Rust-Bio VS dash

Does anyone know of zig native libraries for bioinformatics (here is a Rust example https://rust-bio.github.io/ )? It seems as though one could pull in a lot of bioinformatics C libraries such as done with https://github.com/brentp/hts-zig.

e.g. Rust-Bio

I would start with the book and then rust-bio library. Rust is a pretty low level language compared to R/Python. It’s an especially good fit for writing efficient tools that make use of the kinds of algorithms / data structures that are implemented in rust-bio.

Absolutely! It already is, e.g., https://github.com/rust-bio/rust-bio. I'm moving from the academia/nonprofit world into industry bioinformatics, and I intend to use Rust as much as possible. I've already replaced as much of my Python as possible with Rust. I feel I'm able to create larger, more complex programs with Rust because I have the compiler to keep me from making common mistakes that are so easy to make in dynamically typed languages like Perl and Python. It might take longer to write a program initially, but I've started to create a library of functions I can paste together to do things like parse a positive integer, find a bunch of files with a certain file extension, search through data for a pattern, parse CSV files, etc. Writing my latest book has provided even more common patterns I keep finding I use over and over.

I think there is huge potential for Rust in bioinformatics, and there are already some great projects like https://rust-bio.github.io/. It seems industry is also hiring for these skills. This Nature article is a little old, but also covers why people in the field are looking for greater safety and performance. It's relatively easy to write a Python program to do bio stuff, but it's also very easy to get lots of things wrong or for the resulting program to be slow and/or impossible to extend and maintain. In the long run, I think it makes sense to write in Rust. Perl was king in biofx when I started, and I would not have predicted it being displaced by Python, so there's good reason to believe that Python may one day be eclipsed by Rust.

Well, someone mentioned https://rust-bio.github.io/

So generally stuff in this maths/numerical space. The term is a bit deceptive because it rarely means domain-specific science libraries like rust-bio even thought that might be what you think when you hear "scientific computing".

Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.

You could use something like Streamlit or Dash. In any case you will be accessing your app through the browser.

Useful list. Dash & bokeh as two more in the space

https://github.com/plotly/dash

I also heard of Dash which serves the same purpose I guess, but I think it has more to offer.

Plotly is a plotting library, and Dash is their open-source framework for building data apps with Python, R or Julia. (Dash also has an Enterprise version, but we'll focus on the open-source library here.)

Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.

looks like you can get it manually (albeit with a loss of interactivity) https://github.com/plotly/dash/issues/145

Shiny is a framework that makes it easy to build interactive web applications. Shiny was introduced 10 years ago as an R package. In his 10th anniversary keynote speech, Joe Cheng announced Shiny for Python at the 2022 RStudio Conference. Python programmers can now try out Shiny to create interactive data-driven web applications. Shiny comes as an alternative to other frameworks, like Dash, or Streamlit.