tooluniverse-molecular-cloning

$npx mdskill add mims-harvard/ToolUniverse/tooluniverse-molecular-cloning

Design overlaps or overhangs for Gibson or Golden Gate assembly.

  • Plan how to join DNA fragments into a construct seamlessly.
  • Depends on sequence data and user-specified fragment boundaries.
  • Selects method based on fragment count, reusability, and scar tolerance.
  • Outputs overlap sequences, overhang designs, and domestication instructions.

SKILL.md

.github/skills/tooluniverse-molecular-cloningView on GitHub ↗
---
name: tooluniverse-molecular-cloning
description: Molecular cloning assembly design — Gibson Assembly (overlap design for seamless multi-fragment joining) and Golden Gate Assembly (Type IIS / BsaI / BbsI design with unique 4-bp fusion overhangs). Use when you need to plan how to join DNA fragments into a construct, design assembly overlaps/overhangs, or decide between cloning methods. Covers the domestication (internal-site removal), overhang-uniqueness, and overlap-Tm rules. For PCR primers to generate the fragments, see tooluniverse-primer-design.
disable-model-invocation: true
---

# Molecular Cloning Assembly Design (Gibson & Golden Gate)

Plan how to join DNA fragments into a construct: design the **overlaps** (Gibson) or **Type IIS overhangs** (Golden Gate) and avoid the failures that come from internal sites and non-unique junctions.

## Step 0 — Pick the method

| Use **Gibson Assembly** when | Use **Golden Gate** when |
|---|---|
| A few fragments, **scarless/seamless** junctions anywhere you choose | Many parts, **standardized reusable** parts (MoClo/modular), one-pot |
| You can add ~20–40 bp homology by PCR | You can remove internal BsaI/BbsI sites (domestication) |
| One-off constructs | Combinatorial libraries / repeated assemblies |

Both are sequence-independent (no scar at the junction for Gibson; a 4-bp fusion scar for Golden Gate). For 2–4 unique fragments, Gibson is usually simplest; for libraries or a parts toolkit, Golden Gate.

## Step 1 — Gibson Assembly

```bash
tu run DNA_gibson_design '{"operation":"gibson_design",
  "fragments":["ATGGCG...GAGGAC","GAGGAC...GGCAAG","GGGCAAG...ATCCT"],
  "overlap_length":20}'
```

For each fragment it returns `left_overlap`, `right_overlap`, and `with_overlaps` (the fragment extended with the homology arms you'd add to your PCR primers — hand these to `tooluniverse-primer-design`).

**Gibson design rules**
- **Overlap length 15–40 bp** (20–25 typical); longer for GC-poor junctions.
- **Overlap Tm ≈ 48–65 °C** and balanced between junctions.
- **Fragment order matters** — list fragments in assembly order; the last fragment's 3′ overlaps the first only if you're making a circle (vector).
- **Avoid repeats/secondary structure** at the junctions (hairpins, direct repeats) → misassembly.
- **Unique junctions** — if two junctions share homology, fragments can swap; redesign so each overlap is unique.

## Step 2 — Golden Gate Assembly

```bash
tu run DNA_golden_gate_design '{"operation":"golden_gate_design",
  "parts":["ATGGCG...AAGAAC","CTGAGC...CTGATC","GAGGAG...GTGGTG"],
  "enzyme":"BsaI"}'
```

Returns `parts_with_overhangs`: each part's unique 4-bp `left_overhang`/`right_overhang` and the `full_sequence` flanked by the Type IIS recognition sites (e.g. BsaI `GGTCTC(N1)` … cutting outside its site to leave the 4-bp fusion overhang).

**Golden Gate design rules**
- **Domestication is mandatory.** The chosen enzyme's site (BsaI `GGTCTC`, BbsI `GAAGAC`) must NOT occur **inside** any part, or it will be cut internally. Remove internal sites by silent mutation before assembly — check every part.
- **Overhangs must be unique and non-palindromic.** Each 4-bp fusion site must differ from the others and not equal its own reverse complement, or junctions misligate. The tool assigns unique non-palindromic overhangs; keep them.
- **Avoid high-GC or all-AT overhangs**; published high-fidelity overhang sets (e.g. Potapov 2018) ligate most cleanly.
- **Order is encoded by the overhangs**, not by listing order — the 4-bp junctions define assembly.

