COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do -- execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Data Integration Analysis

Bridge the gap between statistical results and biological understanding. After any computational analysis produces significant findings, this skill teaches how to interpret them using ToolUniverse's biological knowledge tools -- the key advantage over platforms that only do data analysis.

IMPORTANT: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Respond in the user's language.

When to Use This Skill

Apply when:

• Statistical analysis produced a list of significant genes, variants, metabolites, or exposures
• Users want to go beyond p-values to understand WHY something is significant
• Combining computational results with published evidence
• Interpreting differential expression, GWAS hits, or association study results biologically
• Users ask "what does this result mean?" after running an analysis