Splice site prediction in Arabidopsis thaliana pre-mRNA by
combining local and global sequence information.
S.M. Hebsgaard, P.G. Korning, N. Tolstrup, J. Engelbrecht, P. Rouze and S. Brunak,
Nucleic Acids Research, 1996, Vol. 24, No. 17, 3439-3452.
Artificial neural networks have been combined with a
rule based system to predict intron splice sites in the dicot plant
Arabidopsis thaliana. A two step prediction scheme, where a
global prediction of the coding potential regulates a cutoff level for
a local prediction of splice sites, is refined by rules based on splice
site confidence values, prediction scores, coding context, and
distances between potential splice sites. In this approach, the
prediction of splice sites mutually affect each other in a non-local
manner. The combined approach drastically reduces the large amount of
false positive splice sites normally haunting splice site prediction.
An analysis of the errors made by the networks in the first step of the
method revealed a previously unknown feature, a frequent T-tract
prolongation containing cryptic acceptor sites in the 5' end of exons.
The method presented here has been compared to three other approaches,
GeneFinder, GeneMark, and Grail. Overall the method presented here is
an order of magnitude better. We show that the new method is able to
find a donor site in the coding sequence for the jelly fish Green
Fluorescent Protein, exactly at the position that was experimentally
observed in thaliana transformants. Predictions for
alternatively spliced genes are also presented, together with examples
of genes from other dicots, monocots, and algae. The method has been
made available through electronic mail (
the WWW at http://www.cbs.dtu.dk/NetPlantGene.html
Keywords: Arabidopsis thaliana; splice site prediction;
splice site pairing; plant biotechnology; neural networks; rule based