Four old maps were lying in different desk drawers. Each one marked genes linked to cellular aging, but the maps did not match: different names, different scales, different legends. A scientist hunting for a gene to test had to cross-check them by hand, at the risk of missing an important mark.

SenSASP stitched those maps into one. It took gene lists from four databases, CellAge, GenAge, SenMayo and Reactome, and brought them to a common denominator, or more precisely, to a common Ensembl identifier. That removed 210 duplicates: the same gene, recorded under different names in different databases, now appears once. All told, the map contains 1250 genes.

But the map is more than a merged list. Each gene gained three layers that none of the source databases had. The first layer is conservation: does the gene have a counterpart in mouse and zebrafish, meaning can its role be tested in these model organisms. The second layer is expression: in which of 54 human tissues the gene is active. The third layer is an interaction network: which other genes it connects to. Those connections add up to a network of 15 611 edges, and at its center sits TP53, linked to 368 other genes.

Now finding a gene for an experiment feels like querying a well-organized catalog rather than digging through an archive. A biologist can ask: "Which aging genes are present in both mouse and zebrafish, active in the liver, and linked to TP53?" and get an answer in a minute. Such a question used to take days.

SenSASP did more than stitch maps together. It drew trails between genes and marked where those trails run in different organisms. Four old maps that once sat in separate drawers have become a single one, along which you can chart a route to new drugs for age-related diseases.

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