Despite considerable progress in the development of computational tools for the prediction of protein coding genes in sequenced genomes, the identification and annotation of recoded genes lags far behind. The hurdle lies not so much in the fact that recoded genes do not obey standard rules of genetic readout but, rather, in the considerable diversity of recoded genes and sequence elements responsible for recoding. Even among evolutionarily related genes, all utilizing recoding, the diversity of recoding signals can be considerable. An extreme example is when orthologous genes utilize recoding at different stages of gene expression to achieve the same goal. An example is in dnaX, where ribosomal frameshifting is employed by enterobacteria, but transcriptional slippage is used in Thermus thermophilus (40) . A similar situation occurs in bacterial insertion sequence (IS) elements, where a certain group of IS elements utilizes transcriptional slippage to produce ORFA-ORFB fusions, while many other IS elements utilize ribosomal frameshifting for the same purpose (41) . The diversity of recoding functions, combined with the wide spectrum of unrelated sequence elements involved in recoding, makes the design of a uniform model of recoding intractable. Nonetheless, in recent years, we have witnessed the development of specialized models and computational tools for the identification of particular subsets of recoding cassettes, or tools that are specific to recoding events in particular groups of homologous genes (42) (43) (44) (45) .