Deep-piRNA:Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

Piwi-interacting Ribonucleic acids(piRNAs)molecule is a wellknown subclass of small non-codingRNAmolecules that are mainly responsible for maintaining genome integrity,regulating gene expression,and germline stem cell maintenance by suppressing transposon elements.The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development.Due to the vital roles of the piRNA in computational biology,the identification of piRNAs has become an important area of research in computational biology.This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods.The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process.The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method.The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59%and 2.81%at layer I and layer II respectively.It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine.

NaIO4氧化–深度测序技术的优化用于检测微量RNA样品中小RNA 3′末端甲基化修饰

2′-O-甲基化是存在于多种小RNA 3′末端的修饰,具有重要的生物学功能。目前高通量研究小RNA 3′末端2′-O-甲基化的方法主要是NaIO4氧化处理结合小RNA深度测序,但该方法需要3μg总RNA,难以用于研究少量细胞中小RNA的3′末端甲基化修饰。该研究通过调整NaIO4氧化处理的条件以及测试有无甘油终止氧化反应对小RNA文库构建的影响,优化了适用于微量RNA的氧化条件,实现对10 ng小鼠睾丸样本总RNA中的小RNA 3′末端甲基化修饰的深度测序检测,建立了用于检测微量RNA样品中小RNA 3′末端甲基化修饰的方法,为检测少量细胞中小RNA的3′末端甲基化修饰提供了有效方法。