bio-arboreto

Name: bio-arboreto
Availability: InStock
Author: biomaps-infra

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

Updated Mar 13, 2026

Search Tags:#bioinformatics #gene regulatory network #rna-seq #transcriptomics #grn #network inference

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-arboreto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bio-arboreto
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/bio-arboreto
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-arboreto

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires arboreto, pandas, dask, distributed, 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, which is crucial for understanding biological systems.

Core Features & Use Cases

GRN Inference: Uses scalable algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target gene relationships.
Data Analysis: Supports bulk and single-cell RNA-seq data for transcriptomics analysis.
Distributed Computing: Scales to large datasets using Dask for parallel processing.
Use Case: Analyze single-cell RNA-seq data to discover key regulatory interactions driving cell differentiation or response to stimuli.

Quick Start

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

Frequently Asked Questions about bio-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 bulk or single-cell RNA-seq data, you can use algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target relationships from expression matrices. This approach leverages Dask for parallel processing of large datasets.

What is the best way to scale gene regulatory network inference for large transcriptomics datasets?▼

The best way to scale gene regulatory network inference for large transcriptomics datasets is to use Dask and distributed computing libraries. This enables efficient parallel processing during network construction from high-dimensional gene expression data.

Can I use GRNBoost2 and GENIE3 for single-cell RNA-seq analysis?▼

Yes, you can use GRNBoost2 and GENIE3 for single-cell RNA-seq analysis to discover key regulatory interactions driving cell differentiation. These algorithms process transcriptomics data to construct gene regulatory networks from single-cell expression profiles.

Does gene regulatory network inference require pandas and Dask dependencies?▼

Yes, gene regulatory network inference requires pandas, Dask, and distributed libraries. These dependencies are necessary for efficient parallel processing, data manipulation, and scalable network construction across large transcriptomics datasets.

Why use Dask distributed computing for transcriptomics network inference?▼

Dask distributed computing is used for transcriptomics network inference to handle large-scale gene expression datasets efficiently. It enables parallel processing, reducing the computational time required to infer complex gene regulatory networks.