Kmer approaches have greatly developed in recent years, largely driven by the advent of next-generation sequencing. Their speed and automatism (annotation independent) are major advantages. The interest of applying k-mer approaches to study the mobilome is only starting to be explored, with many efforts oriented towards viral metagenomics [1, 2]. We evaluated the potential of applying simple k-mer approaches to understand the evolutionary history of mobile elements by focusing on viruses and plasmids from the domain Archaea, which includes specific and well-defined extrachromosomal element families [3]. Selecting more than 470 cell, virus and plasmid genomes, we explored the dataset structure by multivariate and statistical analyses of genome tetramer profiles. Basic genome network analyses served as reference. Mobile element families, genome GC contents and host orders were identified as major explanatory factors of the tetramer profile distribution patterns. Genomes overall grouped according to the host order, except for haloarchaea who formed a single supergroup. Within each group, cells tended to cluster together while viruses and plasmids tended to cluster according to their own taxonomic family. This pattern likely results from the combined influence of co-evolution and environmental constraints, highly contrasted among those archaea which comprise methanogens and diverse extremophiles. It confirms the potential of k-mer signal for extrachromosomal contig analysis [1, 2, 4], especially for their taxonomic assignation. Another application could be the detection of singular evolutionary trajectories by focusing on outliers. Indeed, previously-known and one new case of recent host transfers were efficiently detected. This approach relies on a distinct informational content than the more elaborate gene homology networks and appears as complementary. References 1. Galiez C et al, Bioinformatics 2017, 33:3113-3114. 2. Iranzo J et al, Journal of Virology 2016, 90:11043-11055. 3. Ren J et al, Microbiome 2017, 5(1):69. 4. Krawczyk PS et al, Nucleic Acids Research 2018:gkx1321-gkx1321.