Hey biostars!
I'm a software engineer by profession and training. After working in the software industry for about two decades, I wanted to explore how my training and experience could help other fields in fundamental life sciences and got really interested in bioinformatics.
After going through numerous courses online, primers on molecular biology and learning some fundamental algorithms over the last couple of months, I am feeling lost when it comes to connecting the early learnings to the real-world use cases and goals.
How do I connect the dots to a grand vision of drug discovery, DNA analysis, disease etc. I'm not even sure at this point if I'm going in the right direction. What I have started to feel is the absence of a domain expert, a molecular biologist perhaps, who I can partner with.
Really looking forward to the guidance from community.
Thank you! ~J
This (or a variation) question is asked every so often. You may want to take a look at these past threads and the discussions in there to get some ideas.
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Despite that computational-driven data analysis is often labelled "bioinformatics", it actually has little to do with computer science at all. Sure some good coding practices, version control and containerization can help, but it's not the driver skills. Rather, it is all about understanding the underlying biology beyond what some online courses or yt videos can teach. Meaning: Knowing the literature of the field you work in (cancer, infection, development, signaling, whatever, and there are a hundred subsets of each), understanding which analysis makes sense and what outcomes may mean. Developing a feel for what is publishable and what is not. I would
Analysis will require extensive retraining and I kind of don't see how there would be a market for someone as senior as you, with essentilly no biological knowledge, given that there will be people with a lot more biological knowledge, but 20 years younger. Learning coding basics these days is not hard, more and more biologists pick it up. I mean damn, we have "old-fart" postdocs shy of computers historically, that now analyze OMICS data with the help of ChatGPT, and with some guidance here and there it is really done well and meaningfully. Our experience, collaborating with pure IT people, was always that communication was extremely difficult and analysis often went dead-end since the common ground was missing in terms of transferring the "how's", and the "why's". What we see all day is that an experienced biologist, say with the help of AI-driven coding, can do more meaningful analysis faster and better than anyone, regardless of coding skill, who has no domain knowledge in what we do. Seriously, I would really exploit your skillset and dive into roles for software development for bioinformatics purposes.
ATpoint, you are absolutely bang on. In fact, my intention is not to switch careers but to connect with this field and build tools/systems that help with the larger vision of drug discovery and others. Building scalable and reliable systems is something that I excel at and I want to combine that with the problems that the users in life sciences domain are facing.
Having an intuition about the fundamentals and how they connect to the end goal is paramount while building systems. This is the primary reason I want to learn bioinformatics and then partner with a molecular biologist to build what is needed by the industry/academia/enthusiasts/public in general.
The lectures by professor langmead at https://www.langmead-lab.org/ are exactly what I was looking for. Thanks to GenoMax for pointing in that direction.
Thank you, genomax, for the recommendations! Very helpful!