skills.rest
swaruplab
swaruplabCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

arboreto

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

94|11|Updated Mar 26, 2026
Search Tags:#arboreto#gene-regulatory-network#distributed-computing#rna-seq#grn#grnboost2#genie3
One-click install
npx skills add https://github.com/swaruplab/operon --skill arboreto-swaruplab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/arboreto
Command: npx skills add https://github.com/swaruplab/operon --skill arboreto-swaruplab

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto enables scalable inference of gene regulatory networks from expression data, allowing researchers to uncover regulatory relationships between transcription factors and target genes across large datasets.

Core Features & Use Cases

  • Scalable GRN inference using GRNBoost2 and GENIE3 for both single-cell and bulk RNA-seq data.
  • Distributable computing support via Dask, enabling execution on local clusters or cloud-based resources.
  • TF filtering & integration allows restricting regulators via a TF list and exporting TF-target networks for downstream analysis.

Quick Start

Run basic GRN inference on your expression matrix to generate a regulator-target network.

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 generate a table of transcription factors, target genes, and importance scores.

Can I use distributed computing for large-scale GRN inference?▼

Yes, gene regulatory network inference supports distributed computing via Dask, allowing you to execute scalable network calculations across local clusters or cloud-based resources.

Does this GRN inference workflow work with both bulk and single-cell RNA-seq data?▼

Yes, the workflow accepts Pandas or NumPy expression matrices to perform scalable gene regulatory network inference for both single-cell and bulk RNA-seq datasets.

How do I restrict gene regulatory network inference to specific transcription factors?▼

You can restrict gene regulatory network inference by providing an optional transcription factor list, which filters the regulators to produce a targeted TF-target network.

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

Both GRNBoost2 and GENIE3 infer gene regulatory networks from expression data to output TF-target-importance tables, but GRNBoost2 is specifically designed to support distributed computing for scalability.

Related Skills

Explore More Data & Analytics Skills→
jasrajtulsijasrajtulsi

arboreto

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

Community
Intermediate
crazymsncrazymsn
22

arboreto

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

Community
Advanced
biomaps-infrabiomaps-infra

bio-arboreto

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

Official
Advanced
qinyan-aiqinyan-ai
48

arboreto

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

Community
Advanced