Nucleic Acids Research, 1992, Vol. 20, No. 13 3453-3462
© 1992
MOLECULAR BIOLOGY |
The prediction of exons through an analysis of spliceable open reading frames
Department of Medical Genetics, University of British Colombia Vancouver, BC, Canada
* To whom correspondence should be addressed
Received February 24, 1992. Revised May 21, 1992. Accepted May 21, 1992.
We have developed a computer program which predicts internal exons from naive genomic sequence data and which will run on any IBM-compatible 80286 (or higher) computer. The algorithm searches a sequence for ‘spliceable open reading frames’ (SORFs), which are open reading frames bracketed by suitable splice-recognition sequences, and then analyzes the region for codon usage. Potential exons are stratified according to the reliability of their prediction, from confidence levels 1 to 5. The program is designed to predict internal exons of length greater than 60 nucleotides. In an analysis of 116 genes of a training set, 384 out of 441 such exons (87.1%) are identified, with 280 (63.5%) of predictions matching the true exon exactly (at both 5' and 3' splice junctions and in the correct reading frame), and with 104 (23.6%) exons matching partially. In a similar analysis of 14 genes in a test set unrelated to the genes used to generate the parameters of the program, 70 out of 80 internal exons greater than 60 bp in length are identified (87.5%), with 47 completely and 23 partially matched. SORFs that partially match true internal exons share at least one splice junction with the exon, or share both splice junctions but are interpreted in an incorrect reading frame. Specificity (the percentage of SORFs that correspond to true exons) varies from 91% at confidence level 1 to 16% at confidence level 5, with an overall specificity of 35 – 40%. The output displays nucleotide position, confidence level, reading frame phase at the 5' and 3' ends, acceptor and donor sequences and scoring statistics and also gives an amino acid translation of the potential exon. SORFIND compares favourably with other programs currently used to predict protein-coding regions.
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