Incorporation of splice site probability models for non-canonical introns improves gene structure prediction in plants.

MOTIVATION: The vast majority of introns in protein-coding genes of higher eukaryotes have a GT dinucleotide at their 5'-terminus and an AG dinucleotide at their 3' end. About 1-2% of introns are non-canonical, with the most abundant subtype of non-canonical introns being characterized by GC and AG dinucleotides at their 5'- and 3'-termini, respectively. Most current gene prediction software, whether based on ab initio or spliced alignment approaches, does not include explicit models for non-canonical introns or may exclude their prediction altogether. With present amounts of genome and transcript data, it is now possible to apply statistical methodology to non-canonical splice site prediction. We pursued one such approach and describe the training and implementation of GC-donor splice site models for Arabidopsis and rice, with the goal of exploring whether specific modeling of non-canonical introns can enhance gene structure prediction accuracy.

RESULTS: Our results indicate that the incorporation of non-canonical splice site models yields dramatic improvements in annotating genes containing GC-AG and AT-AC non-canonical introns. Comparison of models shows differences between monocot and dicot species, but also suggests GC intron-specific biases independent of taxonomic clade. We also present evidence that GC-AG introns occur preferentially in genes with atypically high exon counts.

AVAILABILITY: Source code for the updated versions of GeneSeqer and SplicePredictor (distributed with the GeneSeqer code) isavailable at https://bioinformatics.iastate.edu/bioinformatics2go/gs/download.html. Web servers for Arabidopsis, rice and other plant species are accessible at https://www.plantgdb.org/PlantGDB-cgi/GeneSeqer/AtGDBgs.cgi, https://www.plantgdb.org/PlantGDB-cgi/GeneSeqer/OsGDBgs.cgi and https://www.plantgdb.org/PlantGDB-cgi/GeneSeqer/PlantGDBgs.cgi, respectively. A SplicePredictor web server is available at https://bioinformatics.iastate.edu/cgi-bin/sp.cgi. Software to generate training data and parameterizations for Bayesian splice site models is available at https://gremlin1.gdcb.iastate.edu/~volker/SB05B/BSSM4GSQ/