## Step 3 — QC before ordering

`scripts/cloning_qc.py` screens parts for the problems above: internal BsaI/BbsI sites (Golden Gate), overhang uniqueness/palindromes, and Gibson overlap GC/length — and flags PASS/WARN.

## Step 4 — Gotchas (state these)

- **Internal Type IIS sites** (Golden Gate) — the #1 failure; domesticate every part.
- **Non-unique Gibson overlaps** or shared homology → fragment swapping / misassembly.
- **Repeats and strong secondary structure** at junctions reduce efficiency in both methods.
- **Overlap Tm imbalance** (Gibson) → some junctions form, others don't.
- **Generating the fragments** still needs primers with the overlaps/overhangs appended — design and QC those in `tooluniverse-primer-design` (and BLAST for specificity).

## Honest limitations

- These tools design the assembly junctions; they do not simulate the full ligation/exonuclease reaction or guarantee efficiency — validate by sequencing the assembled construct.
- No vector-backbone or ORF-frame checking — confirm reading frame and backbone compatibility yourself.

## Related skills
- `tooluniverse-primer-design` — design the PCR primers (with homology arms / Type IIS tails) to make the fragments.
- `tooluniverse-sequence-analysis` — handle the input sequences.

More from mims-harvard/ToolUniverse

SkillDescription
create-tooluniverse-skillCreate high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology.
devtu-auto-discover-apisAutomatically discover life science APIs online, create ToolUniverse tools, validate them, and prepare integration PRs. Performs gap analysis to identify missing tool categories, web searches for APIs, automated tool creation using devtu-create-tool patterns, validation with devtu-fix-tool, and git workflow management. Use when expanding ToolUniverse coverage, adding new API integrations, or systematically discovering scientific resources.
devtu-benchmark-harnessContinuous improvement system for ToolUniverse tools, skills, and plugin. Run benchmarks, diagnose failures, route fixes to devtu skills, retest. Use after skill optimization, tool additions, or as regression check.
devtu-code-optimization>
devtu-create-toolCreate new scientific tools for ToolUniverse framework with proper structure, validation, and testing. Use when users need to add tools to ToolUniverse, implement new API integrations, create tool wrappers for scientific databases/services, expand ToolUniverse capabilities, or follow ToolUniverse contribution guidelines. Supports creating tool classes, JSON configurations, validation, error handling, and test examples.
devtu-docs-qualityTOP PRIORITY skill — find and immediately fix or remove every piece of wrong, outdated, or redundant information in ToolUniverse docs. Wrong code, broken links, incorrect counts, and overlapping instructions must be fixed or removed — never left in place. Runs five phases: (D) static method scan, (C) live code execution, (A) automated validation, (B) ToolUniverse audit, (E) less-is-more simplification. Core philosophy: each concept appears exactly once; remove don't add; no emojis; single setup entry point. Use when reviewing docs, before releases, after API changes, or when asked to audit, fix, or simplify documentation.
devtu-fix-toolFix failing ToolUniverse tools by diagnosing test failures, identifying root causes, implementing fixes, and validating solutions. Use when ToolUniverse tools fail tests, return errors, have schema validation issues, or when asked to debug or fix tools in the ToolUniverse framework.
devtu-githubGitHub workflow for ToolUniverse - push code safely by moving temp files, activating pre-commit hooks, running tests, and cleaning staged files. Use when pushing to GitHub, fixing CI failures, or cleaning up before commits.
devtu-optimize-descriptionsOptimize tool descriptions in ToolUniverse JSON configs for clarity and usability. Reviews descriptions for missing prerequisites, unexpanded abbreviations, unclear parameters, and missing usage guidance. Use when reviewing tool descriptions, improving API documentation, or when user asks to check if tools are easy to understand.
devtu-optimize-skillsOptimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.