Gene identification in genomic DNA from eukaryotes is complicated by the vast 
combinatorial possibilities of potential exon assemblies. If the gene encodes a 
protein that is closely related to known proteins, gene identification is aided 
by matching similarity of potential translation products to those target 
proteins. The genomic DNA and protein sequences can be aligned directly by 
scoring the implied residues of in-frame nucleotide triplets against the protein 
residues in conventional ways, while allowing for long gaps in the alignment 
corresponding to introns in the genomic DNA. We describe a novel method for such 
spliced alignment. The method derives an optimal alignment based on scoring for 
both sequence similarity of the predicted gene product to the protein sequence 
and intrinsic splice site strength of the predicted introns. Application of the 
method to a representative set of 50 known genes from Arabidopsis thaliana 
showed significant improvement in prediction accuracy compared to previous 
spliced alignment methods. The method is also more accurate than ab initio gene 
prediction methods, provided sufficiently close target proteins are available. 
In view of the fast growth of public sequence repositories, we argue that close 
targets will be available for the majority of novel genes, making spliced 
alignment an excellent practical tool for high-throughput automated genome 
annotation.