Hi C 3d Genomics

Hi-C And 3D Genomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially Hi-C 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 Hi-C and related 3D genomics analyses including compartments, loops, TADs, differential contacts, and visualization.

When To Use This Skill

• use when the task is Hi-C matrix analysis or 3D genome interpretation
• use when loops, compartments, or TADs must be called or compared
• use when contact-map figures or feature-level summaries are required

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

• Hi-C contact pairs or matrices
• genome bins
• condition metadata

Expected Outputs

• compartments
• loops and TADs
• contact maps and differential summaries

Preferred Tools

• Hi-C processing utilities
• numpy
• pandas
• matplotlib

Starter Pattern

Preferred starting point: Hi-C
Inputs: Hi-C contact pairs or matrices, genome bins, condition metadata
Outputs: compartments, loops and TADs, contact maps and differential summaries

Workflow

1. Validate matrix resolution

Choose a resolution supported by coverage and the biological question.

2. Normalize contact structure

Apply appropriate normalization before calling global or local features.

3. Call 3D features

Infer compartments, TADs, or loops with methods matched to the resolution and assay.

4. Compare conditions carefully

Quantify differences only where coverage and normalization support fair comparison.

5. Produce readable maps

Export heatmaps and feature tables with clear genome coordinates and labels.

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:
• compartments
• loops and TADs
• contact maps and differential summaries

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

• interpreting noisy low-coverage matrices at overly fine resolution
• comparing raw contacts without normalization
• mixing feature scales without stating the resolution

Related Skills

• ATAC Seq
• ChIP Seq
• Methylation Analysis
• Epitranscriptomics

Optional Supplements

• None required for the first pass.