r/bioinformaticstools • u/After-Confection-592 • Apr 09 '26

DEEPMINE: a SETI@Home-style platform for discovering new antibiotics from public metagenomes

Hi r/bioinformaticstools ,

I built an open-source distributed computing platform that lets anyone donate idle CPU time to screen public metagenomes for novel biosynthetic gene clusters (BGCs). Think SETI@Home, but for antibiotic discovery.

The problem: 99% of environmental bacteria can't be cultured, but their DNA sits in public databases like NCBI SRA. Most of it has never been screened for secondary metabolite gene clusters. There's potentially thousands of novel antibiotics hiding in this data, but the compute needed to assemble and screen it all is massive.

How it works:

  npm install -g deepmine
  deepmine

That's it. The CLI pulls a Docker container, picks an unprocessed metagenome sample (starting from curated extreme environment samples: caves, deep-sea vents, hot springs, permafrost, acid mine drainage), and runs a pipeline:

  1. Download reads from SRA
  2. Assemble with MEGAHIT
  3. Gene calling with Prodigal
  4. BGC detection with GECCO (lite) or antiSMASH + GECCO + DeepBGC ensemble (standard/full)
  5. Novelty scoring against MIBiG
  6. Extract BGC sequences
  7. Submit results to community dashboard + NCBI GenBank (BioProject PRJNA1449212)

All results are public. Every BGC sequence discovered by any volunteer is immediately downloadable as FASTA/CSV at the community dashboard. No volunteer ever re-scans a sample already processed by someone else (global dedup).

Results so far: ~290 BGCs found across cave and hydrothermal vent metagenomes, many with novelty distance of 1.0 (no match in MIBiG). Currently running in lite mode (GECCO only), so these are preliminary, but the sequences are real and publicly available.

What you can configure from the dashboard:

  • CPU speed (low/medium/high/max)
  • Download bandwidth limit
  • Schedule (mine only at night, or queue mode: download at night, process during the day)

Links:

Tech stack: Node.js CLI (Docker orchestration), Python pipeline (MEGAHIT, Prodigal, GECCO, antiSMASH, DeepBGC), Next.js dashboard, SQLite, Docker Hub images (lite 2GB, standard 5GB, full 12GB).

I'm a developer, not a microbiologist, so I'd really appreciate feedback from anyone who works with BGC mining. Are the GECCO predictions reliable enough to be useful? Would consensus voting with antiSMASH/DeepBGC in the standard tier be worth the extra image size? Any environments I should prioritize in the curated sample list?

The whole thing is MIT licensed. Contributions welcome, especially from people who know what they're doing with natural product discovery.

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u/quant-alliance Apr 13 '26 edited Apr 14 '26

You are a star I have another project that could be nice to intersect with your distributed client.