title: GC content dependency of open reading frame prediction via stop codon frequencies.
abstract: A frequently used approach for detecting potential coding regions is to search for stop codons. In the standard genetic code 3 out of 64 trinucleotides are stop codons. Hence, in random or non-coding DNA one can expect every 21st trinucleotide to have the same sequence as a stop codon. In contrast, the open reading frames (ORFs) of most protein-coding genes are considerably longer. Thus, the stop codon frequency in coding sequences deviates from the background frequency of the corresponding trinucleotides. This has been utilized for gene prediction, in particular, in detecting protein-coding ORFs. Traditional methods based on stop codon frequency are based on the assumption that the GC content is about 50%. However, many genomes show significant deviations from that value. With the presented method we can describe the effects of GC content on the selection of appropriate length thresholds of potentially coding ORFs. Conversely, for a given length threshold, we can calculate the probability of observing it in a random sequence. Thus, we can derive the maximum GC content for which ORF length is practicable as a feature for gene prediction methods and the resulting false positive rates. A rough estimate for an upper limit is a GC content of 80%. This estimate can be made more precise by including further parameters and by taking into account start codons as well. We demonstrate the feasibility of this method by applying it to the genomes of the bacteria Rickettsia prowazekii, Escherichia coli and Caulobacter crescentus, exemplifying the effect of GC content variations according to our predictions. We have adapted the method for predicting coding ORFs by stop codon frequency to the case of GC contents different from 50%. Usually, several methods for gene finding need to be combined. Thus, our results concern a specific part within a package of methods. Interestingly, for genomes with low GC content such as that of R. prowazekii, the presented method provides remarkably good results even when applied alone.
authors.affiliation: Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07745 Jena, Germany. m.pohl@uni-jena.de
authors.forename: Martin
authors.initials: M
authors.lastname: Pohl
authors.forename: Günter
authors.initials: G
authors.lastname: Theissen
authors.forename: Stefan
authors.initials: S
authors.lastname: Schuster
mesh_terms: D001482:Base Composition; D018388:Codon, Terminator; D016680:Genome, Bacterial; D016366:Open Reading Frames
chemical_list: D018388:Codon, Terminator