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Prompt course · 8 lessons · 22 prompts · 1 hour · Beginner

AI for Bioinformaticians

Learn to use AI for pipeline errors, code, file formats, statistics, analysis plans, QC, figures, and clear write-ups. Each lesson uses real bioinformatics tasks.

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What you'll learn

  • Decode Errors: Turn a cryptic tool error into a plain list of likely causes and next checks.
  • Fix Code: Get help writing, correcting, or translating Python, R, and shell snippets for data tasks.
  • Handle Formats: Parse, filter, and convert SAM, VCF, GFF, and other common biological files.
  • Choose Statistics: Ask for plain explanations of tests and help matching a method to your question.
  • Plan Analyses: Build a workflow skeleton and tool list from a biological question.
  • Read QC Results: Summarize QC reports and differential expression results for yourself or collaborators.
  • Make Figures: Pick a plot and get code for clear figures ready for review or publication.
  • Write Clearly: Draft methods text, pipeline notes, and paper summaries in a useful first pass.

What's inside

8 lessons · 22 prompts
  1. Before you start · framework course RACE Prompt Framework: Role, Action, Context, ExpectationRACE helps bioinformaticians define role, action, context, and expected quality when analyzing RNA-seq data for differential expression.
  2. Start here Priya's Thursday, Two WaysA day in the life of a Bioinformatician, before and after these prompts.
  3. 01 Lesson 1 · 3 prompts Troubleshoot Pipeline Errors
  4. 02 Lesson 2 · 3 prompts Write and Debug Code
  5. 03 Lesson 3 · 3 prompts Handle Biological File Formats
  6. 04 Lesson 4 · 2 prompts Understand Methods and Statistics
  7. 05 Lesson 5 · 3 prompts Plan Analyses and Pipelines
  8. 06 Lesson 6 · 3 prompts Interpret QC and Results
  9. 07 Lesson 7 · 2 prompts Create Data Visualizations
  10. 08 Lesson 8 · 3 prompts Document and Communicate

About this course

7 topics

Use AI Well in Bioinformatics Work

This course teaches you how to ask an AI tool for help with the work you already do. You will practice with errors, code, file formats, statistics, and communication.

The lessons build on each other. You start with fixing a broken run, then move to writing code, reading formats, planning analyses, and sharing what you find. ChatGPT, Claude, or Gemini can all work for these prompts.

  1. The lessons
    1. Troubleshoot Pipeline Errors: Use AI to decode cryptic tool errors, trace failures in logs, and fix broken commands so you can get your pipeline running again.
    2. Write and Debug Code: Get help writing, fixing, and translating Python, R, and shell code for common bioinformatics data tasks.
    3. Handle Biological File Formats: Use AI to parse, filter, and convert SAM, VCF, GFF, and other biological file formats with confidence.
    4. Understand Methods and Statistics: Get plain-English explanations of statistical methods and help choosing the right test for your data.
    5. Plan Analyses and Pipelines: Turn a biological question into a structured analysis plan, workflow skeleton, and tool selection.
    6. Interpret QC and Results: Use AI to read QC reports, summarize differential expression results, and explain findings to collaborators.
    7. Create Data Visualizations: Choose the right plot for your data and get code to produce clear, publication-ready figures.
    8. Document and Communicate: Draft methods sections, pipeline documentation, and paper summaries to share your work clearly.
  2. What the Course Covers

    Eight lessons walk through common bioinformatics moments. You will use AI to troubleshoot pipeline errors, write and debug code, handle biological file formats, understand methods and statistics, plan analyses, read QC and results, create data visualizations, and document your work.

    Every lesson uses prompts you can copy and adapt. The focus is on practical help, not theory.

  3. How the Lessons Connect

    The order matters. Troubleshooting errors comes first because a broken pipeline blocks everything else. Code and file formats follow because they are the daily tools.

    Later lessons move into statistics, analysis plans, QC, figures, and writing. By the end, you have a full loop from raw data to shared results.

  4. How to Use Prompts Well

    Give the AI the exact error, the command, and the file type. Say what you already tried and what you expected to happen.

    Treat its answer like a draft from a helpful colleague. Run the command, check the output, and keep your own notes about what worked.

  5. Who This Course Is For

    Bioinformaticians who run pipelines, write scripts, or review results will get the most from it. You do not need to be a senior developer.

    If you work with genomic, proteomic, or other biological data and want a faster first pass, this course fits.

  6. Safety and Privacy

    Biological data can be sensitive, especially human genomic data. Follow your institution's rules before pasting anything into an AI tool.

    Use public data, de-identified data, or small made-up examples when you practice. Keep patient identifiers, sample IDs, and unpublished results out of public tools unless your workplace allows it.

  7. Your Next Step

    Pick one task from your week that feels slow or frustrating. Open ChatGPT, Claude, or Gemini and try the matching prompt from this course.

    Save what worked in a small prompt library. Then bring that habit to the next pipeline, figure, or methods section.