Skill · em Dados, IA e pesquisa

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed…

Procedência

Origem: davila7/claude-code-templates
Caminho: cli-tool/components/skills/scientific/arboreto
Versão fixada: 57f899e5394bb8ca166f38eacae8f0853cbfe033
Licença: MIT
Espelhado em 25/09/2026
nenhum download no Claude Code Templates (lido em 25/09/2026)

Antes de instalar

5 arquivos · 24,2 KB · inclui 1 script que executa: scripts/basic_grn_inference.py

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/arboreto/.

d=".claude/skills/arboreto"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/arboreto"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/algorithms.md" "$u/references/algorithms.md" \
  -o "$d/references/basic_inference.md" "$u/references/basic_inference.md" \
  -o "$d/references/distributed_computing.md" "$u/references/distributed_computing.md" \
  -o "$d/scripts/basic_grn_inference.py" "$u/scripts/basic_grn_inference.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/arboreto/.

d="$HOME/.claude/skills/arboreto"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/arboreto"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/algorithms.md" "$u/references/algorithms.md" \
  -o "$d/references/basic_inference.md" "$u/references/basic_inference.md" \
  -o "$d/references/distributed_computing.md" "$u/references/distributed_computing.md" \
  -o "$d/scripts/basic_grn_inference.py" "$u/scripts/basic_grn_inference.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/arboreto/.

d=".agents/skills/arboreto"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/arboreto"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/algorithms.md" "$u/references/algorithms.md" \
  -o "$d/references/basic_inference.md" "$u/references/basic_inference.md" \
  -o "$d/references/distributed_computing.md" "$u/references/distributed_computing.md" \
  -o "$d/scripts/basic_grn_inference.py" "$u/scripts/basic_grn_inference.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/arboreto/.

d="$HOME/.agents/skills/arboreto"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/arboreto"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/algorithms.md" "$u/references/algorithms.md" \
  -o "$d/references/basic_inference.md" "$u/references/basic_inference.md" \
  -o "$d/references/distributed_computing.md" "$u/references/distributed_computing.md" \
  -o "$d/scripts/basic_grn_inference.py" "$u/scripts/basic_grn_inference.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/arboreto/.

d=".agents/skills/arboreto"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/arboreto"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/algorithms.md" "$u/references/algorithms.md" \
  -o "$d/references/basic_inference.md" "$u/references/basic_inference.md" \
  -o "$d/references/distributed_computing.md" "$u/references/distributed_computing.md" \
  -o "$d/scripts/basic_grn_inference.py" "$u/scripts/basic_grn_inference.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/arboreto/.

d="$HOME/.gemini/antigravity-cli/skills/arboreto"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/arboreto"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/algorithms.md" "$u/references/algorithms.md" \
  -o "$d/references/basic_inference.md" "$u/references/basic_inference.md" \
  -o "$d/references/distributed_computing.md" "$u/references/distributed_computing.md" \
  -o "$d/scripts/basic_grn_inference.py" "$u/scripts/basic_grn_inference.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.arboreto

Prévia do SKILL.md

---
name: arboreto
description: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation…
---

# Arboreto

## Overview

Arboreto is a computational library for inferring gene regulatory networks (GRNs) from gene expression data using parallelized algorithms that scale from single machines to multi-node clusters.

**Core capability**: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).

## Quick Start

Install arboreto:
```bash
uv pip install arboreto
```

Basic GRN inference:
```python
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)
```

**Critical**: Always use `if __name__ == '__main__':` guard because Dask spawns new processes.

## Core Capabilities
…

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