SNR was deviced to extract the most statistically relevant results out of a pool of collections of element families. It was used to leverage the results from applications of CV (Methods 6.7) with various sets of parameters, as well as those stemming from reshuffling similarity matrices with the different sequences in the corresponding ensemble. As shown in SI Fig. , SNR takes as input a pool of collections of families, and outputs a single collection. In step a each family is expanded into a new collection, by gathering the most similar family in every other input collection (using Tanimoto similarity ). In b each of these is again reduced to a single family, containing the elements present in at least 50% of the families in this collection; this results in a new pool of collections, and this process is repeated M times using this result as the input (step d). In step e the output pool of collections is collapsed into a single collection. After each step, duplicates, empty, or one-element families are removed (steps c and e). The resulting collection is given as the output.