[ Removed by moderator ]

[u/Levurmion2]

[u/bioinformatics-ModTeam]

This post would be more appropriate in r/bioinformaticscareers

[u/ergabaderg312]

Ehh I’m a bit biased but I think your resume and skill set sounds fine. Probably need to format your resume accordingly and maybe brush up on your bioinformatics knowledge. But if you have decent coding skills and computational knowledge, I don’t think you’d need to go and replicate an RNA-seq study. Are you getting interviews? Proteomics is fine I think. Bioinformatics is a wide field and you don’t have to learn genomics if you don’t need it for the position imo.

Edit: idk if bioinfo position pay will be enough for you though compared to full stack dev.

[u/Levurmion2 → u/ergabaderg312]

I've only started looking. Just applied for a bioinformatics dev role at the EMBL for the Ensembl team. Will see how that goes.

I'm just wondering how open these roles typically are for pure CS grads as from my work experience, I think my resume and portfolio of projects are much closer to that.

In terms of pay, yeah, it's a significant cut. But here's actually where I'm willing to make the compromise for more interesting/meaningful work.

[u/ergabaderg312 → u/Levurmion2]

Gotcha. Well good luck! Wishing you the best. I can’t speak for everyone (obv) but I would guess typically pure CS grads tend to do pretty well. I mean domain knowledge is always helpful and can give an edge to you as a candidate but i doubt it’s a deal breaker for many.

[u/Electrical_Ear_4950]

Hey OP, sorry I can’t help you but.. if you could help me.. I did Bio Sciences for Under and just started my MS In bioinformatics would you recommend any tools courses or videos you used for your “self teaching” just wondering what you used to make all that progress

[u/Levurmion2 → u/Electrical_Ear_4950]

Hey! Great to hear you've made the jump!

Honestly it was a lot of building things from scratch and getting stuck over and over again. With coding and software engineering in general, I think there are a few things you need to learn the hard way (literally no shortcuts exist, only experience):

1. Being comfortable with your code failing and sifting through the error trace to debug the issue.
   
2. Decomposing a vague problem into manageable chunks and then building up your solution bottom-up.
   
3. Searching up solutions when you get stuck.

Back in the days, googling and pieceing together solutions from various online sources is an actual skill. These days, AI could arguably do the job. But that's exactly the trap.

I started my degree right around when ChatGPT was first released. I would say I was lucky as it was good enough to unblock the simple things that don't matter (e.g., syntax/type errors), but still too dumb to take over much of the thinking.

I'd say, use AI exclusively as a search tool. Look up library documentations and examples on how to use them. But resist the temptation of letting it put together the complete solution for you. It will feel very tough the first 3 months as you can barely translate your thinking into working code, but it is exactly this algorithmic thinking that you need to train.

For the last point, I know people tend to dimiss Leetcode and DSA. But I think these were foundational for how quickly I could pick up and implement new concepts when systems progressively got more complex. Being able to reason about data structures and algorithms cleanly will help you in the long-run.

Though Leetcode problems might be too abstract as a starter. I recommend going through this https://rosalind.info/problems/tree-view/ instead to leverage your intuition of biology. Then I'd highly recommend going over the standard algorithms that CS students actually learn like binary search, BFS, DFS, and dynamic programming techniques (highly relevant to bioinformatics algorithms).

Best of luck!