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, Han Cheng Search for other works by this author on: Oxford Academic Liping Xu Mathematics Department of the School of Science, Dalian Maritime University , Dalian 116026 , China Search for other works by this author on: Oxford Academic Cangzhi Jia Mathematics Department of the School of Science, Dalian Maritime University , Dalian 116026 , China Corresponding author. Mathematics Department of the School of Science, Dalian Maritime University, Dalian 116026, China. E-mail: cangzhijia@dlmu.edu.cn Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0002-4682-2881 Briefings in Functional Genomics, elae027, https://doi.org/10.1093/bfgp/elae027
Published:
23 June 2024
Article history
Received:
25 March 2024
Revision received:
24 May 2024
Accepted:
06 June 2024
Published:
23 June 2024
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Han Cheng, Liping Xu, Cangzhi Jia, Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework, Briefings in Functional Genomics, 2024;, elae027, https://doi.org/10.1093/bfgp/elae027
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Abstract
RNA interference (RNAi) technology is widely used in the biological prevention and control of terrestrial insects. One of the main factors with the application of RNAi in insects is the difference in RNAi efficiency, which may vary not only in different insects, but also in different genes of the same insect, and even in different double-stranded RNAs (dsRNAs) of the same gene. This work focuses on the last question and establishes a bioinformatics software that can help researchers screen for the most efficient dsRNA targeting target genes. Among insects, the red flour beetle (Tribolium castaneum) is known to be one of the most sensitive to RNAi. From iBeetle-Base, we extracted 12027 efficient dsRNA sequences with a lethality rate of ≥20% or with experimentation-induced phenotypic changes and processed these data to correspond to specific silence efficiency. Based on the first complied novel benchmark dataset, we specifically designed a deep neural network to identify and characterize efficient dsRNA for RNAi in insects. The dna2vec word embedding model was trained to extract distributed feature representations, and three powerful modules, namely convolutional neural network, bidirectional long short-term memory network, and self-attention mechanism, were integrated to form our predictor model to characterize the extracted dsRNAs and their silencing efficiencies for T. castaneum. Our model dsRNAPredictor showed reliable performance in multiple independent tests based on different species, including both T. castaneum and Aedes aegypti. This indicates that dsRNAPredictor can facilitate prescreening for designing high-efficiency dsRNA targeting target genes of insects in advance.
RNAi, insect, red flour beetle, deep learning, dsRNA design
© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)
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