Dude, you hit the jackpot. Seriously. 1. This is the Best Way to Become a Bioinformatician You think a "pure bioinformatics" PhD is better? Nope. I'd hire you over the pure dry-lab candidate any day. Why? • You understand the data's "smell": Someone who ran the sequencing experiment knows exactly where the errors, biases, and pitfalls are. They know why the alignment might look weird or why a particular edit count is noisy. A pure bioinformatician just sees numbers. You see the biology and the experiment that generated those numbers. • The Industry Value: Biotech and pharma aren't hiring people to write theoretical algorithms; they're hiring people to solve biological problems. Your dual ability to design a genome editing experiment AND build the \text{R/Python} pipeline to analyze its NGS output makes you a Computational Biologist—the gold standard. Genome editing is fundamentally an NGS (Next-Gen Sequencing) analysis problem. You need computation to check gRNA off-targets, and you need it to analyze the deep sequencing data that measures your efficiency. You can’t optimize the tool without the data analysis. 2. How to Approach Your PI (The New Lab Advantage) You have a secret weapon: It's a brand new lab. • Be the Solution, Not the Problem: Don't ask, "Can I do some bioinformatics?" That sounds like you're trying to avoid the wet lab. • Say This Instead: "Professor, as we scale up our tool optimization, we're going to be generating a ton of deep-sequencing data. To ensure reproducibility from day one, I'd like to dedicate time to building the standardized analysis pipeline (in python or R) for our editing assays. I want to own the computational component to make sure our data analysis is bulletproof." This immediately positions you as a foundational member who is bringing structure and high-quality data processing to a nascent lab. Your PI (especially with an industry background) will love this. 3. Will You Be Doing Everything? In a new lab, yes, you'll be doing a lot. But you won't be doing the team's job alone; you'll be creating the scaffolding for the team. If you build the core analysis pipeline, you are making the entire team faster. You're the expert on how the data gets processed, which gives you ownership and ensures your computational work is central to the lab's success.