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. 2018 Apr 16;8:6005. doi: 10.1038/s41598-018-24298-y

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The architecture and performance of DeepRed. (A) The hybrid framework of DeepRed. DeepRed consists of separate and pooled components to account for the features of RNA editing derived from the separate samples and pooled samples methods, respectively. Each component combines 11 independent single-cell modules together using a simple averaging method. The separate and pooled components are combined by a simple averaging method. The input of DeepRed is a one-hot-encoded sequence of 201 base pairs (bp) centred at the candidate SNV. (B) The ROC curves (grey) of DeepRed were achieved in 22 gold standard sets in the training set. The average ROC curve of DeepRed in the training set is presented as a blue curve. (C) The ROC curves (grey) of DeepRed were achieved in 42 gold standard sets in the test set. The average ROC curve of DeepRed in the test set is presented as a blue curve.