Tuna Birgun

omicau (Omics Audit) is a reproducible, leakage-safe command-line tool, with an optional no-code web interface, that audits a multi-omic dataset before anyone trusts a predictive result. Given one matrix per omic layer (RNA, protein, methylation, and so on) plus a clinical outcome, it aligns the layers, locks them with a value-level SHA-256 provenance hash, and tests for the failure modes that make omics machine learning non-reproducible: information leakage, batch confounding, target-linked missingness, and modality redundancy. It then benchmarks whether combining layers beats the best single layer, under group-aware cross-validation that keeps every subject's samples on one side of the split, and runs three shuffled-label control baselines that gate the result — when a control scores above chance, omicau flags leakage and refuses to certify the score. It writes a one-page executive verdict for clinicians and a research tab with confidence intervals, calibration, and per-layer diagnostics, alongside a DOME methods block and a model card; it runs fully offline, installs from PyPI, and was built with Claude Code. I validated omicau on eight public datasets spanning human, mouse, zebrafish, yeast, and Arabidopsis, across classification, regression, and survival, and checked every result against the published literature. On TCGA-BRCA (PAM50 subtype, RNA+CNV, n=493) it reached AUROC 0.953 with controls at chance and certified the result, while its leave-one-layer-out test put the gain of fusion over RNA alone at +0.006 with a 95% confidence interval spanning zero, so it reported that one layer suffices; on an Arabidopsis Botrytis-infection set (n=12) it reached a perfect AUROC of 1.000, yet the shuffled-target control also beat chance (0.89), so it declined to certify — the correct call when the sample is far smaller than the feature count. This matters because leakage, batch effects, and redundant layers are a documented cause of irreproducible omics machine learning; integration methods (MOFA, MOGONET) and reporting checklists (DOME, TRIPOD+AI) exist, but no tool audits a dataset before modeling for these failures and then declines to certify a score it cannot trust. omicau fills that gap: it tells a researcher — a genomics postdoc, a lab, a clinic — when not to trust their own model, backed by a provenance hash anyone can recompute.