Gene Regulatory Networks

Gene Regulatory Networks

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially arboreto-like and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for regulatory network inference, regulon scoring, perturbation-aware comparison, and network visualization.

When To Use This Skill

• use when the task is GRN inference or regulon-level interpretation
• use when the data include expression matrices and optionally chromatin features or TF priors
• use when the user needs network-level summaries rather than only gene lists

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• expression matrix
• optional accessibility data
• TF prior resources

Expected Outputs

• inferred networks
• regulon activity tables
• network visualizations

Preferred Tools

• arboreto-like GRN utilities
• networkx
• pandas
• seaborn

Starter Pattern

Preferred starting point: arboreto-like
Inputs: expression matrix, optional accessibility data, TF prior resources
Outputs: inferred networks, regulon activity tables, network visualizations

Workflow

1. Choose the evidence model

Clarify whether inference is coexpression-based, prior-constrained, or multimodal.

2. Infer or score networks

Run network inference or regulon-scoring methods appropriate to the data type.

3. Compare across states

Summarize regulators and network changes across conditions, perturbations, or branches.

4. Visualize selectively

Plot subnetworks or regulator-centric views rather than full unreadable graphs.

5. Export confidence-aware outputs

Store edge weights, regulator scores, and evidence annotations.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• inferred networks
• regulon activity tables
• network visualizations

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Check assay-specific QC such as enrichment quality, coverage behavior, or replicate consistency.
• Verify genome build, interval coordinates, and annotation compatibility.

Anti-Patterns

• presenting inferred networks as validated causal circuitry
• plotting whole dense networks without summarization
• mixing inference evidence types without labeling them

Related Skills

• ATAC Seq
• ChIP Seq
• Methylation Analysis
• Epitranscriptomics

Optional Supplements

• arboreto