Dados e Análise

Comprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance. Use when annotating variants with functional and clinical information.

Create forms, validation schemas, and CRUD API endpoints. Use when building new data entry features or managing existing forms.

Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.

Consulte o CZ CELLxGENE Census (mais de 61M de células). Filtre por tipo de célula/tecido/doença, recupere dados de expressão e integre com scanpy/PyTorch para análise de célula única em escala populacional.

Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.

Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.

Use this skill when you need to structure data in `srs/data` for the Next.js app

Call structural variants (SVs) from short-read sequencing using Manta, Delly, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations that are too large for standard SNV callers. Use when detecting structural variants from short-read data.

Blend multiple Kaggle submission CSVs via weighted average (regression) or rank average (classification/ranking). Use when the user says "blend submissions", "ensemble these CSVs", "rank average", "weighted blend", or "combine my models".

When the user wants to create, optimize, or audit migration guides for users switching from competitors. Also use when the user mentions "migration guide," "migrate from X," "switch to [product]," "import from X," or "data migration." For rebrand and redirects, use rebranding-strategy.

MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.

Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data.