Improving promoter prediction for the NNPP2.2 algorithm: a case study using Escherichia coli DNA sequences

Improved prediction of bacterial transcription start sites

MOTIVATION

Identifying bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters and their transcription start sites (TSSs) in Escherichia coli. The accepted method for this problem is to use position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in large numbers of false positive predictions.

RESULTS

Our approaches to TSS prediction are based upon an ensemble of support vector machines (SVMs) employing a variant of the mismatch string kernel. This classifier is subsequently combined with a PWM and a model based on distribution of distances from TSS to gene start. We investigate the effect of different scoring techniques and quantify performance using area under a detection-error tradeoff curve. When tested on a biologically realistic task, our method provides performance comparable with or superior to the best reported for this task. False positives are significantly reduced, an improvement of great significance to biologists.

AVAILABILITY

The trained ensemble-SVM model with instructions on usage can be downloaded from http://eresearch.fit.qut.edu.au/downloads

FAST DB: a website resource for the study of the expression regulation of human gene products

Human genes use various mechanisms to generate different transcripts having different exon content, which in turn generate multiple protein isoforms having differential and even opposite biological activities. To understand the biological consequences of gene transcriptional activity modulation, it is necessary to integrate the capability of genes to generate distinct functional products, particularly because transcriptional stimuli also affect the exon content of their target gene products. For this purpose, we have developed a bioinformatics suite, FAST DB, which defines easily and accurately the exon content of all known transcripts produced by human genes. In addition, several tools have been developed, including a graphical presentation of all gene products, a sequence multi-alignment of all gene transcripts and an in silico PCR computer program. The FAST DB interface also offers extensive links to website resources for promoter analysis and transcription factor binding site prediction, splicing regulatory sequence prediction, as well as 5′- and 3′-untranslated region analysis. FAST DB has been designed to facilitate studies that integrate transcriptional and post-transcriptional events to investigate the expression regulation of human gene products.

A novel method for prokaryotic promoter prediction based on DNA stability

Background

In 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.

Results

The 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.

Conclusions

Our 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.