## BacPP: bacterial promoter prediction – a tool for accurate sigma-factor specific assignment in enterobacteria.(English)Zbl 1397.92246

Summary: 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 $$\sigma$$70-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 $$\sigma$$ factor (respectively, $$\sigma 24$$, 86.9%; $$\sigma 28$$, 92.8%; $$\sigma 32$$, 91.5%; $$\sigma 38$$, 89.3%, $$\sigma 54$$, 97.0%; and $$\sigma 70$$, 83.6%). BacPP is hence outstanding in recognition and assignment of sequences according to $$\sigma$$ 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 $$\sigma$$ factor. Furthermore, when challenged with promoter sequences belonging to other enterobacteria BacPP maintained 76% accuracy overall.

### MSC:

 92C40 Biochemistry, molecular biology 90C29 Multi-objective and goal programming 92D10 Genetics and epigenetics 68T05 Learning and adaptive systems in artificial intelligence 92-04 Software, source code, etc. for problems pertaining to biology

### Keywords:

neural network; rule extraction; gene expression regulation
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