genomic-predictionlisted

Execution blueprints and code templates for fitting genomic prediction models, including GBLUP, GFBLUP, MultiBLUP, Bayesian methods, and Random Forest using peer-reviewed software. Strictly incorporates robust self-auditing, high-clarity code commenting, big data performance safeguards, and strict matrix alignment rules.
scicrow/genomic-prediction-skill · ★ 0 · Data & Documents · score 62

Install: claude install-skill scicrow/genomic-prediction-skill

# Genomic Prediction Blueprints This skill provides execution blueprints and templates for implementing established genomic prediction models using official software packages. Always use these validated implementations rather than writing custom approximation scripts. ## 1. Core Operating Principles - **No Workarounds**: Never attempt to build custom approximations, pseudo-code fallbacks, or shortcuts to bypass execution errors. - **No Silent Fallbacks**: If required input variables/metadata are missing, explicitly flag the missing data, abort, and report. Do not use pseudo-valid inputs. - **Robust Self-Auditing**: Critically analyze pipelines to ensure alignment with scientific rigor. Use official methods; flag custom pseudo-implementations. ## 2. Environment and Data Prerequisites - **R Packages**: `qgg`, `rrBLUP`, `BGLR`, `randomForest` or `ranger`. - **Stand-alone Executables**: `FAANG/BayesRCO`, `LDAK`. - **Data Formats**: Phenotypes in standard vectors/dataframes. Genotypes as numeric matrices (e.g., 0, 1, 2) or standard formats (PLINK bed/bim/fam). - **Data Transfer Architecture**: Always transfer genomic dataset pathways using binary files (e.g., binary streams via R's `readBin`/Python's `np.tofile`, `.rds` objects, HDF5, or PLINK formats) and index-level metadata. NEVER serialize large biological data matrices into text formats like JSON or XML for inter-process communication. ## 3. High-Clarity Structured Code Commenting - **Structured Section Banners**: ALWAYS