Novel tools for the prediction of promoters in plants and bacteria
Abstract
Aim. The computational search for promoters remains an attractive problem in bioinformatics. Despite the attention it has received for many years, the problem has not been addressed satisfactorily. These studies were aimed to develop novel computer tools for prediction of promoters (transcription start sites, TSSs) in plants and bacteria. Results. Two novel tools for prediction of RNA polymerase II promoters in plants (TSSPlant) and bacteria (bTSSfinder) have been developed. TSSPlant achieves significantly higher accuracy compared to the next best promoter prediction program for both TATA and TATA-less promoters; it is available to download as a standalone program at http://www.cbrc.kaust.edu.sa/download/. bTSSfinder predicts promoters for five classes of σ factors in Cyanobacteria (σA, σC, σH, σG and σF) and for five classes of sigma factors in E. coli (σ70, σ38, σ32, σ28 and σ24). Comparing to currently available tools, bTSSfinder achieves highest accuracy. bTSSfinder is available standalone and online at http://www.cbrc.kaust.edu.sa/btssfinder. Conclusions. To date, TSSPlant and bTSSfinder are most accurate promoter predictors in plants and bacteria, respectively.
Keywords: transcription, RNA polymerase, promoter, TSS, promoter prediction.
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