I built the site I wish someone had handed me ten years ago.

Quick question before we get into it: do you know how sex is determined in a turtle? Most people don’t. I didn’t either, until it became my entire PhD.

The turtle that started it all

I was a wet-lab researcher studying sex determination mechanisms in turtles for my PhD at the University of North Dakota — a purely biological question, with zero code involved. Then our lab got the chance to sequence our turtle model’s whole transcriptome, using this cutting-edge new technology called RNA-seq, back when microarrays were still considered high-tech.

The data landed. Nobody in the department knew what to do with it. My PI looked at me and said, “Lei, I know you like computers and technology. Why don’t you take this on?”

I remember thinking: since when does liking computers qualify you to be a bioinformatician? But underneath the confusion, I was thrilled. New tech, new data, count me in. One problem: I had absolutely no idea where to start, and the internet in those days was not exactly overflowing with RNA-seq tutorials.

Ten years, and 95% of it was useless

So I did what you’d do. I went and begged for help. First stop: the med school, where a few technicians supposedly knew bioinformatics. They knew plenty — just nothing about a non-model organism like mine, and they were not shy about acting like they did anyway.

Next stop: the computer science department. Their advice was thorough. Learn Bash. Learn Linux. Learn Python, C++, data structures, statistics, linear algebra. Build the whole foundation first, they said, then come back to bioinformatics.

I took it seriously. I spent the next ten years doing exactly that.

My conclusion after a decade: that advice was complete bullshit. Ninety-five percent of what I grinded through in those years turned out to be irrelevant to the actual work of analyzing NGS data. I ended up finishing my first RNA-seq project with purchased software, the very thing the “learn everything first” crowd would have scoffed at.

Where the real fun began

From there I did a four-year postdoc in a dry lab in New York, living and breathing RNA-seq analysis until I could do it in my sleep. Then I moved to a lab at UT Southwestern Medical Center in Texas, where things really opened up — hundreds of projects, nearly every NGS data type you can name.

It’s also where I hit the moment that actually built NGS101. I inherited a project from a colleague and just wanted to know one thing: which software, which parameters, so my analysis would line up with his. He wouldn’t tell me. “Just Google it. Read the documentation. Figure it out yourself,” he said, while sitting on the exact answer I needed.

Ten years earlier, computer scientists had gatekept me with a mountain of prerequisites. Now a peer was gatekeeping me with silence. Same trap, different costume: hold the specifics tight, keep people dependent, protect your seat.

I decided I wasn’t going to run my corner of this field that way. So I built NGS101 to do the opposite — every parameter, every line of code, real example data, laid out in the open. If you want to follow along and run it yourself, you can, today, not in five years.

That’s the idea behind every tutorial on this site, and it’s the whole design of the workshop: a complete, end-to-end pipeline, run live, with real-time coaching as you work through your own data — not a recording, not a toy dataset, and not theory before practice.

Who this is for

• Bench scientists who generate NGS data and are tired of waiting on a bioinformatician to tell them what it means
• Medical doctors and researchers moving into genomic research who need a fast, honest on-ramp
• PhD students and postdocs handed a dataset with no one around to teach them
• Anyone who’s been told to “just learn everything first” and would rather just start

Explore the tutorials, or if you’d like a guided path with real feedback on your own data, take a look at the workshop. Either way, welcome — turtles optional.

“What book should I read?”

I get this question constantly from students, and my honest answer is: none. Don’t buy a bioinformatics book.