Genome feature exploration using hyperbolic self-organising maps

Abstract — The advent of sequencing technologies allows to reassess the relationship between species in the hierarchically organized tree of life with respect to pat-terns in their genomic signatures. Self-Organizing Maps (SOM) in Euclidean and hyperbolic space are applied to genomic signatures of 350 different organisms of the two superkingdoms Bacteria and Archaea to link the sequence signature space to pre-defined taxonomic levels, i.e. the tree of life. In the hyperbolic space the SOMs are trained by either the standard algorithm (HSOM) or in a hierarchi-cal manner (H2SOM), which is naturally supported by a lattice structure in hyperbolic space. Genomic signatures containing estimated statistics of oligonucleotide of certain lengths are used as features for training. For evaluating the SOM performances, distances between organisms in the feature space, on the SOM grid and in the taxonomy tree are compared pair-wise using a correlation measure and Spearman’s ρ. We show that the structure recovered using the different SOMs reflects the gold standard of cur-rent taxonomy. The distances between species are better preserved when using the HSOM or H2SOM which makes the hyperbolic space better suited for embedding the high dimensional genomic signatures.