Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks.
BACKGROUND
One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data.


RESULTS
When compared to the SIDD threshold prediction method, artificial neural networks showed noticeable improvements for precision, recall, and F-score over a range of values. The maximal F-score for the ANN classifier was 62.3 and 56.8 for the threshold-based classifier.


CONCLUSIONS
Artificial neural networks were used to predict promoters based on SIDD profile data. Results using this technique were an improvement over the previous SIDD threshold approach. Over a wide range of precision-recall values, artificial neural networks were more capable of identifying distinctive characteristics of promoter regions than threshold based methods.
Department Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Hwy 82 West, Itta Bena, Mississippi 38941, USA.
Charles
C
Bland
Abigail S
AS
Newsome
Aleksandra A
AA
Markovets
D004269:DNA, Bacterial / Q000737:chemistry; D004926:Escherichia coli / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic; D012045:Regulatory Sequences, Nucleic Acid
D004269:DNA, Bacterial