skills/tooluniverse-molecular-cloning/SKILL.md
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.
npx skillsauth add mims-harvard/tooluniverse tooluniverse-molecular-cloningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
| 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.
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
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
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.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.
tooluniverse-primer-design (and BLAST for specificity).tooluniverse-primer-design — design the PCR primers (with homology arms / Type IIS tails) to make the fragments.tooluniverse-sequence-analysis — handle the input sequences.tools
PCR / qPCR primer and oligo design — design forward/reverse primers for a target region (SantaLucia nearest-neighbor thermodynamics), compute melting temperature (Tm) and annealing temperature (Ta), check GC content, and screen an oligo for hairpins and primer-dimers. Use when you need primers for a sequence, want to QC an existing primer pair, or need the Tm of an oligo. Covers the primer-design rules (Tm matching, GC clamp, 3'-end, length) and the tools' constraint quirks.
tools
Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment fitting. Use when you have plasma/serum drug concentrations over time after a dose and need PK parameters, or to compute bioavailability from IV + oral AUCs. NOT for ADMET property prediction from structure (use tooluniverse-admet-prediction).
tools
Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use when you have results from MULTIPLE studies and need a single pooled estimate, or to synthesize evidence from a systematic review / multiple GWAS / replicated experiments. Handles the error-prone effect-size + standard-error preparation (converting OR/HR/CI, two-group means±SD, proportions, and correlations into the (effect, SE) the pooling step needs).
tools
Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.