Genome structural annotation is a multi-level process that includes prediction of coding genes, pseudogenes, promoter regions, repeat elements, regulatory elements in intergenic regions such as small non-coding RNAs (sRNA), and other genomic features of biological significance. Computational gene prediction methods such as Glimmer [8] or GenMark [9] use Hidden Markov models which are based on a training set of well annotated genes. Although these methods are quite efficient, they often miss genes with anomalous nucleotide composition and have several well-described shortcomings: because bacterial genomes do not have introns, detecting gene boundaries is comparatively difficult; due to the usage of more than one start codon, computational genome annotation methods may predict overlapping ORFs [10] ; prediction programs use arbitrary minimum cutoff lengths to filter short ORFs, which may lead to under-representation of small genes. In case of sRNA (small non-coding RNA) prediction, the lack of DNA sequence conservation, lack of a protein coding frame, and the limited accuracy of transcriptional signal prediction programs (promoter/Rho terminator prediction) confound computational prediction [11, 12] .