Hello Bioinformatics lovers,

Tommy here. Welcome, all the new subscribers!

Today's newsletter is a little late. I usually write it the day before and send it out early Saturday morning.

The previous week and coming weeks are super busy, and I have a presentation every week.
​
I still get nervous when I give a presentation
​
Even after 17 years in the US. And it is NORMAL.
​
Okay, let's dive into today's topic.

The communication between Bioinformatician and wet lab scientists.

Avoid This Costly Lab Mistake: Start Collaborating Early

In research, timing is everything—and that includes when bioinformaticians and wet-lab scientists start talking.

Too often, bioinformaticians are called in after the experiment is done—

when batch effects are baked in, metadata is a mess, and sample names are inconsistent.

By then, it’s a rescue mission, not an analysis.

Let’s flip the script.

For Wet-Lab Scientists:

Why involve bioinformaticians before data generation?

1. Better design = better results​
   Bioinformaticians can help plan sample sizes, calculate statistical power, and structure experiments for clean comparisons.
2. Avoid classic pitfalls​
   Harvesting control and treated samples on different days? That’s a batch effect waiting to happen—and it can make your findings unpublishable.
3. Prevent wasted effort​
   Late-stage fixes are often impossible. Bioinformaticians can tell you why it failed but not fix it after the fact.
4. Collaboration saves time and money​
   Engaging your data team early prevents costly reruns and ensures the experiment answers the biological question.

For Bioinformaticians:

Why involve wet-lab scientists before writing your pipeline?

1. Lab workflows affect the data​
   From sample prep to QC and naming conventions, wet-lab variability can derail even your cleanest code.
2. Assumptions = danger​
   If you assume samples are randomized or protocols are standardized without checking, your downstream analysis might be invalid.
3. Real-world context is gold​
   Wet-lab teams know what happened during prep. Their insight helps you customize normalization, QC, and contrasts.
4. Metadata = mission-critical​
   No analysis is better than the metadata behind it. Early conversations help ensure that data structure, annotations, and sample tracking are all usable.

Key Takeaways

• Bioinformatics isn’t just post-processing—it should guide experimental design.
• Wet-lab insight makes dry-lab analysis meaningful.
• Early collaboration improves reproducibility, saves time, and strengthens publications.

Action Items

For Wet-Lab Scientists

• Invite bioinformaticians to your experiment planning meetings.
• Collaborate on sample sizes, batch handling, and metadata collection.

For Bioinformaticians

• Talk to wet-lab scientists before finalizing pipelines.
• Ask about protocols, sample tracking, and potential confounders.
• Build your QC strategy with real-world lab constraints in mind.

It’s not just about making better plots. It’s about doing better science.

Start the conversation early—and avoid the pain later.

btw, if you are interested in more advanced topics of bioinformatics, you may like Stephen Turner's blog https://blog.stephenturner.us/

Other posts that you may find helpful

1. The difference between Extraordinary and Ordinary is that Extra.
2. Want to master bioinformatics data visualization? Learn ggplot2! 🧵👇
3. The body() function you do not want to miss! 🧵 Bioinformatics Tip: How to Filter Data in Bash & R Without Losing the Header
4. Focus on the things that you can control.
5. ​Bioinformatics evolves fast. Just as we catch up with single-cell RNA-seq (scRNA-seq), spatial transcriptomics arrives with new challenges.
6. 🧵 Bioinformatics Tip: Stop Struggling with Large Files—Master sed for Fast Text Processing​
7. 🧵 If you’re doing bioinformatics manually, you’re wasting time and prone to making errors. 4 levels of bioinformatics​
8. 🧵 Transcript-Level RNA-seq to Gene-Level: Simple Unix One-Liners to make the tx2gene file
9. ​Computational analysis of DNA methylation from long-read sequencing ​
10. 🧵 PCA is everywhere in bioinformatics—but did you know it’s just SVD in disguise?​
11. 🧵 How sed Can Supercharge Your Bioinformatics Workflow​
12. 🧵 So You Want to Be a Computational Biologist?​
13. 🧵 Functional Programming in R: Why It’s a Game-Changer for Bioinformatics
14. 🧵 Bioinformaticians: Stop Coding & Start Reading Papers!​
15. 🧵 RNA-seq Normalization: What You Need to Know: CPM, RPKM, TPM ​

I know some of those posts are not really bioinformatics-related, but they are some of the life lessons that I want to share. Reply let me know if you do not want to read those:) I will keep bioinformatics only.
​
It is a lot of posts! Right, I spent at least 4 hours writing them and pre-scheduling them on Sunday :)

Please forward this newsletter to your friends if you think it is helpful.

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!