Promoter sequences are well known to play a central role in gene expression. BacPP is designed to recognize and predict Escherichia coli promoter sequences according to the sigma factor that recognizes the sequence. The specific accuracy for each sigma factor is:
- sigma 24: 86.9%;
- sigma 28: 92.8%;
- sigma 32: 91.5%;
- sigma 38: 89.3%;
- sigma 54: 97.0%;
- sigma 70: 83.6%.
This bioinformatic tool is based on weighted rules extracted from neural networks trained with promoter sequences known to respond to a specific sigma factor. For promoter sequences belonging to other enterobacteria BacPP maintained 76% accuracy overall.
It is possible to read the complete description of BacPP in the paper:
S. de Avila e Silva, S. Echeverrigaray, G. J. Gerhardt. "BacPP: Bacterial promoter prediction - A tool for accurate sigma-factor specific assignment in enterobacteria". J. Theor. Biol., 287 (2011), pp. 92–99.
Available here.
- sigma 28: 92.8%;
- sigma 32: 91.5%;
- sigma 38: 89.3%;
- sigma 54: 97.0%;
- sigma 70: 83.6%.
This bioinformatic tool is based on weighted rules extracted from neural networks trained with promoter sequences known to respond to a specific sigma factor. For promoter sequences belonging to other enterobacteria BacPP maintained 76% accuracy overall.
It is possible to read the complete description of BacPP in the paper:
S. de Avila e Silva, S. Echeverrigaray, G. J. Gerhardt. "BacPP: Bacterial promoter prediction - A tool for accurate sigma-factor specific assignment in enterobacteria". J. Theor. Biol., 287 (2011), pp. 92–99.
Available here.