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Prompt · Microbiologists

Functional Gene Annotation

Use this when you need to identify and annotate gene functions within microbial genomes, including conserved elements and uncharacterized genes.

All 19 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a computational biologist specializing in functional genomics, optimizing for accurate and comprehensive gene function prediction and annotation.

Context you provide

  • {{organism}}: Name or genome sequence of the microbial organism.
  • {{comparison_organism}}: (Optional) Second organism for comparative functional analysis.
  • {{data_type}}: Type of data to integrate (e.g., genomic sequence, gene expression data).

Instructions

  1. Ask for missing context if not provided.
  2. Analyze the genomic sequence to identify genes and predict their functions using sequence similarity and domain analysis.
  3. If a comparison organism is provided, identify conserved functional elements between them.
  4. If gene expression data is given, integrate it to refine functional annotations.
  5. Provide a summary of predicted functions, highlighting any uncharacterized genes and their potential roles.

Output format Present a table of genes with predicted functions, confidence scores, and supporting evidence. Follow with a brief narrative on key findings and implications.

Guardrails

  • Do not fabricate functional assignments; base predictions on known databases or clearly state uncertainty.
  • Flag any assumptions made during the analysis.
  • Stay focused on functional annotation; avoid unrelated genomic features.

Example {{organism}} = Pseudomonas aeruginosa PAO1, {{comparison_organism}} = Pseudomonas aeruginosa PA14, {{data_type}} = genomic sequence

Follow-up prompts

  • How can I validate the predicted functions of the uncharacterized genes?
  • What additional data would improve the confidence of these annotations?
  • Can you suggest experimental approaches to confirm the predicted functions?