Impact of RNA structure on the prediction of donor and acceptor splice sites

Abstract

Background: gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms.

Results: we sought the role of mRNA secondary structures and their information contents for five vertebrate and plant splice site datasets. We selected 900-nucleotide sequences centered at each (real or decoy) donor and acceptor sites, and predicted their corresponding RNA structures by Vienna software. Then, based on whether the nucleotide is in a stem or not, the conventional four-letter nucleotide alphabet was translated into an eight-letter alphabet. Zero-, first- and second-order Markov models were selected as the signal detection methods. It is shown that applying the eight-letter alphabet compared to the four-letter alphabet considerably increases the accuracy of both donor and acceptor site predictions in case of higher order Markov models.

Conclusion: Our results imply that RNA structure contains important data and future gene prediction programs can take advantage of such information.

Figure 1

Average number of structural changes in a 21-nucleotide window around 1000 donor GUs. See the text for details.

Figure 2

Distribution of predicted linear distances of base-paired nucleotides in RNA sequences. See text for details.

Figure 3

Log likelihood ratio (LLR with log-base-2) of formation of loop structure at different positions around splice sites in AtGS and HsGS datasets. The sequences are shown in 5'→3' direction. Asterisked positions are those positions that show a significant difference (p < 0.05 based on the test for differences of two binomial proportions) between the frequency of "loops" in real and decoy sites. 3' AtGS (A), 3' HsGS (B), 5' AtGS (C) and 5' HsGS (D).