Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan.
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.
BMC Bioinformatics. 2021 Oct 16;22(1):503. doi: 10.1186/s12859-021-04428-6.
Piwi-interacting RNAs (piRNAs) are the small non-coding RNAs (ncRNAs) that silence genomic transposable elements. And researchers found out that piRNA also regulates various endogenous transcripts. However, there is no systematic understanding of the piRNA binding patterns and how piRNA targets genes. While various prediction methods have been developed for other similar ncRNAs (e.g., miRNAs), piRNA holds distinctive characteristics and requires its own computational model for binding target prediction.
Recently, transcriptome-wide piRNA binding events in C. elegans were probed by PRG-1 CLASH experiments. Based on the probed piRNA-messenger RNAs (mRNAs) binding pairs, in this research, we devised the first deep learning architecture based on multi-head attention to computationally identify piRNA targeting mRNA sites. In the devised deep network, the given piRNA and mRNA segment sequences are first one-hot encoded and undergo a combined operation of convolution and squeezing-extraction to unravel motif patterns. And we incorporate a novel multi-head attention sub-network to extract the hidden piRNA binding rules that can simulate the biological piRNA target recognition process. Finally, the true piRNA-mRNA binding pairs are identified by a deep fully connected sub-network. Our model obtains a supreme discriminatory power of AUC [Formula: see text] 93.3% on an independent test set and successfully extracts the verified binding pattern of a synthetic piRNA. These results demonstrated that the devised model achieves high prediction performance and suggests testable potential biological piRNA binding rules.
In this research, we developed the first deep learning method to identify piRNA targeting sites on C. elegans mRNAs. And the developed deep learning method is demonstrated to be of high accuracy and can provide biological insights into piRNA-mRNA binding patterns. The piRNA binding target identification network can be downloaded from http://cosbi2.ee.ncku.edu.tw/data_download/piRNA_mRNA_binding .
Piwi 相互作用 RNA（piRNA）是沉默基因组转座元件的小非编码 RNA（ncRNA）。研究人员发现 piRNA 还调节各种内源性转录本。然而，对于 piRNA 结合模式以及 piRNA 如何靶向基因，还没有系统的认识。虽然已经开发了用于其他类似 ncRNA（例如 miRNA）的各种预测方法，但 piRNA 具有独特的特征，需要为其结合靶标预测开发自己的计算模型。
最近，通过 PRG-1 CLASH 实验探测了秀丽隐杆线虫中的全转录组 piRNA 结合事件。基于探测到的 piRNA-信使 RNA（mRNA）结合对，在这项研究中，我们设计了第一个基于多头注意力的深度学习架构，用于计算识别 piRNA 靶向 mRNA 位点。在设计的深度网络中，给定的 piRNA 和 mRNA 片段序列首先进行独热编码，并进行卷积和挤压提取的组合操作，以揭示模式。我们还整合了一个新的多头注意子网络，以提取隐藏的 piRNA 结合规则，这些规则可以模拟生物 piRNA 靶识别过程。最后，通过深度全连接子网络识别真正的 piRNA-mRNA 结合对。我们的模型在独立测试集上获得了卓越的判别能力，AUC[公式：见正文]93.3%，并成功提取了合成 piRNA 的验证结合模式。这些结果表明，所设计的模型具有较高的预测性能，并提出了可测试的潜在生物 piRNA 结合规则。
在这项研究中，我们开发了第一个用于识别秀丽隐杆线虫 mRNA 上 piRNA 靶向位点的深度学习方法。所开发的深度学习方法具有很高的准确性，并能为 piRNA-mRNA 结合模式提供生物学见解。piRNA 结合靶标识别网络可从 http://cosbi2.ee.ncku.edu.tw/data_download/piRNA_mRNA_binding 下载。
