such as GRNBoost2 and GENIE3 to analyze expression data and map transcription factor-target gene relationships across different cell types.

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

Yes, gene regulatory network inference supports distributed computing, allowing you to scale computation from single machines to multi-node clusters to handle large-scale transcriptomics datasets.

What is the best way to identify transcription factor-target gene relationships in bulk RNA-seq?▼

The best way to identify transcription factor-target gene relationships in bulk RNA-seq is by applying network inference algorithms like GRNBoost2 or GENIE3 to your gene expression data.

Do I need pandas and dask to run gene regulatory network inference?▼

Yes, gene regulatory network inference requires dependencies including pandas, dask, and scipy, which provide the data structures and distributed computing framework necessary for processing expression data.

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