Hello Bioinformatics lovers,

Tommy here. I am in Yale today for an event. Hope you enjoy today's newsletter.

"How do people like you ever get last-author papers?"

A cell biologist asked Florian Markowetz this during a 2008 job interview.

The subtext: you just crunch numbers. You're not a real biologist.

Markowetz went on to write one of the most important essays in our field: "All biology is computational biology" (PLOS Biology, 2017).

By 2012, a funding panel had called his group a "mathematical service unit."

By 2016, the NEJM editors were using the term "research parasites" to describe scientists who re-analyze others' data.

The bias ran deep. But the biology moved on.

Computation brings order. Linnaeus classified species by hand. Today, Gene Ontology, Ensembl, and KEGG organize biological knowledge at a scale no individual could manage.

These aren't auxiliary tools — they're the infrastructure everything else builds on.

Computation reveals what eyes can't. You can't see mutational signatures in one genome. You need thousands and the math to find patterns.

TCGA and the Human Cell Atlas are the modern explorer's maps — rough but essential.

Computation makes ideas testable. "Tumor heterogeneity drives drug resistance" is hand-waving until you quantify clonal diversity across patient cohorts.

The math turns hypotheses into something you can actually test.

No one says "pipette biologist." So why "computational biologist"? We're all biologists wielding different tools.

I lived this transition myself. Four years into my PhD, two first-author papers in hand, my adviser asked me to analyze a 2 GB text file.

I couldn't open it in Excel. That frustration launched me into Unix, Python, R — and eventually a whole new career.

The takeaway: learn to code. Learn statistics. These are table stakes now. The next breakthroughs won't come from a bench or a terminal exclusively — they'll come from sharp questions, clean data, solid code, and strong stats working together.

I know the AI is hot. Learning those basic skills will help you to better leverage AI.

For example, Claude Code is run in a terminal (well, you can run it on the desktop app too), the unix skills are still useful.

You see Claude does all the grep, ls, find commands and you understand what it does. It runs the cron job and you know the basics too.

btw, I asked Claude to record how I set up openclaw, the AI chatbot here https://divingintogeneticsandgenomics.com/post/openclaw-ai-assistant-mac-mini-setup/​

Happy Learning!

Tommy aka crazyhottommy

PS:

If you want to learn Bioinformatics, there are four ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​
4. I post every day on Linkedin teaching bioinformatics and AI.

Stay awesome!

PPS: