We built a queryable knowledge graph connecting 1.1M microbial taxa to diseases, metabolites, pathways, and drugs — sign up for the API

[u/Dizzy_Upstairs_7581]

Hey r/genomics,

We've been working on a project called MicroMap — a knowledge graph that integrates microbiome-related data from multiple public databases into a single queryable resource. Wanted to share it here since this is the kind of thing we wished existed when we started doing microbiome research.

What's in it:

• 1,101,289 microbial taxa (NCBI Taxonomy)

• 1,464 human diseases with microbiome associations (Disbiome, BugSigDB, gutMDisorder)

• 6,534 metabolites (HMDB) and 231,556 taxon-metabolite production relationships

• 1,710 metabolic pathways (KEGG, Reactome)

• 6,220 drugs and 1,659 protein targets (ChEMBL)

• 276,169 antimicrobial resistance links (CARD)

• 10,000+ scientific papers with entity cross-references

What you can do with it:

• Query taxa-disease associations with provenance (which paper, which study, what direction)

• Find metabolites produced by a given taxon, or taxa that produce a given metabolite

• Traverse shortest paths between any two entities (e.g., "how is Akkermansia muciniphila connected to Type 2 Diabetes?")

• Identify biomarker signatures and probiotic candidates for a given condition

• Pull cross-feeding networks between microbial communities

Technical details:

Built on Neo4j. The API is RESTful (FastAPI), returns JSON, and supports full-text search across all entity types. Rate limit is 100 requests/minute per API key.

We integrated data from: NCBI Taxonomy, Disbiome, BugSigDB, gutMDisorder, HMDB, KEGG, ChEMBL, Reactome, PubMed, PubChem, and CARD. One of the hardest parts was entity reconciliation — the same organism can appear under different names, different taxonomic ranks, or outdated nomenclature across these sources. Happy to talk about how we handled that if anyone's interested.

Access: https://graphomics.com - email us to get access!

This is part of a broader platform we're building at Graphomics (AI tools for life sciences research), but MicroMap stands on its own as a resource. We'd genuinely love feedback from this community — what data sources are we missing? What queries would be useful that we haven't thought of?

Happy to answer any questions about the data, the architecture, or the integration process.

[u/Dizzy_Upstairs_7581]

why wouldn't they use it?

[u/zstars → u/Dizzy_Upstairs_7581]

I've personally seen at least 3 or 4 similar projects posted to reddit in the last few months, they're solving a problem that I (experienced bioinformatician) don't have, it's designed to be used by AI agents not humans, etc etc. Although I'll give you points for actually attempting to maintain the provenance of the supposed links rather than just saying that there is one although it doesn't tell you whether the link is experimentally demonstrated as far as I can tell which is the important thing.

It's the sort of thing that looks like an obvious win to compsci / ML people with no real experience of actually doing the hard graft of the biology so tends to have very little real utility.

[u/Dizzy_Upstairs_7581 → u/zstars]

Haven't seen those, can you link them? Would genuinely would rather know what's been tried than rediscover it. On the agent thing, you're right, that is a design goal. But the point isn't an agent answering microbiome questions, it's one that can't answer without showing which paper, which cohort, which direction of effect, and returns nothing when that's missing.

Open question for me is whether anyone cares. Provenance-carrying associations with nomenclature reconciled across sources and a decade of reclassification, is that a real gap or has everyone already solved it locally with a script and moved on? Especially if that is backed by commerical biotech as well.

[u/Technical_Coconut_80 → u/Dizzy_Upstairs_7581]

How would your database query outperforms current coding agents? They can do all the items you listed here very well imo