Hello Bioinformatics lovers,

Tommy here again. Do you feel eager to receive my emails on Saturdays?

At least, it now become my habit. I will open the email and fire it up.

Today, I will share how seemingly unrelated skills shaped my bioinformatics career.

( I will have all my week's long posts linked in the end too).

I never set out to be a bioinformatician. In fact, I started my career at the bench, pipetting away in a wet lab,

studying epigenetics in cancer.

Back then, I had no idea that a single experiment—my first ChIP-seq analysis—would change the course of my career forever.

ChIP-seq was new to me. I struggled. The data was messy.

I didn’t know how to analyze it properly. But I was hooked.

That led me to discover Shirley Liu’s lab, a powerhouse in computational genomics.

I devoured their papers, studied their tools, and unknowingly set myself on a path that would define my career.

Years later, I found myself leading bioinformatics for CIDC at Dana-Farber—in Shirley’s group.

During my postdoc at MD Anderson, I picked up Snakemake to automate genomics pipelines.

It wasn’t glamorous. Just a way to make my life easier.

But when I joined Shirley’s lab, Snakemake was critical to CIDC’s data processing.

That skill helped land me the job.

At Harvard FAS Informatics, I worked on single-cell RNA-seq and single cell ATAC-seq.

I had no idea if this would ever be useful in my future career.

But at Dana-Farber, I ended up collaborating with Tao Liu, the creator of MACS2—the most widely used peak caller.

That single-cell expertise became one of my strongest assets.

In grad school, I took Rafa Irizarry’s EdX course on data science. Three times.

I admired his work. His teaching.

A decade later, I found myself working alongside him at Dana-Farber.

At Dana-Farber, I led a team for the first time.

That’s when I learned a hard truth: Technical skills alone aren’t enough.

I started reading about leadership. I took courses. I attended management camps.

Those lessons prepared me for my next leap—building a computational team from scratch at Immunitas.

When I started, I had no idea where each skill would lead me.

You Can’t Connect the Dots Looking Forward

I just followed my curiosity and kept learning.

Now, looking back, I can connect the dots—

and I see how every random skill, every challenge, and every unexpected turn led me here.

The Lesson? Collect the Dots.​
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• Every skill you learn matters—even if its value isn’t obvious yet.
• Follow your curiosity, invest in learning, and say yes to new challenges.
• The best opportunities come when preparation meets chance.

Other posts that you may find helpful:
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1. ​Someone followed my tutorial to replicate a genomics paper. You should too! I am creating videos for the tutorial here.
2. ​Why are there so many gene IDs? Let's break down the complexities of gene annotations and how to convert between them.
3. Want to choose the maximum value within a group of data? Here's how you can do it using dplyr in R.
4. ​Genome Builds Matter: Avoid Costly Mistakes in Genomic Analysis
5. ​Why LSI (Latent Semantic Indexing) is Essential for scATAC-seq Analysis​
6. Three unix one-liners to turn a fastq file to a fasta file.
7. How to Use samtools – A Must-Know Tool for NGS Data
8. ​How to find the closest genomic feature to a region?​
9. ​Why do people hesitate to share their code?​
10. Many bioinformatics students don’t know much about NGS pre-processing. But trust me, understanding the raw data is essential. Here’s why.​
11. Imagine losing weeks or months of bioinformatics work because of a failed hard drive. Scary, right? That’s why backups are non-negotiable. How to use cron to backup.

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!