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Sequencing of bacterial genomes has become a common technique of the present day microbiology. Thereafter, data mining in complete genome sequence is an essential step to uncover the uniqueness and evolutionary success of microorganisms. Oligonucleotide usage (OU or k-mer) statistics provides invaluable tools to get insight into genome organization and functionality. The study of genome OU signatures has a long history dating back to early publications by Karlin et al. 1995, 1997, 1998, who focused mainly on dinucleotide compositional biases and their evolutionary implications. Statistical approaches of OU comparison were further advanced by Deschavanne et al., 1999, who applied chaos game algorithms; and by Pride et al., 2003, who extended the analysis to tetranucleotides using Markov Chain Model simulations. Later, a number of practical tools for phylogenetic comparison of bacterial genomes (Coenye & Vandamme, 2004; van Passel et al., 2006); identification of horizontally transferred genomic islands (Mrazek & Karlin, 1999; Pried & Blaser, 2002; Nakamura et al., 2004; Azad & Lawrence, 2005; Dufraign et al., 2005; Becq et al., 2007) and assignment of unknown genomic sequences (Abe et al., 2003; Teeling et al., 2004) based on OU statistics became publicly available. These approaches exploited the notion that genomic OU composition was less variable within genomes rather than between them, regardless of which genomic regions had been taken into consideration (Jernigan & Baran, 2002). A general belief was that if a significant compositional difference was discovered in genomic fragments relative to the core genome, these loci most likely can be assigned to horizontally transferred genetic elements (transposons, prophages or integrated plasmids). This approach was criticized by several researchers (Koski et al., 2001; Wang 2001), who pointed out that codon bias and base composition are poor indicators of horizontal gene transfer. Therefore, there is a need for more informative parameters which also take into account higher order DNA variation. An overview of the current OU statistical methods based on di-, tetraand hexanucleotides has been published recently (Bohlin et al., 2008). The conclusion of the review was that all methods were context dependent and, though being efficient and powerful, none of them were superior in all applications. Thus, the major

Linguistic Approaches for Annotation, Visualization and Comparison of Prokaryotic Genomes and Environmental Sequences