G-quadruplexes (G4s) are a class of stable structural nucleic acid motifs 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 stretches of other nucleotides. 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 the state-of-the-art. 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.

中文翻译：

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宾夕法尼亚：使用可解释的神经网络精确探索核G四联体

G-四链体（G4s）是一类稳定的结构核酸基序，已知在广泛的基因组功能（例如DNA复制和转录）中起作用。对G4结构的经典理解指向通过其他核苷酸的可变长度延伸连接的四个可变长度的鸟嘌呤链。使用G4免疫沉淀的实验和测序实验已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识别的计算方法的需求。在这里，我们介绍PENGUINN，这是一种基于卷积神经网络的机器学习方法，可以学习G4序列的特征并准确预测G4的性能优于最新技术。