GenomeIDE
Why Genome Editing Decisions Must Be Replayable
If you cannot deterministically replay a decision later, you cannot defend it when results drift.
Replayability makes genome editing decisions defensible: deterministic artifacts, provenance, and evidence bundles that can be verified months later when results drift.
System of Record →Section 1
What the tool is
A replayable decision is one that can be rerun later with the same inputs to recover the same artifacts: reports, intermediate evidence, manifests, and hashes.
Section 2
Why scientists care
Genome editing programs fail quietly when decisions live in Slack, notebooks, or ad-hoc scripts: teams cannot prove what was known at decision time, and drift looks like incompetence.
Section 3
How Helix solves it
Export evidence bundles with explicit assumptions, IDs, and artifact hashes
Section 4
How the algorithm works
Replayability depends on deterministic execution: stable ordering, fixed seeds, controlled floating-point behavior, and explicit versioning of every dependency.
Section 5
Try it in Helix Studio
Start by reviewing an example evidence bundle and the verification commands; then run a pilot on one real Edit Program to validate replayability in your environment.
Section 6
FAQ
Is replayability the same as reproducibility?
Reproducibility is the mechanism; replayability is the reviewer-facing promise: you can rerun later and recover the exact decision artifacts, not just something close.
What breaks replayability most often?
Hidden defaults: unpinned reference versions, toolchain drift, and unstated assumptions that change behavior when the team, machine, or dataset changes.
Do replayable decisions require cloud infrastructure?
No—replayability can be offline. What matters is deterministic artifacts, provenance, and a verifier that can replay and diff outputs.