FROGS troubleshooting
Resolve common frogs pathway analysis issues including validation errors, low significance, and slow runs.
This page helps you resolve the common issues with frogs pathway analysis — validation errors from mismatched compound or gene identifiers, large dropped-inputs reports, low significance in the results, and slow runs.
Not available in chat
FRoGS is deprecated on the platform and its Modal backend has been taken offline — it is not registered with the orchestrator, so chat cannot call it. Existing FRoGS job results should be treated as unverified: a still-registered legacy form of the tool faked its results, returning mock predictions as SUCCESS regardless of input.
Validation errors
- Ensure compound IDs match those used in target annotations and signatures.
- Use consistent gene identifiers (all HGNC or all Ensembl) across targets, signatures, and pathways.
- Confirm pathway sets are present and formatted correctly if you uploaded custom lists.
Dropped inputs report is large
- Deduplicate compounds and signatures before upload.
- Check the report for missing targets or malformed signatures; fix and re-run.
- Tighten minimum target coverage only after validation passes cleanly.
Empty or low-significance pathways
- Loosen p-value/FDR cutoffs slightly to confirm signal exists.
- Broaden pathway set (e.g., include GO + KEGG) to increase coverage.
- Verify signatures have sufficient completeness; raise tolerance if needed.
Slow runs
- Reduce pathway set size or batch size for large signature matrices.
- Limit to high-confidence signatures first, then expand once parameters are stable.
Export issues
- Re-run exports after changing filters or cutoffs; exports reflect the latest results.
- Keep compound-to-pathway contribution scores for downstream selection and wet lab follow-up.
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Outputs and interpretation
Interpret frogs pathway rankings, enrichment scores, and compound-pathway contribution metrics.
Targets & Modalities overview
Guide RNA design and off-target analysis, PROTAC linker and ternary complex modeling, tumor mutational burden, and genome-scale flux balance analysis.