A novel promoter prediction method based on multiple sigma factors model for bacterial genomes





ABSTRACT
Though non-housekeeping sigma subunits in bacteria play an important role in extracellular stimuli response, currently no accurate high-throughput techniques are able to annotate the type of sigma promoter utilized by a transcript. In this work, we present a novel method, which provides bacterial promoter prediction based on a comprehensive modelling of multiple sigma factors. The features of four types of trans-species conserved sigma promoters are characterized by a probabilistic model and employ an expectation maximization (EM) strategy for iteratively parameter training. The resultant method, named as SigmaPromoter, is designed based on a scoring scheme to predict all promoters in a prokaryotic genome, and is able to annotate sigma factors used by the promoters. Upon test of the reliable sets, we show that SigmaPromoter is effective in detecting distinct types of sigma factors regulating transcription events, moreover the total prediction performance of SigmaPromoter evidently outperforms the existing methods. Compared to the current best predictors, SigmaPromoter achieves both the best sensitivity and the best specificity on average. In addition, SigmaPromoter's promoter prediction is able to annotate alternative transcription with the advantage of higher reliability.

Please direct your questions or comments to hqzhu(at)pku.edu.cn or ylsrommel(at)gmail.com
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CITATION
Longshu Yang, Qi Wang, Cheng Yang, Queyue Wang, Feifei He, Li Qu and Huaiqiu Zhu. A novel promoter prediction method based on multiple sigma factors model for bacterial genomes.
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