While 'outcome-neutral' approaches using mRNA motifs known to promote efficient PRF cannot identify new classes of frameshift signals, they enable an expansion of our understanding of operational uses for PRF. The seminal study in this field searched the yeast genome for operational À1 ribosomal frameshift (À1 RF) promoting motifs resembling well characterized examples of viral À1 RF signals, identifying $260 putative such elements (9) . This work was limited by incomplete annotation of the yeast genome and insufficient computational resources available at the time. New bioinformatics tools were subsequently developed and applied using faster and more robust computational platforms. The results showed that: pattern matching approaches coupled with a predictive method for folding RNA sequences provided a dramatic improvement in the results; À1 RF motifs are widespread in the genome of Saccharomyces cerevisiae and many have predicted secondary structures with statistically significant measures of free energy (15) . This analysis showed that $11% of yeast genes contain at least one high probability À1 RF signal. Furthermore, we demonstrated that nine putative À1 RF signals selected from a variety of S. cerevisiae genes/genome promoted efficient recoding in vivo. More recently, this bioinformatics protocol has been applied to additional genomes. Currently, more than 25 genomes have been analyzed and it appears that 8-12% of genes contain at least one potential À1 RF signal (see PRFdB at http://prfdb.umd.edu/) (17) .