Hello Bioinformatics lovers,

Tommy here, I gave a talk at Boston University sharing my experience in learning bioinformatics.

You can find the recording here. There is a Q&A section at the end that you may find helpful as well.

Due to my background in a wet lab, I focus primarily on the biological side when I do bioinformatics analysis.

(That's your strength if you are from a biology background)

Most bioinformaticians fall into the same trap:
They assume protein abundance equals protein function.

It feels intuitive. More protein = more activity, right?
Wrong. Dangerously wrong.

The truth:
The most important proteins don’t change much in abundance.
They change in activity. And that changes everything.

You’ll miss the real story if you only count proteins.
The function lives in post-translational modifications—PTMs.

Take transcription factors.
They’re often barely detectable in expression data.
But they control entire gene networks.
It’s not the abundance—it’s the switch from inactive to active.

A few examples:

• Phosphorylation turns MAPK into a signal-sending machine. A tiny phosphate group triggers massive changes in cell behavior.
• Ubiquitination tags IκB for destruction, freeing NF-κB to flood the nucleus and activate immune genes. No increase in protein levels needed.
• PARP1 senses DNA breaks and adds ADP-ribose chains, sending a repair signal that recruits fixers like XRCC1—within seconds.

None of these relies on protein “levels.”

The lesson:​
Abundance ≠ function.
Function is hidden in modifications, localization, and interactions—layers RNA-seq or proteomics alone won’t reveal.

So next time you analyze gene or protein expression, pause before over-interpreting. Ask yourself:
Is it active? Is it modified? Is it functional?

Key takeaway:​
Bioinformaticians must think like biologists.
Otherwise, you risk missing the point entirely.

Other posts that you may find helpful:

1. Want to ruin your analysis in one move? Ignore the biology behind the data.​
2. No one tells you this: Coding isn’t about memorizing syntax. It’s about frustration, failure, and trying again anyway ​
3. ​The perception of success vs the reality of success.​
4. ​How to write R functions.​
5. Human-generated biological data are messy, and this is the reality when analyzing data. ​
6. ​deep understanding of a heatmap.​
7. If I told you the answer to “How many ER binding sites are in MCF7 cells?” is anywhere between 1,000 and 20,000—would you believe me?

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/​

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Stay awesome!