ideogram VS lowdefy

> 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

I'm really enjoying reading through the docs and the tutorial. We've created Lowdefy, a config web-stack which makes it really simple to build quite advanced web apps. We're writing everything in YAML, but it has it's limitations, specifically when doing config type checking and IDE extensions that go beyond just YAML.

I've been looking for a way to have typed objects in the config to do config suggestions and type checking.. PKL looks like it can do this for us. And with the JSON output we might even be able to get there with minimal effort.

Is there anyone here with some PKL experience that would be willing to answer some technical questions re the use of PKL for more advanced, nested config?

See Lowdefy:

https://lowdefy.com/

https://github.com/lowdefy/lowdefy

Awsome! With Lowdefy we tried to build a low-code framework that works like code. We’ve developed a schema in which to define applications and we’ve built all kinds of apps for enterprise customers. Massive, advanced CRM systems, call centre solutions, ticketing systems, a light MRP, all kinds of survey apps and so many dashboards. Even our docs and our website are Lowdefy apps!

Give Lowdefy a try and reach out it you have any questions or want to see what is possible :) (We need to invest a lot more into content and examples, bootstapping is a grind!)

https://github.com/lowdefy/lowdefy

Also add Lowdefy onto the list https://github.com/lowdefy/lowdefy

co-founder here :)

100% this. And yes, good documentation takes a lot of investment but it pays off like compound interest. But with that done, it becomes even more important not to pull the carpet for no good reason, you are building a tower and documentation is at the foundation.

We’ve built Lowdefy [1] as an open source project and documented it with all effort, 200 pages of docs. I often forget why or how something works and then jump to the docs. This investment keeps on paying of as we use Lowdefy to build customer apps, new devs in the team typically take less than two week to get up to speed and start making contributions, the sharp ones, just a two or three days.

This year, we’re extended our documentation onto customer apps aswell, with flow diagrams, state machine definitions, detailed field level explication schema definitions, and end user test procedures. The key here for this documentation is detail. It should be easier to reach for the docs and the the answer, than to dive in the code and interpret it.

1 - https://github.com/lowdefy/lowdefy

https://github.com/lowdefy/lowdefy Co-Founder here.

Check out https://lowdefy.com/ they even have a sample survey app as one of their examples.

You can build an app that would do this

Lowdefy is a low code tool that uses AG Grid as a block component, allowing you to create apps which render data in AG Grid without a lot of coding knowledge. There is a Lowdefy example using AG Grid here.

Shameless plug, also check out Lowdefy - https://github.com/lowdefy/lowdefy

I’m also going to jump in here and say try Lowdefy https://github.com/lowdefy/lowdefy - co-founder here.

We take a different angle and believe that low code should still work like code. We focus on a developer first approach.