As we are - according to some expert opinions - living in the Century of Biology, I found it interesting to reflect on Go’s usage within the field.
Go has some great features that make it really well suited for biology, such as:
A relatively simple language that can be learned in a short time even for people without a CS background. This is super important aspect for biologists. Fantastic support for cross-compilation into all major computer architectures and operating systems, as static, self-sufficient executables making it extremely simple to deploy tools, something that can’t be said about the currently most popular bio language, Python.
TL;DR: We wrote a post on gopherdata.io, about the growing ecosystem of Go-based workflow tools in bioinformatics. Go read it here It is interesting to note how Google’s Go programming language seems to increase in popularity in bioinformatics.
Just to give a sample of some of the Go based bioinformatics tools I’ve stumbled upon, there is since a few years back, the biogo library , providing common functionality for bioinformatics tasks.
I’ve been following the development of D , Go and Rust (and also FreePascal for some use cases ) for some years (been into some benchmarking for bioinfo tasks ), and now we finally have three (four, with fpc) stable statically compiled languages with some momentum behind them, meaning they all are past 1.0.
While I have went with Go for current projects , I still have a hard time “totally falling in love” with any single of these languages.