Hello Bioinformatics lovers,

Tommy here. Everyone wants to master bioinformatics fast.

But here’s the cold truth: speed is a lie. Time is your ally.

After 13 years in the field, here’s what I’ve learned.

Bioinformatics is not a crash course It’s not “learn Python and call it a day.”

It’s a craft. Like a language, you grow into it.

I wrote my first “Hello, world” script 13 years ago. It was slow. Messy. Frustrating. But it was real learning.

What time gives you

With mileage, you notice things others miss:

• Bad FASTQ files before the aligner screams
• Inconsistent metadata that wrecks downstream stats
• PCA plots that lie

These are scars earned, not shortcuts taken.

Intuition is built, not bought

Experience teaches you:

• Why a QC step matters
• When an outlier is noise vs. signal
• How to debug a DESeq2 error with StackOverflow (and now, LLMs)

No tutorial gives you that. Only time does.

Example 1: FASTQC A beginner runs:

​
fastqc sample1.fastq
​

An experienced analyst runs it on all samples, checks overrepresented sequences, and knows what a bad adapter peak looks like.

Example 2: RNA-seq with DESeq2

A newcomer runs:

​
dds <- DESeqDataSetFromMatrix(countData, colData, design = ~ condition)
dds <- DESeq(dds)
​

A seasoned analyst pauses:

• Are replicates balanced?
• Any hidden batch effects?
• Should I use lfcShrink() before plotting?

That pause is wisdom earned.

The craft of bioinformatics

You pick up tools. You learn to swing them better. Eventually, you build things that last.

If you feel behind, that’s good. It means you’re aware. Keep going. Learn every week.

Five years from now, you’ll look back and be shocked by your growth.

Key takeaways

• Time is your biggest advantage
• Experience builds intuition
• Don’t rush; compound learning daily
• Mistakes teach more than success

Action items

• Set aside 30 minutes a day for deep learning
• Review your mistakes monthly
• Follow mentors in bioinformatics
• Contribute to real projects
• Start here: my tutorial​

The truth: You won’t master bioinformatics in 6 months.

But in 6 years? You’ll walk into any dataset with confidence.

Just keep going.

Other posts that you may find helpful

1. ​Data visualization is a critical step in data analysis. 8 links to bookmark for better data visualization:🧵
2. Bioinformatics is more about biology + informatics.
3. ​5 websites to analyze GEO RNAseq data without a single line of code 👇
4. ​How to calculate correlation for single-cell data.
5. ​omnideconv is an ecosystem of user-friendly tools and resources for cell-type deconvolution. (bulk RNAseq, spatial data)
6. ​Too many bioinformatics analysis crash silently.​
7. ​23 tools to work with (single-cell) TCR/BCR-seq immune repertoire data 🧵 👇
8. ​Sargent is a transformation- and cluster-free cell-type annotation method
   ​

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!