title: BacPP: bacterial promoter prediction--a tool for accurate sigma-factor specific assignment in enterobacteria.
abstract: Promoter sequences are well known to play a central role in gene expression. Their recognition and assignment in silico has not consolidated into a general bioinformatics method yet. Most previously available algorithms employ and are limited to σ70-dependent promoter sequences. This paper presents a new tool named BacPP, designed to recognize and predict Escherichia coli promoter sequences from background with specific accuracy for each σ factor (respectively, σ24, 86.9%; σ28, 92.8%; σ32, 91.5%; σ38, 89.3%, σ54, 97.0%; and σ70, 83.6%). BacPP is hence outstanding in recognition and assignment of sequences according to σ factor and provide circumstantial information about upstream gene sequences. This bioinformatic tool was developed by weighing rules extracted from neural networks trained with promoter sequences known to respond to a specific σ factor. Furthermore, when challenged with promoter sequences belonging to other enterobacteria BacPP maintained 76% accuracy overall.
authors.affiliation: Universidade de Caxias do Sul, Programa de Pós-Graduação em Biotecnologia, Av. Francisco Getúlio Vargas, 1130-CEP 95070-560, Caxias do Sul-RS, Brazil. sasilva6@ucs.br
authors.forename: Scheila
authors.initials: S
authors.lastname: de Avila E Silva
authors.forename: Sergio
authors.initials: S
authors.lastname: Echeverrigaray
authors.forename: Günther J L
authors.initials: GJ
authors.lastname: Gerhardt
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004755:Enterobacteriaceae / Q000235:genetics*; D004926:Escherichia coli / Q000235:genetics; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic / Q000235:genetics*; D012808:Sigma Factor / Q000235:genetics*
chemical_list: D012808:Sigma Factor