Hello Bioinformatics lovers,

Last week, I shared my dream lineup of 10 courses for learning bioinformatics, and the response surprised me.

Instead of diving in, many of you asked the same question: "Which one should I start with?"

Here's what you need to hear: You're overthinking it.

The real learning doesn't happen when you're comparing syllabi or waiting to pick the "optimal" course.

It happens when you struggle through your first analysis, when the code breaks, when you can't figure out why your p-values look weird.

The Foundation Still Matters (Even With AI)

I keep getting this question: "Can't I just use AI now? Do I still need to learn the basics?"

Yes. Emphatically yes.

Here's why: AI will happily give you code that runs. It will generate beautiful plots.

It will even explain what it's doing. But AI can't tell you if the answer is right.

Take ssGSEA (single-sample Gene Set Enrichment Analysis) as an example. It might give you working code.
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But "Should I use raw counts, DESeq2 normalized counts, or TPM for ssGSEA?"

Only one is correct (spoiler: it's TPM or FPKM, because ssGSEA ranks genes within each sample and needs gene-length normalization).

The input must reflect the relative expression of genes in a biologically meaningful, smoothly varying way. Raw or count-like data distorts ranking.

Without understanding the statistics behind the method, you'll never catch that error.

You'll get results that look professional but are fundamentally wrong.

Ask AI Better Questions

The problem isn't AI itself—it's how you use it.

Instead of "Write code to run ssGSEA on my data," try:

• "Help me understand how ssGSEA calculates enrichment scores"
• "What input should I use: raw counts, DESeq2 normalized counts, or TPM?"
• "Can I compare ssGSEA scores across different datasets?" (Answer: No. The scores are normalized per dataset)
• "Explain what this code chunk is doing, line by line"

Use AI as a learning companion, not a shortcut.

The Learning Paradox

Here's something uncomfortable but true: Struggling makes your brain remember deeper. It's good for learning.

Yes, AI can make you lazy. I catch myself doing it too—skipping code review, only checking when something obviously breaks.

But this is dangerous. AI can give you wrong answers you don't even realize are wrong.

You need the foundation. Statistics. Linear algebra. Machine learning basics. Not because you'll implement everything from scratch, but because you need to know when something is off.

What To Do Right Now

1. Pick any course from my list—literally any one—and commit to going through it end to end
2. Apply it to real problems as you learn
3. Use AI to accelerate, not replace, your learning

Stop optimizing which course to take. Start learning.

The students who succeed aren't the ones who found the perfect curriculum. They're the ones who started yesterday.

Happy Learning!

Tommy aka crazyhottommy

PS:

If you want to learn Bioinformatics, there are other 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/​

Stay awesome!