How do labs work for this class?
precourse material
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
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 • Jan 14, 2026
Installing software can be a real headache, so let us help you! This lab will be focused on helping you with IT support and getting to know the software tools that we'll be using throughout the course.
Lab 2 • January 21, 2026
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 3 • January 28, 2026
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 4 • February 4, 2026
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 5 • February 11, 2026
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 6 • February 19, 2026
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 7 • February 25, 2026
Artificial Intelligence is revolutionizing how we interact with code. In this lab, we'll review the solution for the last lab, but will use AI to guide us. I'll demonstrate how you can use the AI 'pair programmer' called Github Copilot to much more rapidly and seemlessly start new coding projects.
Lab 8 • March 4, 2026
Use your newfound public data sleuthing skills to find and analyze one of the first and largest transcriptomic studies of SARS-CoV-2. You'll start by using the tools from the last class of find this data online, then explore the study metadata to formulate a question, and carry out an analysis of the data to find an answer to your question.
off week • March 11, 2026
No lab this week. Hope you find some time to relax and recharge over the Sprimg break!
Lab 10 • March 18, 2026
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 11 • March 25, 2026
In this lab you'll create a custom R function that automates many of the tasks and visualizations that we've worked with throughout the course.
Lab 12 • April 1, 2026
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 an unpublished single cell atlas of the small intestine.
Lab 13 • April 15, 2025
To conclude the course, you'll continue working with the MIST dataset from the last lab and will use more sophisticated single cell approaches to identify unique transcriptional states elicited by infection in the intestine.