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arboreto

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

Updated Mar 10, 2026
Search Tags:#bioinformatics#gene regulatory network#rna-seq#transcriptomics#grn#network inference
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill arboreto-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/arboreto
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill arboreto-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, dask, scikit-learn, arboreto, 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, uncovering the intricate relationships between transcription factors and their target genes.

Core Features & Use Cases

  • GRN Inference: Utilizes scalable algorithms like GRNBoost2 and GENIE3 to identify regulatory interactions.
  • Data Analysis: Supports analysis of transcriptomics data, including bulk RNA-seq and single-cell RNA-seq.
  • Use Case: When analyzing single-cell RNA-seq data, use this Skill to identify key transcription factors that regulate specific cell-type differentiation pathways.

Quick Start

Use the arboreto skill to infer a gene regulatory network from the expression data file 'expression_data.tsv' and save the results 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 gene regulatory networks from RNA-seq data?▼

To infer gene regulatory networks from RNA-seq data, you can use scalable algorithms like GRNBoost2 and GENIE3 that identify transcription factor-target gene relationships from gene expression datasets.

What is the best way to identify transcription factors regulating cell-type differentiation in single-cell RNA-seq?▼

Identifying transcription factors in single-cell RNA-seq relies on gene regulatory network inference to map regulatory interactions, pinpointing key transcription factors driving specific cell-type differentiation pathways.

Can I use pandas and dask for large-scale transcriptomics network inference?▼

Yes, large-scale transcriptomics network inference supports distributed computation using dask and pandas for data manipulation, enabling scalable processing of extensive gene expression datasets.

Does GRNBoost2 work with scikit-learn for gene regulatory network analysis?▼

GRNBoost2 works with scikit-learn for gene regulatory network analysis, leveraging its machine learning framework alongside dask and pandas to compute regulatory interactions from expression data.

How do I process bulk RNA-seq data for transcription factor-target gene identification?▼

Processing bulk RNA-seq for transcription factor-target gene identification involves inputting expression data into network inference algorithms like GENIE3, which output a network mapping regulatory relationships.

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