![]() ![]() As promoters are typically located directly upstream of the transcription start site (TSS), we used published TSS global maps obtained using sequencing technology such as dRNA-seq and Cappable-seq to define promoter sequences. Here, we developed a general (species independent) bacterial promoter recognition method, Promotech, trained on a large data set of promoter sequences of nine distinct bacterial species belonging to five different phyla (namely, Actinobacteria, Chlamydiae, Firmicutes, Proteobacteria, and Spirochaetes). In their benchmark, they found that iPro70-FMWin achieved the best results in terms of accuracy and MCC. Cassiano and Silva-Rocha carried out a comparative assessment of bacterial promoter prediction tools for identifying E. coli promoter prediction methods such as MULTiPly, SELECTOR, iPromoter-BnCNN, IBPP, and iPromoter-2L, among others (Supplementary Table S1). comparatively assessed G4PromFinder’s performance in terms of F1-score with that of bTSSfinder, PePPER, and PromPredict and found that G4PromFinder outperformed the other three tools in GC-rich bacterial genomes. G4PromFinder utilizes conserved motifs and focuses on Streptomyces coelicolor A3(2) and Pseudomonas aeruginosa PA14. Based on this, we hypothesized that predictive performance can be improved if a method is trained on data from a diverse group of bacterial species.Īnother bacterial promoter detection method evaluated on a multi-species data set is G4PromFinder. coli to identify promoters, and bTSSfinder focuses on E. BPROM uses five relatively conserved motifs from E. These results are promising as they showed that it is possible to recognize promoters of several bacterial species even when the methods were designed for specific bacterial species. The best average sensitivity (recall) values obtained were 59% and 49% by bTSSfinder and BPROM, respectively, while bTSSfinder achieved higher accuracy than the other three assessed tools. evaluated the performance of their method (bTSSfinder) and other three methods on ten bacterial species belonging to five different phyla. Additionally, the performance of current tools rapidly decreases when applied to whole genomes, and thus, it is common practice to restrict the size of the input sequence to a few hundred nucleotides. However, most of these tools were designed to recognize promoters in Escherichia coli or in few (2 or 3) bacterial species, and their applicability to a wider range of bacterial species is unproven. There have been numerous bioinformatics tools developed to recognize bacterial promoter sequences (summarized in Supplementary Table S1). Recognizing promoters is critical for understanding bacterial gene expression regulation. σ factors are bacterial DNA-binding regulatory proteins of transcription initiation that enable specific binding of RNAP to promoters. Promoters are DNA segments essential for the initiation of transcription at a defined location in the genome, which are recognized by a specific RNA polymerase (RNAP) holoenzyme (E σ).
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