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Step by step

1. Upload data — a CSV/TSV with identifiers in the first column. For fold-change data, include a log2FoldChange (or lfc) column; for counts/abundance, the numeric columns are averaged. You can also click Or use sample data to try it instantly.
2. Choose a pathway source:
   • Reactome — fetches the real, layout-complete SBGN export for the pathway.
   • KEGG — fetches KGML. Toggle between Image overlay (your data over KEGG’s official diagram) and Vector (KGML) (clean, editable SVG).
   • Custom SBGN File — upload any .sbgn map (Newt, Reactome, VANTED/SBGN-ED, CySBGN…).
3. Select species & pathway. Pathways that contain genes from your data are grouped at the top of the list.
4. Set the data type — Normalized counts / abundance (sequential scale) or Log2 fold change (divergent scale).
5. Generate. The map renders in your browser. Or click Try offline demo for a bundled example with no network.
6. Explore & download. Pan, zoom, hover a coloured node for its values, search to highlight, and Download SVG.

Identifier matching

Source	Matched against
SBGN (Reactome / custom)	glyph label (whole label, then word tokens)
KEGG genes	node symbols (graphics name, e.g. ENO1) and ids with the organism prefix stripped (ath:AT1G12345 → AT1G12345, hsa:7157 → 7157)
KEGG compounds	KEGG compound ids (C00022)

So gene symbols, locus ids and Entrez ids all work for KEGG. Matching is case-insensitive.

Import from NASA OSDR

The Import from NASA OSDR panel pulls differential-expression data straight from the Open Science Data Repository:

• Curated collection — pick a ready-made, pre-sliced study (e.g. OSD-120 CARA — Arabidopsis spaceflight) and it imports + projects onto the suggested KEGG pathway in one click.
• Live OSD accession — type an accession (e.g. OSD-120), Fetch, choose a contrast and gene-ID column, and Import. The app slims the DE table to your contrast in the browser. Very large tables (e.g. OSD-120’s 252 MB, 132 contrasts) are better added via the offline pipeline (tools/osdr_slim.py) — they then appear in the curated collection.

After importing, pick a KEGG pathway and Generate (curated entries auto-select one).

Try it with Arabidopsis (KEGG)

Fastest: click 🌱 Load Arabidopsis (KEGG) sample in the app — it loads real Arabidopsis genes and renders the live KEGG Phenylpropanoid biosynthesis map (ath00940) in one click.

Or do it manually with the ready-made example: examples/arabidopsis_kegg_demo.csv — real Arabidopsis AGI locus IDs (AT#G#####) with log2 fold-change values.

1. Download it, then Upload Gene Data.
2. Database KEGG, species Arabidopsis thaliana (ath).
3. Pick a pathway, e.g. Phenylpropanoid biosynthesis (ath00940), Glycolysis (ath00010), or Plant hormone signal transduction (ath04075).
4. Generate — your genes light up on the KEGG map.

Verified mapping (demo set → real KGML gene nodes): ath00940 → 16, ath00010 → 9, ath00710 → 9, ath04075 → 9.

Notes

• KEGG REST has no CORS headers, so KGML and images are fetched via a free public proxy.
• Databases without SBGN-ML/KGML (MetaCyc, SMPDB, PANTHER, MetaCrop) can still be visualised by exporting them to SBGN (e.g. via Newt) and using Custom SBGN File.

🚀 Launch the app