Long:
This is the bit that makes biology awesome to me, so excuse me for the small essay ;-)
Proteins are basically extremely advanced nanomachines which work together in larger systems to ultimately form a cell. Having a listing of all the proteins in a cell (the sum of the parts) is insufficient to grok the whole, but it's pretty darn important. The abilities and limitations determine and constrain what a cell can do, and ultimately influence what organisms and ecosystems are and are not capable of. Which is a big chunk of the science of biology.
Uniprot lists many/all of the genes/proteins that have been decoded so far. It's a bit odd to call that a "niche" in the context of the field of biology.
I'm not going to discourage you though: Proteins and protein systems are pretty darn awesome!
Example:
(using KEGG rather than uniprot, since it's got graphical maps, which is handy to get an intuition)
For instance, if you want to know why you need to eat and why you need to breathe (flowchart for what your cells do with starch and oxygen):
* https://www.genome.jp/pathway/map00500 start by finding starch on this map (gets split into glucose)
* https://www.genome.jp/pathway/map00010 which gets broken down into 2* pyruvate
* https://www.genome.jp/pathway/map00020 which gets processed
* https://www.genome.jp/pathway/map00190 and ultimately "burned" with oxygen.
Each step is a 'chemical reaction catalyzed by proteins'[1] (in the rectangles). You can dig in deeper to find your actual source code: Say we click on a random step (in this case near the top of glycolysis on map 00010)
* https://www.genome.jp/entry/K01810+K06859+K13810+K15916+5.3....
At the bottom you can find the gene listed for Homo Sapiens (HSA)
* https://www.genome.jp/entry/hsa:2821
And this lists the amino-acid (AA) sequence for the protein, and the nucleotide (NT) sequence found in humans. Since this is highly preserved functionality, that's probably (almost) exactly the source code that you have in each of your cells.
KEGG is nice to get an overview of some of the pathways that are fully understood with the maps.
[1] calling it a "chemical reaction" is sort of underselling many proteins. Proteins can have moving parts and can work together. I prefer to think of them as sophisticated nanomachines.
I actually checked uniprot, yes, it lists proteins (probably most of them), but ontology is raither narrow, it has few dozens properties, you can't for example query that DB with question: give me diseases which can be attributed to broken pathways synthesizing protein X, you would need to do a lot of manual work and check external databases of uncertain quality.
Another question is quality of that dataset, why it is so obvious that all those millions of pathways for hundreds thousands pathways researched and described with 100% accuracy?
nod
Uniprot is more useful if you're looking for the actual "bare metal" NN and AA sequences. Which is rather important in its own right, obviously: Sooner or later you DO need the actual sequences if you're going to do something with them in real life.
But uniprot doesn't -itself- give you an understanding of what that code is then doing.
Meta analyses? Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery.
Also the most valuable knowledge is not in any publication. If you are reading about it in an article then you are already 2-3 years too late.
I search https://www.google.com/search?q=site%3Ageneontology.org+nlp+... and don't see anything meaningful.
> Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery.
my opinion is that it has to be top level tool in modern science which could easily sort out lots of bs and contradictions in reported results.
So, is your claim that all topics/niches/verticals/steps in pharma development and productions are covered by all those databases with absolute quality and perfect UI/workflow? I follow some studies about trials meta-analysis, and my impression is that end result is mainly produced by manual work of some low paid postdocs, which makes them not trustworthy.
> LOL!
I actually claim non-trivial expertise in this area (facts extraction from untrivial niche documents), and my observations is that even SOTA (aka results from top researchers) are hardly useable in real life, because this area is very hard. Could you support your "LOL!" with any references I could check?..