iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory

Background and objective: The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods.Methods: In this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Sec-ondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was uti-lized to extract global features. Finally, we adopted the self-attention mechanism to improve the contri-bution of relatively important features, which further enhances the performance of the model.Results: Our model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recog-nition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength pre-diction.Conclusions: our model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The study introduced a new hybrid model to identify promoters and predict their strength, achieving higher accuracy by combining different neural networks and mechanisms.