Programming-Languages on Living Systems_

Why didn't Go get a breakthrough in bioinformatics (yet)?

Mon, 13 May 2024 17:05:00 +0200

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.
Fantastic support for concurrency and writing code as a set of parallell operations that streams data between them. Again, as opposed to Python. More on that later.
A large standard library that contains a lot of common needs, even for writing user interfaces and web servers.

Go has in fact garnered some use for bioinformatics tools over the years, with some indications that its use is increasing. Examples of popular tools and toolkits are SeqKit (a veritable swiss army knife for bioinformatics), the BioGo toolkit , the Gonetics package and lately the Gonomics package and finally the Poly package for synthetic biology. And this is besides heavy use in infrastructure-oriented projects like the Benthos stream processing tool, the Reflow pipeline tool and Pachyderm orchestration suite .

Go is growing in bioinformatics workflow tools

Fri, 10 Nov 2017 12:54:00 +0100

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. It was recently reviewed in two great blog posts (part I , part II ). Further, Brent Pedersen also wrote a little collection of Go based bioinfo tools, compiled down into a single static binary, called goleft , and finally, I there is the elprep tool , used to prepare .sam/.bam/.cram files for variant calling, which was published in a PLoS one paper . Just a few examples.

Combining the best of Go, D and Rust?

Sat, 11 Jun 2016 14:24:00 +0200

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. They all fulfill different subsets of my wishlist for an optimal compiled data munging language.