poly VS seq

- https://github.com/TimothyStiles/poly: Poly is a fast, well tested Go package for engineering organisms.

Poly: Fast Go package for engineering organisms

You might want to look at computational biology. Jim Allison won the Nobel Prize back in 2018 for his work on immunotherapy for cancer and there's a lot of basic research work to be done to perfect this approach. Epigenetic clocks are really interesting too (see Steve Horvath's work). Also, there's synthetic biology, where you could, for example, explore this package that's written in Go: https://github.com/TimothyStiles/poly

Check out poly. It’s written in go and I’m using it for one of my projects too. The goal is that we should have high performance libraries that we can use knowing what people are working on the forks will give the community a leg up.

The most popular bioinformatic package I've seen in go is poly.

I write synthetic biology software for a living and maintain this open source, Go package for engineering DNA that has high test coverage and a nice little dev community around it.

https://github.com/TimothyStiles/poly

A large part of my project's community are devs that want to get into the field but can't tolerate the ridiculously low pay, laughably bad management, disrespect, and what amounts to 40+ years of technical debt that's endemic to biotech software.

I've had companies here in the Bay Area offer me 100K a year with a straight face. I've had companies during interview tell me they're looking for someone to help, "set up GitHub". I've seen job listings for low paid web dev positions require applicants to have PhDs.

The reality is that except for a growing handful of places management straight up won't know the difference between IT and software engineers. It's what I call the naive buyers problem.

The demand for software engineers in biotech is generated by naive buyers that don't know what they need, why they need it, or how to get it.

Benchling and Recursion Pharmaceuticals have reputations in the industry of paying, "standard software salaries". So do the research divisions at places like deepmind/microsoft/google but in my experience there's even new multi-billion dollar institutes where senior management has never even heard the term devops.

Most places advertise for "data scientist", positions or some analog, instead of software engineers. This is mostly because upper management has never met an actual practicing software engineer in a professional setting. Many come from academia where the culture and work requirements heavily disincentivize standard software engineering practices.

It's also not uncommon for a biotech company to either have a very under qualified CTO whose main programming experience is what they learned doing ML research like stuff during their PhD or not even have one at all which has huge downstream consequences.

This week a software engineer trying to make the switch to biotech actually DM'd me to ask why they were seeing a ton of data science / ML job positions but no software engineering / devops positions.

They were worried that these companies were trying to save on costs by forcing their data scientists to create infrastructure but it's actually worse than that. Most of these companies aren't even aware that there's supposed to be infrastructure.

Despite all of this the future is looking better and I'm starting to find new companies and positions that are well... reasonable. I learned about this thread from a friend at a party last night that works at one of these companies. There's a small, strong new wave of companies and developers out there pushing biotech software forward. Hopefully some (including myself) make it big while pushing the idea that better tech equals better biotech.

It is more like the X Y Z W. However, the X Y Z W bits I am working on as well (https://github.com/TimothyStiles/poly , https://github.com/TimothyStiles/allbase , trilo.bio, freegenes.org). Going for fully automated "make bacterium X produce molecule Y", but still a while away (but surprisingly not THAT far off)

> Seq is a Python-compatible language, and the vast majority of Python programs should work without any modifications

https://github.com/seq-lang/seq

Seq: A programming language for high-performance computational genomics\ (17 comments)

They support both, and will deprecate Python 2 style soon.

https://github.com/seq-lang/seq/issues/223