Computational Biology and Bioinformatics
Volume 8, Issue 1, June 2020, Pages: 15-19
Received: May 24, 2020;
Accepted: Jun. 8, 2020;
Published: Jun. 20, 2020
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Mengmeng Zhang, College of Life Sciences, Capital Normal University, Beijing, China
Lu Wang, College of Life Sciences, Capital Normal University, Beijing, China
Ping Wan, College of Life Sciences, Capital Normal University, Beijing, China
The mechanism of prokaryotic gene expression remains incompletely understood. Promoters are regions in genome that locating upstream to genes and regulate of gene expressions. Despite more and more E. coli K-12 promoter sequences have been obtained experimentally, and some regions such as -10 region and -30 region have been described, the features in promoter sequences are far from explicitly characterized. Here, we address this challenge using an approach based on the deep convolutional neural network (CNN). We collected six classes of E. coli K-12 promoter sequences which are all annotated as with strong evidence and belong to only one promoter class in RegulonDB database. Then, we applied the CNN model to recognize the six classes of promoters. The CNN model achieved an accuracy of above 97% for all six classes of promoters. Next, we extracted the weight matrix of the last convolution layer in CNN with the Grad-Cam algorithm, and convert the weight matrix to an information content matrix. Finally, we visualized the information content matrix as promoter logos using the logomaker tool and discover the promoter features in six classes of promoters. Our approach could not only find the previous described promoter feature regions, but could also discover promoter features with better sensitivity and accuracy. We provide a novel computational approach to discover features in biological sequences.
Discovering Escherichia coli K-12 Promoter Features Using Convolutional Neural Network, Computational Biology and Bioinformatics.
Vol. 8, No. 1,
2020, pp. 15-19.
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