BacPP: bacterial promoter prediction--a tool for accurate sigma-factor specific assignment in enterobacteria

J Theor Biol. 2011 Oct 21;287:92-9. doi: 10.1016/j.jtbi.2011.07.017. Epub 2011 Aug 3.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Enterobacteriaceae / genetics*
  • Escherichia coli / genetics
  • Gene Expression Regulation, Bacterial / genetics
  • Neural Networks, Computer
  • Promoter Regions, Genetic / genetics*
  • Sigma Factor / genetics*

Substances

  • Sigma Factor