# beams-BGC

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Kayla Azima](https://cerebralvalley.ai/u/kaylazima)
- **GitHub:** https://github.com/kaylaque/bgcflow-plm https://github.com/kaylaque/bgclens
- **Demo video:** https://youtu.be/q9_J_grF_e4?si=wAEGHuPVIXr2tNh2
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/187

BGCFlow is the standard pipeline for mining biosynthetic gene clusters (BGCs) across bacterial pangenomes, but its output is hard to interpret without expert time, and every novelty-relevant stage (antiSMASH, MIBiG, Pfam, GTDB) can only recognize what already resembles something known — homology-matching is the ceiling. We built two additive tools to break past it. BGCLens is a read-only layer that ingests a finished BGCFlow project, recommends and runs the right statistics (PCoA, PERMANOVA, enrichment, diversity), renders figures, and writes plain-English interpretations via a guarded LLM pass that strips any invented numbers or accessions. BGCFlow-PLM adds an ESM2 protein-language-model side-channel that flags biosynthetic-core proteins with no close MIBiG neighbor — a remote-homology novelty axis the databases miss — without ever touching an antiSMASH call. Together they lower the expertise barrier to acting on genome-mining results and help researchers surface genuinely novel candidates instead of only re-finding the already-catalogued.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/187. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt