SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas

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 organismE. coli.As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such asPseudomonas,a Gram-negative bacterium of crucial medical and biotechnological importance. Results: We developedSAPPHIRE,a promoter predictor for sigma 70 promoters inPseudomonas.This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the - 35 and - 10 boxes of sigma 70 promoters found experimentally inP. aeruginosa and P. utida. SAPPHIRE currently outperforms established predictive software when classifyingPseudomonas sigma 70 promoters and was built to allow further expansion in the future. Conclusions: SAPPHIREis the first predictive tool for bacterial sigma 70 promoters inPseudomonas. SAPPHIRE is free, publicly available and can be accessed online at. Alternatively, users can download the tool as a Python 3 script for local application from this site.