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

Journal: BMC bioinformatics
Published Date:

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.

Authors

  • Lucas Coppens
    Laboratory of Gene Technology, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 21, Box 2462, 3001, Leuven, Belgium.
  • Rob Lavigne
    Department of Biosystems, KU Leuven, Leuven, Belgium.