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Project Ideas ​

These are real project ideas from participants. Pick one that sounds interesting, or bring your own - the goal is to learn Git, GitHub, and AI tools by building something you care about.

Alternative Project Ideas ​

Looking to explore something different? These ideas come from fellow participants.

Genomic Variant Effect Prediction ​

Use Google DeepMind's AlphaGenome to predict how patient variants affect gene expression, chromatin accessibility, and histone modifications.

The right starting point depends on your question. For a visual, no-code exploration, try AlphaGenome Viewer - a web app that lets you query interval predictions, compare reference vs. alternate alleles, and score variants with point-and-click controls. For custom analyses or batch processing, the official Colab notebooks let you script against the AlphaGenome SDK directly.

AlphaGenomePythonVariant Analysis

Research Data Management Dashboard ​

Build a desktop app that handles data entry, stores everything in a local database, runs automated analysis, and displays results in visual dashboards.

Perfect if you work with structured research data and want a custom solution for your lab's workflow. For a reference implementation with a very similar stack, see VarLens - an offline-first Electron + Vue + SQLite app for clinical variant analysis. You'll learn how to build cross-platform desktop apps, work with databases, and create interactive visualizations.

ElectronVue/ViteSQLiteDesktop Tools

AI-Assisted Academic Writing Pipeline ​

Combine Zotero (for managing references), QuillBot (for paraphrasing), and GPTZero (for checking AI detection) into a streamlined research writing workflow.

Learn how to chain different AI tools together and build a personal writing assistant. This project focuses on the practical workflow of writing papers: organizing references, improving clarity without plagiarism, and ensuring your writing sounds human.

ZoteroQuillBotGPTZeroAcademic Writing

AI-Powered RNAseq Enrichment Summarizer ​

Feed enrichment analysis results to an LLM via API, get plain-language summaries of what the results mean, then validate those summaries using an LLM-as-judge approach.

Covers the full LLM workflow: designing prompts, handling structured data, avoiding hallucinations, and validating outputs. You'll learn how to use AI APIs programmatically, not just through chat interfaces.

PythonOpenAI/Anthropic APIPrompt Engineering

What Could AI Help With? ​

Don't have a specific project yet? Here are common research pain points where AI tools can help. Pick one that matches your daily frustrations and explore it during the hands-on session.

Common Research Pain Points ​

Literature Review ​

Ask AI to read papers, extract key findings, and compare methodologies across studies. Great for getting up to speed on a new topic.

Data Cleaning ​

Transform messy datasets into tidy formats, handle missing values, or convert between file types. AI can write scripts to automate tedious data prep.

Manuscript Writing ​

Generate first drafts of methods sections, improve clarity, or adapt tone for different audiences. Overcome writer's block and polish rough drafts.

Debugging Code ​

Paste error messages and get explanations, suggested fixes, and prevention strategies. AI excels at pattern matching in error logs.

Boilerplate Code ​

Create starter templates for common tasks like data visualization, statistical tests, or file I/O. Save time on repetitive patterns.

Data Visualization ​

Describe what you want to show, and AI can suggest plot types and generate the code. Useful for exploratory analysis or publication figures.

Project-Specific Resources ​

Contribute Your Ideas ​

Have a project idea? Edit this page and add it - that's what contributing on GitHub looks like.

Next step: Check the resources page for curated links and further learning.