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 [6]. There's a Blender biology community, too!
I'm more interested in read speed than write speed. I have about 2 MB of data that I fetch, parse and transform into a nested object for easy look-up by various types of keys. It consists of 6 other objects, and I'd guess it's < 50 MB in total size.
In my brief experiment, it was 12% faster to read from the web Cache API [1], re-parse and re-transform that nested object than to read the fully transformed object using IndexedDB via idb-keyval [2]. That surprised me! I went on to learn that IndexedDB does a structured clone as part of such reads, which I suspect is the main cause of slowness in my use case.
Related commits to reproduce that finding are in [3], specifically [4].
WikiPathways supports advanced queries via their SPARQL API and UI. See [1] and [2]. I find WikiPathways nice because it lets logged-in users create and edit pathways, with a low barrier to entry.
I've been building a way to find related genes using biochemical pathways [3]. The source code linked there includes practical examples for fetching information on genes in those pathways, which you rightly note is needed for something compelling. That and other code there might help spark ideas for you on how to glue together various biochemistry and molecular biology APIs to achieve your vision.
I'm currently working on a way to drastically expand the set of organisms and pathways covered by WikiPathways. Yeast has 66 pathways there, compared to 1319 for human. By doing fast ortholog detection at runtime (using another SPARQL API, provided by OrthoDB [4]) I'm hoping to be able to convert relevant annotated pathways across organisms, e.g. human to yeast, mouse to rat, Arabidopsis to rice -- and vice versa.
I created Ideogram.js, a JavaScript library for chromosome visualization [1].
Ideogram supports genomic views to research and report findings on cancer, clinical variants, gene expression, evolution, agriculture, and more [2]. What previously existed for genome visualization was either focused on short genomic regions (e.g. genes) or complex to set up and maintain.
[1] https://undark.org/2024/07/09/informed-consent-human-genome-...