Nucleic Acids Research, Vol 26, Issue 20 4748-4757, Copyright © 1998 by Oxford University Press
V Brendel and J Kleffe
Prediction of splice site selection and efficiency from sequence inspection
is of fundamental interest (testing the current knowledge of requisite
sequence features) and practical importance (genome annotation, design of
mutant or transgenic organisms). In plants, the dominant variables
affecting splice site selection and efficiency include the degree of
matching to the extended splice site consensus and the local gradient of U-
and G+C-composition (introns being U-rich and exons G+C-rich). We present a
novel method for splice site prediction, which was particularly trained for
maize and Arabidopsis thaliana. The method extends our previous algorithm
based on logitlinear models by considering three variables simultaneously:
intrinsic splice site strength, local optimality and fit with respect to
the overall splice pattern prediction. We show that the method considerably
improves prediction specificity without compromising the high degree of
sensitivity required in gene prediction algorithms. Applications to gene
identification are illustrated for Arabidopsis and suggest that successful
methods must combine scoring for splice sites, coding potential and
similarity with potential homologs in non-trivial ways. A WWW version of
the SplicePredictor program is available at
http:/gnomic.stanford.edu/~volker/SplicePredi ctor.html/
ARTICLES
Prediction of locally optimal splice sites in plant pre-mRNA with applications to gene identification in Arabidopsis thaliana genomic DNA
Department of Mathematics, Stanford University, Stanford, CA 94305, USA. vbrendel@iastate.edu
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