G4PromFinder: An algorithm for predicting transcription promoters in GC-rich bacterial genomes based on AT-rich elements and G-quadruplex motifs

Background: Over the last few decades, computational genomics has tremendously contributed to decipher biology from genome sequences and related data. Considerable effort has been devoted to the prediction of transcription promoter and terminator sites that represent the essential "punctuation marks" for DNA transcription. Computational prediction of promoters in prokaryotes is a problem whose solution is far from being determined in computational genomics. The majority of published bacterial promoter prediction tools are based on a consensus-sequences search and they were designed specifically for vegetative sigma(70) promoters and, therefore, not suitable for promoter prediction in bacteria encoding a lot of sigma factors, like actinomycetes. Results: In this study we investigated the possibility to identify putative promoters in prokaryotes based on evolutionarily conserved motifs, and focused our attention on GC-rich bacteria in which promoter prediction with conventional, consensus-based algorithms is often not-exhaustive. Here, we introduce G4PromFinder, a novel algorithm that predicts putative promoters based on AT-rich elements and G-quadruplex DNA motifs. We tested its performances by using available genomic and transcriptomic data of the model microorganisms Streptomyces coelicolor A3(2) and Pseudomonas aeruginosa PA14. We compared our results with those obtained by three currently available promoter predicting algorithms: the sigma(70)consensus-based PePPER, the sigma factors consensus-based bTSSfinder, and PromPredict which is based on double-helix DNA stability. Our results demonstrated that G4PromFinder is more suitable than the three reference tools for both the genomes. In fact our algorithm achieved the higher accuracy (F-1-scores 0.61 and 0.53 in the two genomes) as compared to the next best tool that is PromPredict (F-1-scores 0.46 and 0.48). Consensus-based algorithms produced lower performances with the analyzed GC-rich genomes. Conclusions: Our analysis shows that G4PromFinder is a powerful tool for promoter search in GC-rich bacteria, especially for bacteria coding for a lot of s factors, such as the model microorganism S. coelicolor A3(2). Moreover consensus-based tools and, in general, tools that are based on specific features of bacterial sigma factors seem to be less performing for promoter prediction in these types of bacterial genomes.