Evidence-guided research support

Diagnose data, logs, configurations, and results together.

OmniGenet first runs reproducible checks on each artifact, then asks AI to explain the structured evidence, prioritize fixes, and identify the next reproducible action.

1Collect evidenceUpload files or select an existing Job.

2Run deterministic checksParse structure, errors, warnings, metrics, and provenance.

3AI synthesisExplain priorities and next actions from the checked evidence.

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Download a complete AI diagnostic case

Load all four files into the matching upload cards instantly, or download the ZIP for manual testing.

Baseline

Healthy GWAS run

Valid summary statistics, successful log, standard configuration, and a completed result.

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Root-cause challenge

Failed PRS run

Invalid variants, missing PLINK/reference files, low memory, ancestry mismatch, and a failed result.

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New diagnostic

Add the materials that describe the problem

Up to 8 files · 50 MB each

DATA Research data CSV, TSV, VCF, summary statistics, phenotype, marker or omics tables No data file selected Or load a built-in example LOG Run logs Snakemake, Python, R, PLINK, PRSice, DMU or scheduler stdout/stderr No log selected Or load a built-in example CFG Configuration OmniGenet YAML, method settings, manifests, software and resource configuration No configuration selected Or load a built-in example RESULT Structured results analysis_summary.json, metric tables, method outputs or exported summaries No result selected Or load a built-in example

Existing OmniGenet Job ID

When supplied, OmniGenet adds the job status, log tail, structured summary, method eligibility, and result-file inventory.

What should OmniGenet focus on?

AI response Use configured remote model

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