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arboreto

Infer gene regulatory networks from gene expression data with Dask.

Updated Dec 17, 2025
Search Tags:#bioinformatics#genomics#Dask#gene regulatory networks#GRN inference#RNA-seq analysis
One-click install
npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill arboreto-robotlearning123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/robotlearning123/claude-scientific-skills/tree/main/scientific-skills/arboreto
Command: npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill arboreto-robotlearning123

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto addresses the challenge of accurately and efficiently inferring gene regulatory networks from large-scale gene expression datasets.

Core Features & Use Cases

  • Scalable GRN Inference: Supports distributed computing and scales from single machines to multi-node clusters.
  • Algorithm Selection: Offers GRNBoost2 and GENIE3 algorithms, tailored for different datasets and requirements.
  • Transcription Factor Filtering: Allows focusing inference on specific transcription factors.
  • Use Case: Utilize Arboreto to analyze RNA-seq data, pinpointing transcription factor-target gene relationships and regulatory interactions.

Quick Start

Install Arboreto:

uv pip install arboreto

Run a basic GRN inference:

import pandas as pd
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # Load expression data (genes as columns)
    expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')

    # Infer regulatory network
    network = grnboost2(expression_data=expression_matrix)

    # Save results (TF, target, importance)
    network.to_csv('network.tsv', sep='\t', index=False, header=False)

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 data by loading an expression matrix into pandas and applying the grnboost2 algorithm to calculate transcription factor-target gene importance scores.

What is the best way to scale GRN inference for large genomics datasets?▼

Scaling GRN inference for large genomics datasets is handled using Dask and distributed computing, allowing the network inference process to run efficiently from a single machine to a multi-node cluster.

Can I use Arboreto to filter specific transcription factors during network inference?▼

Yes, Arboreto supports transcription factor filtering during network inference, allowing you to focus the calculation specifically on regulatory interactions involving a predefined list of transcription factors.

What algorithms are available for gene regulatory network inference in Python?▼

Available algorithms for gene regulatory network inference include GRNBoost2 and GENIE3, which are tailored for analyzing different gene expression datasets and specific bioinformatics requirements.

Do I need Dask installed to run gene regulatory network inference?▼

Dask and distributed are required dependencies to enable parallelized computation, but the core network inference can operate on standard pandas DataFrames loaded from TSV files.

What format should expression data be in for gene regulatory network analysis?▼

Expression data should be formatted as a TSV file with genes as columns, loaded into a pandas DataFrame where the network inference algorithms process the expression matrix directly.

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