How to read the output

1. Check the coverage line first. It reports how many of your IDs were recognised and, of those, how many carry GO or KEGG annotation. Only annotated genes can ever appear in a result, so a low annotated count caps everything below it.
2. Read the fold-enrichment, not just p. GeneRatio is the share of your list in a term; BgRatio is the share of the background genome in the same term. A small p with the two ratios nearly equal is a large-list artefact.
3. Use the corrected value. With thousands of GO terms tested, raw p alone is not evidence. Rank by p.adjust (BH by default). Raise max size if very broad terms were filtered out, and lower it to suppress them.
4. Expand a term to see its genes. Click any row to list the intersecting genes — that intersection is what you carry into the next experiment.
5. For KEGG, read the class column before the p-value. KEGG pathways are defined mostly on human and animal metabolism. In a non-model plant, orthology transfer pushes plant genes onto those maps, so Human Diseases and Organismal Systems entries appear as artefacts — a mulberry gene set "enriched" for Pathways in cancer is not a result, it is a formatting coincidence of KEGG's namespace. These classes are hidden by default and the toggle above restores them. What survives the filter — Flavonoid biosynthesis, Glutathione metabolism, Stilbenoid biosynthesis — is where the biology is.
6. Treat GO cellular component and molecular function with the same suspicion. GO is species-neutral, so whatever appears is genuinely annotated — but the annotation was transferred from orthologs across all kingdoms by eggNOG-mapper. Non-plant terms such as neuron projection cytoplasm or periplasmic space do occur. They are usually weak and sink below the top of the ranking; if one rises high, go back to the gene list before believing it.
7. Gene ID naming. Since 2026-09-12 the whole platform uses the manuscript haplotype names TWCG_HapA1 / TWCG_HapA2 / TWCG_HapB. The two former schemes overlapped on the single string TWCG_HapB, and that is the only case this tool cannot resolve for you:
   • If your list contains any TWCG_HapC. or bare TWCG_HapA. ID, it is read as pre-migration and the whole list is converted automatically — TWCG_HapA. → TWCG_HapA1., TWCG_HapB. → TWCG_HapA2., TWCG_HapC. → TWCG_HapB..
   • If your list is made up only of TWCG_HapB. IDs, it is taken at face value — TWCG_HapB now means the M. alba subgenome (27,269 genes). A pre-migration list of TWCG_HapB. IDs therefore has to be rewritten to TWCG_HapA2. by hand; the tool will not guess, because guessing wrong would silently return the other subgenome's result.
   • A list mixing both schemes is rejected with an explicit error rather than silently mis-read.