Details

Autor(en) / Beteiligte

• de Avila e Silva, Scheila
• Echeverrigaray, Sergio
• Gerhardt, Günther J.L.

Titel

BacPP: Bacterial promoter prediction—A tool for accurate sigma-factor specific assignment in enterobacteria

Ist Teil von

• Journal of theoretical biology, 2011-10, Vol.287, p.92-99

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2011

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. ► Promoter recognition and prediction 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 E. 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%). ► For promoter sequences belonging to other enterobacteria, BacPP maintained 76% accuracy overall.