Bioinformatics Core
Fall 2026 · UMass Chan Medical School
Theory and hands-on training in the omics methods, from bulk RNA-Seq through single-cell to spatial, paired with the responsible use of AI in research. Every technique is worked on a real study from Manuel Garber's lab, run end to end on Foundry Connect.
Three assays, three answers to the same tissue. Each rung buys you resolution and charges you something for it. Knowing which rung answers your question is most of the skill, and it is the spine this course is built on.
| Assay | What it measures | Best for | Blind to |
|---|---|---|---|
| Bulk RNA-Seq | Average expression over a whole tissue | Clean genotype or treatment contrasts, whole transcriptome, most sensitive, cheapest | Which cells, and where |
| Single-cell RNA-Seq | Expression per individual dissociated cell | Cell types, composition shifts, cell-state heterogeneity | Location. Plus dissociation artifacts and sparsity |
| Spatial (Xenium, seqFISH) | Expression per cell in its tissue location | Neighborhoods and cell-to-cell circuits in situ | Genes outside the targeted panel |
Working scientists across domains, not only genomics. Grad students, postdocs, bench researchers, and core-facility users, with mixed to low coding background. If you have never opened a terminal, the first 45 minutes of Session 1 were written for you.
No prior Linux, programming, or machine learning is assumed. Everything runs in a browser.
Mac, Windows, Linux, or a Chromebook. All are fine. Tell us on the form if you do not have one and we will find you a machine.
A Foundry Connect account, a Foundry membership, and a UMass Chan HPC cluster account, all in place before the first session. Request an HPC account, and we will help you with the rest.
Sep 18 through Oct 23, 1:00 to 4:00 pm. Hands-on throughout, with light homework. The closing capstone is optional.
Responsible AI is not a lecture at the end. It is its own session and a checkpoint inside every other one, because some people in this room work with patient data, and everyone has something they should not paste into a public tool.
You will never be asked to put real data into an AI tool during this course, and Session 1 covers exactly why.
Six Fridays, September 18 through October 23, every session 1:00 to 4:00 pm. Sessions 4 through 6 climb the ladder one rung at a time, and each opens by contrasting itself with the rung below.
All sessions are in Amphitheater II (S4-102), except October 16, which moves to Amphitheater I (S2-102).
Opens with a 45-minute quick start: accounts, the minimum command line, and where everything runs. Then how AI and LLMs work and how they fail, plus patient data and PHI, verification, disclosure, and bias.
Four ways to use AI: search, co-author, validator, and tutor. Prompting craft, and verifying everything.
The shared stats and figures toolkit, plus using AI as a statistics BS detector on your own results.
Differential expression, worked through the Vernia hepatic JNK study and the PPARα to FGF21 axis.
Cell types and composition, through the Gellatly vitiligo study and the question of why Tregs fail.
Tissue circuits in situ, through the Wang photosensitive-skin study and its keratinocyte to fibroblast to myeloid circuit.
Apply the whole workflow to something you are actually stuck on.
No toy datasets. Each domain session teaches its method on a published study from Manuel Garber's lab, with the real data, on the real pipelines. The study supplies the problem; the session is about the technique that answers it.
| Assay | Study | The biological question |
|---|---|---|
| Bulk RNA-Seq | Vernia et al. 2014 Cell Metabolism |
How does hepatic Jnk1/Jnk2 signaling reshape the liver transcriptome through the PPARα to FGF21 axis? |
| Single-cell | Gellatly et al. 2021 Science Translational Medicine |
Which immune subsets expand in vitiligo skin, and why do regulatory T cells fail to control them? |
| Spatial | Wang et al. 2026 Nature Immunology |
Where do MMP9-positive myeloid cells sit, and how does a keratinocyte to fibroblast to myeloid circuit drive photosensitivity? |
Instructors
Guest speakers
Leads the Data Science and AI Research team at Moderna, working on Bayesian methods for drug discovery. PhD from MIT Biological Engineering, previously at Novartis Institutes for Biomedical Research. A core developer on NetworkX and PyMC, and the author of the open-source packages pyjanitor and nxviz.
Fourteen years in computational biology across single-cell genomics, epigenomics, and spatial transcriptomics, previously Director of Computational Biology at Immunitas Therapeutics. Teaches bioinformatics to a following of more than 100,000 through the Chatomics newsletter, blog, and YouTube channel, alongside his widely used RNA-seq, ChIP-seq, and scRNA-seq analysis notes.
Registration open
Six Friday afternoons, September 18 through October 23, 1:00 to 4:00 pm at UMass Chan. Registration takes about three minutes: we ask what you work on and what you already use, so the sessions are pitched at the people actually in the room.
Register for Fall 2026