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arboreto

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

18|1|Updated Dec 27, 2025
Search Tags:#bioinformatics#arboreto#gene-regulatory-network#distributed-computing#rna-seq#transcription-factor#grn
One-click install
npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill arboreto-logauaengstrom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/LogauaEngstrom/claude-scientific-skills/tree/main/scientific-skills/arboreto
Command: npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill arboreto-logauaengstrom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, arboreto, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Infers gene regulatory networks from expression data.

Core Features & Use Cases

  • Scalable GRN inference for single-cell and bulk RNA-seq data.
  • Supports GRNBoost2 and GENIE3 algorithms with optional transcription factor filtering.
  • Facilitates distributed computing with Dask for large datasets.

Quick Start

Run a GRNBoost2-based inference on your expression matrix to generate TF-target networks.

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 expression data?▼

You can infer gene regulatory networks from RNA-seq expression data by applying GRNBoost2 or GENIE3 algorithms to an expression matrix, generating transcription factor-target relationship networks.

Can I use Dask for distributed computing when inferring GRNs on large datasets?▼

Yes, you can use Dask for distributed computing to scale gene regulatory network inference across large datasets, enabling efficient TF-target discovery for both single-cell and bulk RNA-seq analyses.

What input data formats are supported for gene regulatory network inference?▼

Gene regulatory network inference accepts inputs as a Pandas DataFrame or a NumPy array, requiring gene names to identify transcription factors and targets within the expression matrix.

Does arboreto support transcription factor filtering during GRN inference?▼

Yes, arboreto supports optional transcription factor filtering during gene regulatory network inference, allowing you to restrict the analysis to specific TFs when generating target networks from expression data.

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

Both GRNBoost2 and GENIE3 algorithms infer gene regulatory networks from expression data, with GRNBoost2 offering scalable distributed execution alongside optional transcription factor filtering capabilities.

Is single-cell RNA-seq data supported for gene regulatory network inference?▼

Yes, single-cell RNA-seq data is fully supported for gene regulatory network inference, enabling scalable transcription factor-target discovery across large datasets using Pandas DataFrames or NumPy arrays.

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