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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
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