OmniRayn · Foundry Classroom · Genomics

Genome to
Candidate

Two ways to find a molecule in a genome.

Students Instructors
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OmniRayn/Genome to Candidate
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Method A · homology-anchored mining

Start from a known function. Find its relatives.

The move

Take a function you understand; search the genome for sequences that resemble it.

The cost

CPU-cheap — runs on a laptop, effectively $0 of compute.

The strength

Transparent — you can always say why a hit showed up.

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OmniRayn/Genome to Candidate
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Method B · de-novo scan (a genome language model)

No anchor — let the model find the unexpected.

The move

A genome language model surfaces candidates from scratch — no known relative.

The power

Can find what resembles nothing we already know.

The catch

Nothing to anchor to — the weakest-confidence tier.

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OmniRayn/Genome to Candidate
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The method sets the confidence tier

How you searched decides how sure you can be.

Method A → tethered

Grounded Class-level

Anchored to known biology — you can trust more of it up front.

Method B → unanchored

Novel

Exciting by construction, uncertain by construction.

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OmniRayn/Genome to Candidate
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A worked locus

What a candidate looks like leaving the pipeline.

Predicted function

Receptor-targeted regulatory peptide

Confidence tier
Class-level
Validation state
Hypothesis

The honest starting label: a prediction, not a result.

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OmniRayn/Genome to Candidate
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From sequence to testable hypothesis

The hand-off to mechanism screening.

A / B
Genome scan
A sequence with a predicted function.
Mechanism screening
What might it do, against which target?
Testable hypothesis
A claim a lab can falsify.
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OmniRayn/Genome to Candidate
Non-commercial
The license

"Computational predictions, pending wet-lab; structure-function framing only; not experimental evidence and not a disease, treatment, or efficacy claim."

Every candidate from genome mining is a starting hypothesis — not a result, however elegant the method.

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