2lpiRNApred: a two-layered integrated algorithm for identifying piRNAs and their functions based on LFE-GM feature selection

Piwi–interacting RNAs (piRNAs) are indispensable in the transposon silencing, including in germ cell formation, germline stem cell maintenance, spermatogenesis, and oogenesis. piRNA pathways are amongst the major genome defence mechanisms, which maintain genome integrity. They also have important functions in tumorigenesis, as indicated by aberrantly expressed piRNAs being recently shown to play roles in the process of cancer development. A number of computational methods for this have recently been proposed, but they still have not yielded satisfactory predictive performance. Moreover, only one computational method that identifies whether piRNAs function in inducting target mRNA deadenylation been reported in the literature. In this study, we developed a two-layered integrated classifier algorithm, 2lpiRNApred. It identifies piRNAs in the first layer and determines whether they function in inducting target mRNA deadenylation in the second layer. A new feature selection algorithm, which was based on Luca fuzzy entropy and Gaussian membership function (LFE-GM), was proposed to reduce the dimensionality of the features. Five feature extraction strategies, namely, Kmer, General parallel correlation pseudo-dinucleotide composition, General series correlation pseudo-dinucleotide composition, Normalized Moreau–Broto autocorrelation, and Geary autocorrelation, and two types of classifier, Sparse Representation Classifier (SRC) and support vector machine with Mahalanobis distance-based radial basis function (SVMMDRBF), were used to construct a two-layered integrated classifier algorithm, 2lpiRNApred. The results indicate that 2lpiRNApred performs significantly better than six other existing prediction tools.

Piwi相互作用RNA（Piwi-interacting RNAs，piRNAs）在转座子沉默过程中不可或缺，参与生殖细胞生成、生殖系干细胞维持、精子发生与卵子发生等生理过程。piRNA通路是维持基因组完整性的核心基因组防御机制之一。此外，piRNA在肿瘤发生进程中同样发挥关键作用：近期研究证实异常表达的piRNAs参与了癌症发生发展过程。目前针对piRNA的各类计算预测方法已相继被提出，但现有工具的预测性能仍未达到理想水平。值得注意的是，目前文献中仅报道了一种可识别piRNAs是否具备诱导靶mRNA脱腺苷化功能的计算方法。本研究开发了一款双层集成分类器算法2lpiRNApred：第一层用于精准识别piRNAs，第二层则用于判定目标piRNA是否具有诱导靶mRNA脱腺苷化的功能。为实现特征降维，本研究提出了一种基于卢卡模糊熵与高斯隶属函数的新型特征选择算法（LFE-GM）。本研究整合了5种特征提取策略，包括K元组特征（Kmer）、广义并行关联伪二核苷酸组成、广义序列关联伪二核苷酸组成、归一化莫罗-布罗托自相关与吉尔里自相关，同时采用2种分类器，即稀疏表示分类器（SRC，Sparse Representation Classifier）与基于马氏距离径向基核的支持向量机（SVMMDRBF，support vector machine with Mahalanobis distance-based radial basis function），来构建双层集成分类器框架2lpiRNApred。实验结果表明，2lpiRNApred的综合性能显著优于其他6种现有主流预测工具。