SAPPHIRE

SAPPHIRE predicts σ70 promoters in Pseudomonas genomes using an artificial neural network that detects resemblance to the -35 and -10 consensus boxes.

Key Features:

• Neural Network-Based Classification: SAPPHIRE employs an artificial neural network (ANN) that evaluates DNA sequences based on similarity to the -35 and -10 consensus boxes of σ70 promoters.
• Specialization for Pseudomonas σ70 promoters: The model is tailored to σ70 promoter characteristics observed in Pseudomonas species.
• Training Data from Pseudomonas aeruginosa and Pseudomonas putida: The ANN was trained on experimentally validated σ70 promoter sequences from P. aeruginosa and P. putida.
• Performance Compared to Existing Tools: SAPPHIRE outperforms existing promoter prediction software when classifying σ70 promoters in Pseudomonas.

Scientific Applications:

• Regulatory element identification in Pseudomonas genomes: Prediction of candidate σ70 promoters and associated regulatory elements within Pseudomonas genomic sequences.
• Support for experimental studies of gene regulation: Provides predicted promoter locations to guide wet-lab validation of transcriptional regulation and expression patterns.
• Applications in medical microbiology and biotechnology: Enables investigation of promoter-driven gene expression in Pseudomonas species relevant to clinical and industrial contexts.

Methodology:

An artificial neural network was trained on known σ70 promoter sequences from Pseudomonas aeruginosa and Pseudomonas putida and evaluates input DNA sequences for resemblance to the -35 and -10 consensus boxes to classify σ70 promoters.