Re-invent yourself

04/24/2012, ten years ago today, I started to learn how to program for the first time using python. That’s four years into my Ph.D. at the University of Florida. I was trained in a molecular cancer biology lab and was happily pipetting reagents into the Eppendorf tubes. Look at the nice gel pictures I could make!?

When my Ph.D. advisor asked me to analyze a public ChIP-seq dataset, things started to change. I downloaded it, and it was 2GB in size. I could not even open it with excel! It was the first time I realized that I needed to learn how to program.? I started learning coding in April 2012 with my first ever python book: python programming for absolute beginners.? I still remember the days that after work, I would sit down in front of the computer and go through the book until 10 pm every day. It was not that practical to translate what I had learned into what I wanted to analyze in the lab, but I entered a new world!

?In the Fall semester of 2012, I took a beginner bioinformatics course at the University of Florida using practical computing for biologists as a reference book.? It is a great book, and it taught me the regular expression, Unix commands, and some python stuff directly related to biology. I was deeply attracted by the beauty of codes and was surprised/satisfied with how useful learning coding can be.

?I then relentlessly took almost 40 courses related to data analysis from Coursera, Edx, Udacity, and datacamp. In 2013, I started to blog the things I had learned with the hope of sharing the tricks and the problems I solved. I did not realize it was that popular for bioinformatics beginners. It attracted over 6000 views per month and was ranked in the top 75 bioinformatics Blogs and Websites for Bioinformaticians by Feedspot in 2019.

?When I look back, the biggest challenge for a bioinformatics newbie is knowing nothing about the resource to learn. I was the only one on the floor learning how to program, and I spent many hours searching online. The learning curve was steep initially, but I gradually got into the full swing. I curated a list of resources for getting started with genomics to help others in the same boat as me.

?In 2015, my strong interest in computation propelled me to pursue a computational biology postdoc with Roel Verhaak, who was in MD Anderson Cancer Center at the time and was a key player in the TCGA Glioblastoma project. There, I had the opportunity to play with TB size real cancer genomic datasets. I accumulated extensive experience dealing with bulk RNA-seq, whole-exome/genome seq, bisulfite sequencing, ChIP-seq, and ATAC-seq data and built various pipelines to process them uniformly.

My later job at Harvard FAS informatics and Dana-Farber extended my skills to analyze single-cell RNAseq and single-cell ATACseq datasets. Now, I am fortunate to lead the computational biology team at Immunitas, using single-cell genomics to find new cancer therapy targets to help the patients.

What a wild ride for the past ten years! New challenges are not necessarily bad. Without stepping out of my comfort zone and learning it, I could not be me today. Instead of staying away from it, grab the opportunity to re-invent yourself, and you will find another brand new world for your to explore. I am grateful to many mentors and friends who helped me.?

Three years ago, I started to take leadership courses and I love them! The idea of empowering others to be their best version and accomplishing bigger things as a team that a single person can not achieve struck me. I look forward to the next ten years!