武汉大学胥国勇小组的一项最新研究开发出了RNAirport——基于深度神经网络的植物代表性基因模型数据库。该项研究成果发表在2024年3月20日出版的《遗传学报》上。

该课题组人员开发了一种排序算法和一个深度学习模型，来注释五种植物物种的代表性5’前导区。研究团队对基于Krthemkal-Wallis测试算法的aS介导的转录异构体的样本内和样本间频率进行了排序，并确定了具有代表性的aS-5’前导区。为了进一步分配具有代表性的5'端，课题组研究人员训练了深度学习模型5’leaderP，以从cap分析基因表达(CAGE)数据中学习aTSS介导的5'端分布模式。

该模型准确地预测了5'端，并在拟南芥和水稻中得到了实验证实。具有代表性的包含5’前导区基因模型和5'- leaderP可在RNAirport (http://www.rnairport.com/leader5P/)上访问。第一阶段的5'前导区注释记录了5'前导区的多样性，并将为Ribo-Seq ORF注释铺平道路，与人类GENCODE最近启动的项目相同。

据了解，5’前导区，最初被称为5’非翻译区，由于选择性剪接(aS)和转录起始位点(aTSS)，包含多个同工异构体。因此，研究团队需要一个具有代表性的5’前导区，来检测嵌入的RNA调控元件在控制翻译效率方面的作用。

附：英文原文

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

Author: anonymous

Issue&Volume: 2024/03/20

Abstract: A 5′-leader, known initially as the 5′-untranslated region, contains multiple isoforms due to alternative splicings (aS) and transcription start sites (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- 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 (CAGE) 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/). This stage 1 5′-leader annotation records 5′-leader diversity and will pave the way to Ribo-Seq ORF annotation, identical to the project recently initiated by human GENCODE.

DOI: 10.1016/j.jgg.2024.03.004

Source: https://www.sciencedirect.com/science/article/pii/S1673852724000572