Abstract
BackgroundIn the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability.ResultsThe promoter region is less stable and hence more prone to melting as compared to other genomic regions. Our analysis shows that a method of promoter prediction based on the differences in the stability of DNA sequences in the promoter and non-promoter region works much better compared to existing prokaryotic promoter prediction programs, which are based on sequence motif searches. At present the method works optimally for genomes such as that of Escherichia coli, which have near 50 % G+C composition and also performs satisfactorily in case of other prokaryotic promoters.ConclusionsOur analysis clearly shows that the change in stability of DNA seems to provide a much better clue than usual sequence motifs, such as Pribnow box and -35 sequence, for differentiating promoter region from non-promoter regions. To a certain extent, it is more general and is likely to be applicable across organisms. Hence incorporation of such features in addition to the signature motifs can greatly improve the presently available promoter prediction programs.
Highlights
In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation
We propose a prokaryotic promoter prediction method, which is based on the stability differences between promoter and non-promoter regions
It is interesting that the promoters from diverse bacteria, which have quite different genome composition (A+T composition: E. coli 0.49, B. subtilis 0.56 and C. glutamicum 0.46), show strikingly similar features
Summary
In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. One of the biggest challenges is the task of gene prediction and to fulfil this need, several gene prediction programs have been developed (For reviews see [1,2,3,4,5]). Most of these prediction programs require training based on prior knowledge of sequence features such as codon bias, which in turn are organism specific. One of the important steps towards ab initio gene prediction is to develop better promoter and TSS (transcription start site) prediction methods
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