Hi Bioinformatics lovers,

Tommy here. Please forward this newsletter to someone if you think it will help them.

I've just written a hands-on tutorial on up-leveling your skills in analyzing RNA-Seq data.

I will show you how to use purrr::map() and list column to avoid repetition in your R code. Read it here.

Today, we will talk about doing bioinformatics like doing an experiment:

Bioinformatics isn’t just crunching numbers.

It’s not staring at a terminal until your eyes blur.
It’s not waiting for Seurat to finish spitting out clusters.

It’s an experiment.
Every single time.
Just like a wet lab.
Except your pipette is code.

You start with a hunch.
A maybe.
A whisper of “what if.”

Then you design.
You test.
It fails.
You pivot.
You refine.
You circle back.

And somewhere along that messy loop, something clicks.

One of my favorite moments?
Single-cell data. Looked like garbage.
I was ready to throw it out.
But a biologist leaned over and said:
“Those cells were under stress.”

Suddenly the noise wasn’t noise anymore.
It was biology.
It was signal.
It was truth.

That moment didn’t come from a pipeline.
It came from a conversation.
From collaboration.
From listening.

Here’s the raw truth:
Bioinformatics without biology is just noise.
You need both.
Your code.
Their insight.
The back-and-forth dance of data and domain.

So don’t hide behind tools.
Don’t be the person who blindly runs DESeq2.
Be the person who asks:

Why here?
Why this marker?
What does this pathway mean?

That’s the real job.

Every analysis is an experiment.
Run → Feedback → Refine.
And repeat.

No bench?
No problem.
You’re still a scientist.
Still experimenting.

The best insights don’t come from code.
They come from curiosity.
From collaboration.
From refusing to accept noise as noise.

So ask yourself:
What’s the biology hiding in your data?

And—what was your “aha” moment?

Hit reply. I’d love to hear it.

Other posts that you may find helpful

1. Bioinformaticians, I’ll say it loud. Never blindly trust the data you're given.​
2. ​Rapid epigenomic classification of acute leukemia​
3. You won’t remember what you did. This tip will save you hours in the future.
4. If you work on cancer, DepMap is a great resource to mine.
5. Bioinformatics moves fast. If you rely only on recipes from books, you’ll soon find they’re obsolete. ​
6. In high-dimensional bio data—transcriptomics, proteomics, metabolomics—you're almost guaranteed to find something “significant.” Even when there’s nothing there. ​
7. What does the dot product have to do with bioinformatics?

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. Lastly, I have a book called "From Cell Line to Command Line" to teach you bioinformatics.

Stay awesome!