G-quadruplexes (G4s) are a class of stable structural nucleic acid secondary structures that are known to play a role in a wide spectrum of genomic functions, such as DNA replication and transcription. The classical understanding of G4 structure points to four variable length guanine strands joined by variable length nucleotide stretches. Experiments using G4 immunoprecipitation and sequencing experiments have produced a high number of highly probable G4 forming genomic sequences. The expense and technical difficulty of experimental techniques highlights the need for computational approaches of G4 identification. Here, we present PENGUINN, a machine learning method based on Convolutional neural networks, that learns the characteristics of G4 sequences and accurately predicts G4s outperforming state-of-the-art methods. We provide both a standalone implementation of the trained model, and a web application that can be used to evaluate sequences for their G4 potential.

G-四链体 (G4) 是一类稳定的核酸二级结构，已知在广泛的基因组功能中发挥作用，例如 DNA 复制和转录。 G4 结构的经典理解指向由可变长度核苷酸片段连接的四个可变长度鸟嘌呤链。使用 G4 免疫沉淀和测序实验的实验已经产生了大量极有可能形成 G4 的基因组序列。实验技术的费用和技术难度凸显了对 G4 识别计算方法的需求。在这里，我们提出了 PENGUINN，一种基于卷积神经网络的机器学习方法，它可以学习 G4 序列的特征并准确预测 G4，其性能优于最先进的方法。我们提供训练模型的独立实现，以及可用于评估序列 G4 潜力的 Web 应用程序。