MiGA generates a clustering-based indexing of databases using ANI distances (for clade projects) or AAI distances (for all other projects). This indexing enables quickly searching databases with query genomes.
The AAI or ANI values are transformed to distances (1 - identity), and the all-vs-all distance matrix is used to generate a k-medoids partition (PAM: Partition Around Medoids). k is selected to simultaneously optimize for maximum Silhouette average width and minimum Silhouette negative area, between 2 and 100 (or the number of genomes minus 1, whichever is smaller). Once the partitions are defined, the same algorithm is applied recursively to each partition with 8 or more genomes. The resulting clustering-based indexing is used to speed-up query searches. In some cases it can also be used as de novo typing scheme, in particular for ANI distances (clade projects).
In addition to the above clustering-based indexing, MiGA clusters genomes by Markov Clustering (MCL) using all ANI values above 95% as edges. The result is a collection of discrete genomospecies. The list of genomes per genomospecies is sorted by medoid-ranking, in which the first genome has the minimum average distance to all other genomes in the genomospecies.