Local thermodynamic stability scores are well represented by a non-central student's t distribution

Evaluation of FORS-D analysis: a comparison with the statistically significant stem-loop potential

The stem-loop potential of a nucleic acid segment (expressed as a FONS value), decomposes into base composition-dependent and base order-dependent components. The latter, expressed as a FORS-D value, is derived by subtracting the value of the base composition-dependent component (FORS-M) from the FONS value. FORS-D analysis is the use of FORS-D values to estimate the potential of local base order to contribute to a stem-loop structure, and it has been used to investigate the relationship between stem-loop structure and other selective pressures on genomes. In the present study, we evaluated the reliability of FORS-D analysis by comparing it with statistically significant stem-loop potential, another robust method developed by Le and Maizel for examining stem-loop structure. We found that FORS-M values calculated using 10 randomized sequences are as reliable as those calculated using 100 randomized sequences. The resulting FORS-D values have a similar trend and distribution as statistically significant stem-loop potential, implying that FORS-D analysis is as reliable as the latter in measuring the distribution of base order-dependent stem-loop potential. Since the calculation of the FORS-M values is time consuming, the integrated program Bodslp developed by us will become a convenient tool for large-scale FORS-D analysis. The results also suggest that for some purposes the online program SigStb developed by Le and Maizel may be used as an alternative tool for FORS-D analysis.

Calculation of folding energies of single-stranded nucleic acid sequences: conceptual issues

The stability of a folded single-stranded nucleic acid depends on the composition and order of its constituent bases and may be assessed by taking into account the pairing energies of its constituent dinucleotides. To assess the possible biological significance of a computed structure, Maizel and coworkers in the 1980s compared the energy of folding of a natural single-stranded RNA sequence with the energies of several versions of the same sequence produced by shuffling base order. However, in the 2000s many took as self-evident the view that shuffling at the mononucleotide level (single bases) was conceptual wrong and should be replaced by shuffling at the level of dinucleotides (retaining pairs of adjacent bases). Folding energies then became indistinguishable from those of corresponding shuffled sequences and doubt was cast on the importance of secondary structures. Nevertheless, some continued productively to employ the single base shuffling approach, the justification for which is the topic of this paper. Because dinucleotide pairing energies are needed to calculate structure, it does not follow that shuffling should not disrupt dinucleotides. Base shuffling allows determination of the relative contributions of base composition and base order to total folding energy. The potential for secondary structure arises from pressures acting at both DNA and RNA levels, and is abundant throughout genomes-with a probable primary role in recombination. Within a gene the potential can often be accommodated, and base order and composition work together (values have the same negative sign) in contributing to total folding energy. But sometimes protein-coding pressure on base order conflicts with the pressure for secondary structure and the values have opposite signs. Total folding energy can be deemed of potential biological significance when the average of several readings is significantly less than zero.

Identification of functional, endogenous programmed -1 ribosomal frameshift signals in the genome of Saccharomyces cerevisiae

In viruses, programmed -1 ribosomal frameshifting (-1 PRF) signals direct the translation of alternative proteins from a single mRNA. Given that many basic regulatory mechanisms were first discovered in viral systems, the current study endeavored to: (i) identify -1 PRF signals in genomic databases, (ii) apply the protocol to the yeast genome and (iii) test selected candidates at the bench. Computational analyses revealed the presence of 10 340 consensus -1 PRF signals in the yeast genome. Of the 6353 yeast ORFs, 1275 contain at least one strong and statistically significant -1 PRF signal. Eight out of nine selected sequences promoted efficient levels of PRF in vivo. These findings provide a robust platform for high throughput computational and laboratory studies and demonstrate that functional -1 PRF signals are widespread in the genome of Saccharomyces cerevisiae. The data generated by this study have been deposited into a publicly available database called the PRFdb. The presence of stable mRNA pseudoknot structures in these -1 PRF signals, and the observation that the predicted outcomes of nearly all of these genomic frameshift signals would direct ribosomes to premature termination codons, suggest two possible mRNA destabilization pathways through which -1 PRF signals could post-transcriptionally regulate mRNA abundance.