An unsupervised classification scheme for improving predictions of prokaryotic TIS.
BACKGROUND
Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes.


RESULTS
We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum.


CONCLUSIONS
On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool "TICO" (TIs COrrector) which is publicly available from our web site.
Tech
Maike
M
Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. maike@gobics.de
Meinicke
Peter
P
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D016000:Cluster Analysis; D018387:Codon, Initiator / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D039662:Prokaryotic Initiation Factors / Q000235:genetics*; D014176:Protein Biosynthesis / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*
D018387:Codon, Initiator; D039662:Prokaryotic Initiation Factors