Skill UI

PrimeKG is a comprehensive knowledge graph integrating over 20 primary databases to map complex biological relationships. It allows users to query multiscale data, including genes, drugs, diseases, and phenotypes. Ideal for drug discovery, identifying repurposing opportunities, and analyzing deep disease contexts in network pharmacology.

A comprehensive guide to implementing production-grade reliability patterns for Snowflake environments. Covers advanced techniques like Time Travel for point-in-time recovery, cross-region database replication, account failover groups for disaster recovery, and application-level resilience coding (e.g., connection failover logic). Essential for building fault-tolerant, highly available data pipelines.

This skill performs advanced pathway and gene-set enrichment analysis crucial for interpreting gene lists (e.g., DE genes, screen hits). It distinguishes between Over-Representation Analysis (ORA) for thresholded lists and Gene Set Enrichment Analysis (GSEA) for full ranked lists. Supports scoring pathway activity (ssGSEA/GSVA) and utilizes major databases like GO, KEGG, Reactome, and MSigDB for comprehensive biological interpretation, ensuring publication-ready results.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.