Hello Bioinformatics lovers,

Tommy here.

When my PhD adviser handed me a 2 GB text file to analyze, I couldn’t open it. Not in Excel, not in anything I knew.

That was the day I learned my biology training had a hole in it the size of a genome.

The skills that fixed it weren’t the glamorous ones. No new algorithm saved me.

What saved me was learning to organize a project so I could find my own work six months later, and to write code I could trust.

These five papers teach exactly that. I gave a talk this year on good-enough practices for reproducible computing, and every point traced back to one of them.

Start here: Noble’s A Quick Guide to Organizing Computational Biology Projects is the one I send to every new hire.

One directory layout, one naming convention, and your future self stops cursing your past self.

Then build on it:

​Best Practices for Scientific Computing (Wilson et al.) is the full checklist: version control, testing, not copy-pasting code you’ll regret.

​Good Enough Practices in Scientific Computing, says you don’t need all of it on day one. Do the achievable subset. This is the one to actually follow.

​Ten Simple Rules for Reproducible Computational Research (Sandve et al.) is the short version you can pin above your desk. Record every intermediate step. Store the raw data behind every plot.

​Ten Simple Rules for Biologists Learning to Program (Carey & Papin) is for the wet-lab scientist staring at the rabbit hole I once fell into. Rule 5, learn how to ask questions, is worth the read on its own.

None of these say it out loud: this is the work that compounds. A clever method gets you one paper.

A reproducible, well-organized project gets you the next ten, and lets a collaborator pick up where you stopped without a single panicked Slack message.

AI can write your code now. It cannot decide where your files live, what your raw data means, or whether your result reproduces.

That judgment stays yours. These papers sharpen it.

Read one this weekend. Hit reply and tell me which rule you break the most. I break “record every intermediate result” constantly, and I know better.

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 post daily on Linkedin https://www.linkedin.com/in/%F0%9F%8E%AF-ming-tommy-tang-40650014/recent-activity/all/ and you may find useful posts there that I did not include in the newsletter

Stay awesome!