How you can transform from a wet biologist to a computational biologist even if you do not have any prior experiences.

Did you know that all biology is computational biology? and you are handicapped if you do not know any computational skills?

Here is the problem you face: as a wet biologist, you do not know where to start and you are overwhelmed with all the resources online. Many of the courses are not practical and you feel they are not useful after taking them.

Are you disappointed that people are too busy to help you with your data analysis? Are you frustrated that you do not know where to start learning yourself? Trust me. I was there, and I fully understand it.

Luckily, there is a solution now for you. Let me introduce you the book: From Cell line to Command line.

This book helps wet biologists to learn computational biology without prior experiences.

What is it all about?

What you will learn in this book:

  • Essential skills you need for biological or any data analysis which means you will have the full freedom for your own data analysis
  • Tips and mistakes that I have learned from my ten years of computational experience which means you can avoid many of the mistakes that I made and shortcut the success
  • Resource that I collected from the past 10 years for further reading and learning which means you save a lot of time to find the right resources and can learn deeper
  • Even if you are an experienced bioinformatician, I am sure you will learn something useful from the book
  • A peek at the contents of the book 👇

There are both technical parts and general suggestions. This book is not a programming book. Many of the topics will not be dived deeply, however it will serve as a compass to guide your learning directions.

It does cover many essential analysis skills such as bash, Exploratory data analysis (EDA), PCA, heatmap, matrix factorization, Snakemake, and machine learning etc., using real examples that you can follow.

Once you pass the initial steep learning slope, you will get into full swing. You will be able to learn anything with determination and discipline!​

My Story

I had zero computation experience 4 years into my Ph.D. My Ph.D advisor asked me to analyze a public dataset which is 2GB, and I could not open it with Excel. Suddenly, I realized that I didn’t have data analysis skills. A bit of frustration was an understatement.

The learning curve was steep for me when I started to learn computation. There were a lot of challenges and frustrations. I did not have anyone to turn to. During my Ph.D., I was the only one on the floor who was learning bioinformatics.

It was like a rabbit hole. I started stockpiling books to learn Unix, python, and R programming languages. I also took many online courses on edx, Coursera, and udacity. I took pride in being one of their first loyal customers before they became popular.

It took me a long time to master practically useful skills as the courses I took usually not applicable to what I was analyzing.

I also spent a lot of time googling and asking questions on forums such as and

I took many detours and hope to share my experience and tips to help you avoid them.

In summary, I learned computational biology the hard way.

This is my timeline:

I started my Ph.D. in 2008 in a wet lab—first author wet lab papers: PNAS (2011), JBC (2013). I began to learn programming in April 2012.

I joined MD Anderson for a computational biology postdoc in March 2015 to Oct 2018. I learned a ton but did not have any first-author papers. I did write a biostars handbook ChIP-seq chapter (2017) though.

Dry lab: two co-first author papers: cancer cell (2020), cell reports (2021). computational work at MD Anderson was published when I was working at Dana-Farber Cancer Institute.

Dry lab: two first-author/co-corresponding author papers: Bioinformatics (2020), F1000research (2021). First author: Nature communications (end of 2021). Last author: Biorxiv 2023.

I still have two co-first-author computational papers in the making.

Now, as the director of computational biology in a biotech startup, I lead a team of computational scientists analyzing single-cell genomics data. We use machine learning approaches and cloud computing for new target discovery.

Read my
back story in Nature.

It took me almost ten years to make the transformation. If you dedicate 1 hour every day to studying the code in the book, you will become a data master in a year! Be sure to read the books and take the courses recommended in this book to enhance your skills.

If I can do it, YOU can too!

This book helps YOU to make the same transformation with a shorter time!

What others say

What you will get

A zip file of all the HTML files so you can browse through them. Click the index.html, and you will be able to read it. A 440-page PDF file and an epub file will also be included. The HTML files are preferred for the best reading experience.

It is going to be a dynamic book that I will update regularly after your feedback. You will get all the newer version for free once you buy the early versions.

Hooray! Version 2 is finally here. If you have bought Version 1, I will send version 2 to you for FREE. Version 2 change log

Money-back guarantee

If you bought the book and think it is not what you want, email me within 7 days. I will return the money, and you keep the book.

Take Action

“The best time to start learning is ten years ago; the second best time to start learning is right now."

Do not miss out the opportunity to make the change you want.

No more begging for help, and you will have full control of your data analysis! What a liberty!

Click "Start transforming!" NOW, fill in your email (make sure it is correct!), pay with credit card, then you will get an email with a link to download a zip file of size ~250Mb. Unzip it, click the HTML files and begin to indulge yourself into the world of computational biology!

Happy Learning!