How do labs work for this class?
precourse material (required)
Learn more about our in-person labs, how you can participate in-person or virtually, and what you'll learn along the way.
All labs are held in-person on the campus of the University of Pennsylvania, and use real datasets from infectious disease research to hone and expand your computational skills. Virtual options are available through the course Discord page.
precourse material (required)
Learn more about our in-person labs, how you can participate in-person or virtually, and what you'll learn along the way.
Lab 1 • January 27, 2027
Using command-line tools often requires that you run similar code for each of your samples (e.g. read mapping). In this lab, you'll learn how to automate this redundant process using a simple code-aware text editor, making it possible for you to get work done even when you're not sitting in front of your computer. How great is that?!
Lab 2 • February 3, 2027
Your working directory is already starting to get messy, and the proliferation of files and file-types will only continue throughout the course. It's time to discuss best practices for managing an active coding project using the version control system, Git, and the related web resource, Github.
Lab 3 • February 10, 2027
At some point we all have to wrestle with gene annotations – that is, all the stuff we can label a gene with. In this lab, you'll learn to access a world of gene-centric annotation data and will practice on gene expression data from non-model organisms.
Lab 4 • February 17, 2027
What about those reads that didn't map to the human reference? In this lab you'll learn to make the most from your RNA-seq data by digging through these 'junk' unmapped reads. It turns out that most RNA-seq studies are 'metatranscriptomes'.
Lab 5 • February 24, 2027
Explore a large and multivariate dataset generated from the helmith parasite, Schistosoma mansoni, an important pathogen of humans. You'll use dimensional reduction to understand how factors like sex, developmental stage, genetic strain and drug treatment contribute to differences in gene expression.
Lab 6 • March 3, 2027
Artificial Intelligence has revolutionized how we interact with code and, increasingly, bioinformatics tools and data analyses. In this lab, you'll learn how to use the AI 'pair programmer' called Github Copilot to much more rapidly and seemlessly start new coding projects. In the second half of lab, we'll use the command-line interface for Google's Gemini v2.5 to see whether agentic AI can carry out a complete RNA-seq analysis using only a detailed prompt.
University Spring Break • March 10, 2027
Relax and enjoy the time off. We'll see you back here next week!
Lab 7/8/9 • March 17, 24, and 31, 2027
In last week's lecture, you learned to use functional enrichment tools like GO and GSEA to identify themes in your RNA-seq data. In this lab, we'll put these important skills to the test!
Lab 10/11/12 • April 7, 14, and 21, 2027
At this point, you've learned the basics of processing and analyzing scRNA-seq data. In this lab, we'll put those skills to the test by exploring GutPath – a recently released single cell atlas of the small intestine.