Hello Bioinformatics lovers,

I came back home 10:30 pm last night from a panel discussion.( I was exhausted and did not have the strength to write it last night)

That's why today's newsletter is a little late 😞

Today's newsletter is short. But is extremely important.

I wish I had been taught how to organize the project better, but here you are!

These foundational papers aren’t about algorithms or flashy tools.

They’re about how to work well—how to organize, code, and collaborate in a way that makes your science stronger, more reproducible, and easier to share.
​
1. A Quick Guide to Organizing Computational Biology Projects​
​

​
2. Ten Simple Rules for Reproducible Computational Research ​
​
3. Ten simple rules for biologists learning to program ​
​
4. Good enough practices in scientific computing ​
​
5. Best Practices for Scientific Computing​

Other posts that you may find helpful

1. Can you trust a boxplot with a small sample size?
2. Why is bioinformatics so complicated? Because biology is messy, layered, and full of exceptions.
3. You will stand out ahead of 99% of the people in anything if you do the following​
4. ​Use a genome browser to catch errors.
5. The worst moment for a bioinformatician? When the data arrives—and it’s already too late to fix the experiment.​
6. You ran a drug response assay. You expected cell counts to drop as drug concentration increased. But the data looks... wrong. Why? ​
7. Two patients. Same drug. Same total dose. One gets better. One gets worse. Why?​
8. The most influential course I’ve ever taken in computational genomics
9. Most cancer drugs don’t fail because they’re ineffective. They fail because they’re too toxic.​
10. String manipulation is a critical daily task for bioinformaticians. Here is a cheatsheet using stringr
11. ​AI won’t save your bioinformatics pipeline if your metadata is a mess.
12. You think your study is groundbreaking? Wait until you learn how HeLa contamination skewed whole archives—misidentifications across thousands of papers
13. Everyone wants to do “cutting-edge” bioinformatics. But in industry, the real work often starts with one question:
    Can you handle real data? I made a challenge for you.
14. ​Pressure is always there.​
15. ​I made many bad mistakes. but they helped me grow. ​
16. ​We all feel lost at some point in life, but here's what I've learned.​

Ignore my life lessons if you only like bioinformatics:)

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!