PREDICTION OF LOCALLY OPTIMAL SPLICE SITES IN PLANT PRE-MESSENGER-RNAWITH APPLICATIONS TO GENE IDENTIFICATION IN ARABIDOPSIS-THALIANA GENOMIC DNA

Prediction of splice site selection and efficiency from sequence inspe ction is of fundamental interest (testing the current knowledge of req uisite sequence features) and practical importance (genome annotation, design of mutant or transgenic organisms), In plants, the dominant va riables affecting splice site selection and efficiency include the deg ree of matching to the extended splice site consensus and the local gr adient of U- and G+C-composition (introns being U-rich and exons G+C-r ich), We present a novel method for splice site prediction, which was particularly trained for maize and Arabidopsis thaliana, The method ex tends our previous algorithm based on logitlinear models by considerin g three variables simultaneously: intrinsic splice site strength, loca l optimality and fit with respect to the overall splice pattern predic tion. We show that the method considerably improves prediction specifi city without compromising the high degree of sensitivity required in g ene prediction algorithms. Applications to gene identification are ill ustrated for Arabidopsis and suggest that successful methods must comb ine scoring for splice sites, coding potential and similarity with pot ential homologs in non-trivial ways. A WWW version of the SplicePredic tor program is available at http:/gnomic.stanford.edu/ volker/SplicePr edictor.html/.