A dictionary based approach for gene annotation

This paper describes a fast and fully automated dictionary based approach to gene annotation and exon prediction. Two dictionaries are constructed, one from the nonredundant protein OWL database and the other from the dbEST database. These dictionaries are used to obtain O(1) time lookups of tuples in the dictionaries (4 tuples for the OWL database and 11 tuples for the dbEST database). These tuples can be used to rapidly find the longest matches at every position in an input sequence to the database sequences. Such matches provide very useful information pertaining to locating common segments between exons, alternative splice sites, and frequency data of long tuples for statistical purposes. These dictionaries also provide the basis for both homology determination, and statistical approaches to exon prediction. For instance, using the OWL protein database on a benchmark test set of 130 genes, and after removing sequences from the database with exact amino acid homology to genes in our test set, we find 88% of coding nucleotides, and 99% of our predictions of coding nucleotides are correct. Also, 81% of coding exons are predicted exactly, while 82% of our predictions of exons agree exactly with the published annotation of their genes