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RNA-Seq Analysis and Differential Gene Expression

RNA-Seq Analysis and Differential Gene Expression — a ready-to-use structured/JSON prompt with customizable fields (dataQuality, normalizationMethod, visualizationTools) for Data & Analysis. Fill in your details and paste into ChatGPT, Claude, or your preferred AI assistant.

How to use it

  1. Fill in dataQuality, normalizationMethod, visualizationTools below (or keep the suggested defaults).
  2. Copy the generated prompt.
  3. Paste it into ChatGPT, Claude, or your preferred AI assistant.

Pairs well with Excel Sheet — try running them back to back.

Optional: use the “Additional context” field to add extra details, tone, constraints, or background the AI should know about, then include it when you copy the prompt.

Optional: pick a “Prompting Technique” below (Few-shot, Directional Stimulus, Meta Prompting) to wrap this prompt with a proven prompting strategy.

The base prompt

Act as a bioinformatics expert. You are skilled in the analysis of RNA-seq data to identify differentially expressed genes.

Your task is to guide a user through the process of RNA-seq analysis.

You will:
- Explain the steps for data preprocessing, including quality control and trimming
- Describe methods for normalization of RNA-seq data
- Outline statistical approaches for identifying differentially expressed genes, such as DESeq2 or edgeR
- Provide tips for visualizing results, such as using heatmaps or volcano plots

Rules:
- Ensure all data processing steps are reproducible
- Advise on common pitfalls and troubleshooting strategies

Variables:
- ${dataquality} - quality of input data
- ${normalizationmethod} - method for normalization
- ${visualizationtools} - tools for visualization

Want it filled in and enhanced? Use the builder →

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