ideogram VS Task

> Where are the polished, powerful design tools for biology

User interfaces for biology have drastically improved over the last 10 years.

Domain-specific tools like genome browsers, protein viewers, or phylogenetic explorers [1-3] almost all look and feel a lot better than they did in 2012.

The biggest exception here is UCSC Genome Browser, which has an old-school design and web technology stack. That said, it's steadily added features over the years, has substantially sleekened UX in its periphery, and remains widely used.

There are also bespoke visual design resources for biology applications that are good and getting better, like BioRender and PhyloPic [4-5]. There are multi-tiered packages like Dash Bio that wrap biology components together. There's Blender biology community, too!

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1. Genome browsers and components: https://jbrowse.org/jb2/, https://www.ncbi.nlm.nih.gov/genome/gdv, https://igv.org/app, https://eweitz.github.io/ideogram

2. Protein viewers: https://pymol.org/, https://nglviewer.org/ngl/

3. Phylogenetic explorers: https://clades.nextstrain.org/

4. https://biorender.com/

5. http://phylopic.org/

6. https://github.com/plotly/dash-bio, https://dash.gallery/Portal/?search=[Pharma]

I'm more interested in read speeds than write speeds. I have about 2 MB of data that I fetch, then parsed and transformed into a heavily nested object for easy look-up by various types of keys.

In my brief experiment, it was 12% faster to read from the web Cache API, re-parse and re-transform that heavily nested object than to read the fully transformed object via IndexedDB. That surprised me! My understanding is that IndexedDB does a structured clone as part of the read, which I suspect is the main cause of slowness of IndexedDB relative to the Cache API approach in my use case.

Related commits to reproduce that finding are in [1], specifically [2].

[1] https://github.com/eweitz/ideogram/pull/285

I created Ideogram.js, a JavaScript library for chromosome visualization [1]. Ideogram supports drawing and animating genome-wide datasets, enabling a variety of genomic views [3].

[1] https://github.com/eweitz/ideogram

[2] https://eweitz.github.io/ideogram

So many tools in this space! This one looks a little bit like go-task, but it seems maybe better for production workflows because if timeout support, while go-task seems more aimed to command line work/makefile replacement.

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https://github.com/go-task/task

View on GitHub

A similar tool is `task` https://taskfile.dev/ . It is quite capable and also a single executable. I've grown to quite like it.

check out tasks - a bit of a learning curve but arguably more powerful imo

That's when I came across taskfile.dev. Task is an automation tool designed to be more accessible than other options, such as GNU Make.

Full disclosure, I did not review Conda or Hatch fully. Not that there is anything explicitly wrong with either of them. Conda is too specific to the scientific community for my general taste. Hatch seems to go well with Conda and also uses the PyProject manifest as well. It's nice that it gives you several built in tools, similar to commit hooks, but I tend to like to roll my own via a Taskfile and run them with Poetry.

Taskfile is a tool for streamlining repetitive development tasks. It helps automate activities like building, testing, and deploying applications. Unlike Makefile, Taskfile uses YAML for configuration, making it more readable and user-friendly.

9. We test everything with another promotion which runs make targets which build docker containers to run python scripts (pytest)

This is also built by a complicated web of wildcarded makefile targets, which need to be interoperable and support a few if/else cases for specific components.

My plan is to migrate all of this to something simpler and more straightforward, or at least more maintainable, which is honestly probably going to turn into taskfile[0] instead of makefiles, and then simple python scripts for the glue that ties everything together or does more complex logic.

My hope is that it can be more straightforward and easier to maintain, with more component-ized logic, but realistically every step in that labyrinthine build process (and that's just the open-source version!) came from a decision made by a very talented team of engineers who know far more about the process and the product than I do. At this point I'm wondering if it would make 'more sense' to replace it with a giant python script of some kind and get access to all the logic we need all at once (it would not).

[0] https://taskfile.dev/

task - a task runner and a replacement for make