r/learnbioinformatics

is bioinformatics (with ai) the way to go?

thanks

P.S. by 2100 is fine by me also.

1. Is coding absolutely necessary to build a career in bioinformatics or computational biology? I genuinely dislike coding. Are there roles where coding is minimal?

2. Can someone succeed in molecular docking or computational drug discovery with only basic Python and Linux knowledge?

3. How much coding do you actually do in your day-to-day job as a bioinformatician or computational biologist?

4. For someone from a biotechnology background, what's the biggest challenge when transitioning into bioinformatics?

5. Which programming language should I learn first if my goal is molecular docking and drug discovery?

6. How much mathematics and statistics do I really need for computational biology?

7. Is molecular docking becoming oversaturated, or is there still good demand in academia and industry?

8. If you could start over in bioinformatics today, what would you learn first?

9. Are there bioinformatics careers that focus more on biological interpretation than programming?

10. Can someone who isn't passionate about coding still enjoy computational biology, or is coding the majority of the work?

11. What skills do biotech graduates usually lack when applying for bioinformatics jobs?

12. How important are wet-lab skills if I want to work in computational biology?

13. What are the biggest misconceptions biotechnology students have about bioinformatics?

14. Would you recommend bioinformatics over a traditional wet-lab career in 2026? Why or why not?

15. What's one piece of advice you wish someone had given you before entering bioinformatics?

And folks that's all!

Thank you!

I'm genuinely interested in learning bioinformatics so hope i get to learn a lot from this sub!!!

am 21 btech biotechnology graduate here who wanna learn bioinformatics and have no idea where to start tbh.

i am a guy with no much knowledge in bioinformatics as ive studied bioinformatics as one subject and it covered only the basics like multiple sequence alignment, phylogeny, etc.

and nothing deep like docking, and no other genomics od proteomic tools.

so i wanna learn in bioinformatics and people tell me where to progress and where can I get the resources reg all these.

For context, I am pursuing a PhD in Genomics in Europe (I'm originally Canadian), specifically in using genomic sequencing and downstream tools to diagnose genetic kidney disease patients who go undiagnosed in clinic.

The lab I joined is just breaking into the space (theyre more into wet lab/proteins) and the labs that are bioinformatics heavy here are mostly evolutionary.

In the past half year I've been in my PhD, I've started to realize that I will probably have to learn and explore things myself much more than some of fhe other candidates. I dont come from a computational background; for my MSc I just happened to update my labs variant analysis pipeline to the point where it was quite different (but useful!). Currently I've been looking at some short read sequencing data and I have a pipeline (using a mix of python and R) that annotates from VCF --> filters for rare variants --> uses some online databases to filter for genes that are specific to the patient phenotype.

I want to upgrade my skills. I'm starting to learn Nextflow, but I also want to learn how to analyze long read and RNAseq data (I'm supposed to get patients with RNA and lrGS data as soon as the research center is equipped to perform it, which may be a while). I don't want to just learn though as I only have 4 years, so I was wondering if there were databases I could mine to potentially come up with work related to my project (perhaps something like gene/variant discovery)?

I apologize for what may be simple/dumb questions; whenever I try to explain my ideas to my PI I can see their eyes glaze over, and most of the diagnostic employees here are busy with hammering down a workflow for the research center. If anyone has advice on where to look or even papers to read I'd be eternally grateful.

Hey everyone,

Following up on my earlier post, a lot of you asked about machine learning content, so here it is.

15 chapters from what a model actually is through to foundation models like Geneformer and AlphaGenome. Capstone project on real single-cell data. Every chapter asks whether the complexity was actually necessary. Chapter 6 is entirely about when NOT to use AI.

Google Colab notebooks included. Free tier compatible.

Still completely free.

👉 https://bioskillslab.dev/