Hello Bioinformatics lovers,

People complain about my chatomics YouTube audio quality.

I take the feedback! I just spent $100 on a wireless microphone, and I hope the audio quality will be much better.

The fact is that when I started my channel 2 years ago, I did not think too much about the quality.

I just opened loom, and started recording.

over 120 videos later, I think I need to pay a little more attention to the quality now :)

Thanks for your support as always!

Today, we will talk about:

​
The Myth of the Bioinformatics Expert

We love labels.

“Expert.” “Specialist.” “Guru.”

But in bioinformatics, those words don’t mean what we think.

1. Expertise is always relative​
Bioinformatics isn’t one skill.
It’s an ocean: RNA-seq, proteomics, single-cell, structural biology.
Each of those fields could take a lifetime to master.

I’m comfortable with bulk RNA-seq and ChIP-seq.
But the first time I touched spatial transcriptomics?
I felt like a beginner again.

And that’s normal. Every dataset carries its own nuances.

2. Insights come from unexpected places​
Some of my most important lessons didn’t come from papers or textbooks.
They came from Twitter.

Like the time I learned that UMAP distances aren’t meant for absolute comparisons.​
Or a single post on batch effects in scRNA-seq that completely reshaped how I do QC.

Being humble enough to learn changed everything about my analysis.

3. Build your personal knowledge bank​
Here’s a tip I wish I started earlier:
Make an “Insights” folder in your email.

Forward every useful post, thread, or resource you come across.
Review it weekly.
Over time, it becomes your personal reference library.

4. The best experts admit they don’t know​
The smartest people I know in this field are never the loudest in the room.
They’re the first to say:
“I don’t know—but let’s figure it out together.”

That humility is what keeps them sharp.

Key takeaways​
• Expertise in bioinformatics is always specific.
• Social media is a powerful learning tool—if you use it well.
• Save what you find. Revisit it often.
• “I don’t know” is not a weakness—it’s the beginning of real expertise.

Other posts that you may find useful:

1. If you’re waiting for perfect bioinformatics data, you’ll never publish.​
2. You’re drowning in bioinformatics tools. You think you need the “best” one. You don’t.
3. Think you’ll remember every step of your bioinformatics project 3 months from now? You won’t. And it will cost you.
4. A Bioinformatician's UNIX Toolbox.
5. I stopped trying to keep up with every new *Seq method, and my career got better.​
6. The highest ROI (return on Investment)?​
7. ​solid foundation in statistics is even more important in the age of AI.
8. ​Why I sometimes ignore Nature Methods papers when picking bioinformatics tools.
9. ​Pixi, an alternative to conda and mamba for package management.
10. The first time I saw a red error message, I thought I broke everything. Turns out—it was just the computer trying to help me.​
11. have learned three ways to make important decisions.
12. ​I just got "Deep Learning for biology".
    ​

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!