A safety net, not a substitute. Every result must be confirmed by standard laboratory antimicrobial susceptibility testing and reviewed by a qualified healthcare or laboratory professional.
How GATCHA turns a genome into an antibiotic-response report, what each stage of the pipeline contributes, and how to move around by keyboard.
FASTA: an assembled, quality-checked genome (.fasta / .fa / .fna, up to 50 MB).
Species: the declared organism. Anything outside the validated set (E. coli, K. pneumoniae, S. aureus) is scored as no-call.
Antibiotic panel: the drugs to score. Unsupported drugs are hidden, never guessed.
Uploaded sequence data is processed as data only. It is never forwarded to any language model or third party.
Prediction: one of likely_to_work, likely_to_fail, no_call.
Calibrated confidence: a resistance probability after Platt or isotonic calibration on held-out genetic groups.
Evidence: the AMR features detected (gene, mutation, plasmid) with their database source, and whether each is a known determinant or only a statistical association.
Familiarity + OOD: how close this genome sits to the training distribution.
Decision receipt (JSON): a full audit trail of rules fired, model versions, thresholds, and no-call reasons.
Screens species and QC before any model runs. Fails fast on the wrong organism, a poor assembly, or contamination.
Output · pass / fail with a reason
The gold-standard annotator turns the assembly into known resistance genes, point mutations, and plasmids, collapsed into a hierarchical feature record.
Output · feature record (genes, mutations, drug classes)
One interpretable model per antibiotic (logistic regression or XGBoost, chosen by grouped cross-validation), then Platt or isotonic calibration so the confidence means what it says.
Output · raw + calibrated resistance probability
A drug-target gate plus three triggers (low confidence, conflicting evidence, out of distribution) downgrade weak or risky cases to an honest no-call instead of a guess.
Output · final call, no_call_reasons[], lab_confirmation_required
Deep-learning encoders (ESM-2, DNABERT-2, a multi-task net) were benchmarked offline on K. pneumoniae for the record, but the live path stays on the fast, auditable calibrated baseline.
g is a leader key: tap g then a letter within about a second. Shortcuts are ignored while typing in inputs.