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Volume 51 Issue 6
Jun.  2024
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Article Navigation >  Journal of Genetics and Genomics  > 2024  >  51(6): 652-664

RNAirport: a deep neural network-based database characterizing representative gene models in plants

doi: 10.1016/j.jgg.2024.03.004
  •  

    a. State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China;

  •  

    b. Hubei Hongshan Laboratory, Wuhan, Hubei 430070, China

Funds:

D Program of China (2023ZD04073), the Major Project of Hubei Hongshan Laboratory (2022hszd016), the Key Research and Development Program of Hubei Province (2022BFE003), and the National Natural Science Foundation of China (32070284) to G. Xu.

This study was supported by grants from the National Key R&

  • Received Date: 2024-03-03
  • Accepted Date: 2024-03-16
  • Rev Recd Date: 2024-03-15
    • Available Online: 2025-06-06
  • Publish Date: 2024-03-20
    Abstract
  • A 5′-leader, known initially as the 5′-untranslated region, contains multiple isoforms due to alternative splicing (aS) and alternative transcription start site (aTSS). Therefore, a representative 5′-leader is demanded to examine the embedded RNA regulatory elements in controlling translation efficiency. Here, we develop a ranking algorithm and a deep-learning model to annotate representative 5′-leaders for five plant species. We rank the intra-sample and inter-sample frequency of aS-mediated transcript isoforms using the Kruskal–Wallis test-based algorithm and identify the representative aS-5′-leader. To further assign a representative 5′-end, we train the deep-learning model 5′leaderP to learn aTSS-mediated 5′-end distribution patterns from cap-analysis gene expression data. The model accurately predicts the 5′-end, confirmed experimentally in Arabidopsis and rice. The representative 5′-leader-contained gene models and 5′leaderP can be accessed at RNAirport (http://www.rnairport.com/leader5P/). The Stage 1 annotation of 5′-leader records 5′-leader diversity and will pave the way to Ribo-Seq open-reading frame annotation, identical to the project recently initiated by human GENCODE.
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