Background In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance. Results We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the − 35 and − 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future. Conclusions SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com. Alternatively, users can download the tool as a Python 3 script for local application from this site.

As part of the ongoing renaissance of phage biology, more phage genomes are becoming available through DNA sequencing. However, our understanding of the transcriptome architecture that allows these genomes to be expressed during host infection is generally poor. Transcription start sites (TSSs) and operons have been mapped for very few phages, and an annotated global RNA map of a phagealone or together with its infected host-is not available at all. Here, we applied differential RNA-seq (dRNA-seq) to study the early, host takeover phase of infection by assessing the transcriptome structure of Pseudomonas aeruginosa jumbo phage ɸKZ, a model phage for viral genetics and structural research. This map substantially expands the number of early expressed viral genes, defining TSSs that are active ten minutes after ɸKZ infection. Simultaneously, we record gene expression changes in the host transcriptome during this critical metabolism conversion. In addition to previously reported upregulation of genes associated with amino acid metabolism, we observe strong activation of genes with functions in biofilm formation (cdrAB) and iron storage (bfrB), as well as an activation of the antitoxin ParD. Conversely, ɸKZ infection rapidly down-regulates complexes IV and V of oxidative phosphorylation (atpCDGHF and cyoABCDE). Taken together, our data provide new insights into the transcriptional organization and infection process of the giant bacteriophage ɸKZ and adds a framework for the genome-wide transcriptomic analysis of phage-host interactions.

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