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

ABSTRACT:

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 promo

SUBMITTER: Coppens L 

PROVIDER: S-EPMC7510298 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature