## 1 Introduction  
Piwi-interacting RNA (PiRNA), a newly identified class of small non-coding RNA of which the length is 26-33 nt, can combine with PIWI proteins to regulate a transcription gene silencing process, heterochromatin modifications and to maintain germline and stem cell function in animals [1; 2; 3; 4]. However, high-throughput sequencing indicates that tens of thousands of different piRNAs produced in various animals cannot recognize transposons [5]. Therefore, the function of piRNA needs to be further investigated. Experimental verification of piRNA targets and the piRNA-targeting rules are quite difficult to prove [6]. Crosslinking immunoprecipitation (CLIP) analyses of PIWIs suggest that they associate with diverse mRNAs. However, because diverse piRNAs engage with many mRNAs, it is hard to infer the target of a given piRNA from these CLIP analyses [6; 7; 8]. Therefore, additional approaches are required to distinguish piRNA sites in vivo.  
In recent years, several computing biology tools have been proposed to identify piRNAs. The first model to identify piRNAs was piRNApredictor, firstly developed by [PERSON] et al. [9]. After three years, [PERSON] et al. [10] proposed the second model for predicting piRNAs based on the transposon interaction and SVM (Support Vector Machine). Recently, [PERSON] et al. [11] applied the sequence information and physicochemical features of piRNAs and non-piRNAs to construct the model to predict piRNAs. In addition, [PERSON] et al. [12] used a powerful ensemble approach, which achieved a substantial improvement. According to the most attractive work by [PERSON] et al. [13], 2L-piRNA (two-layer ensemble classifier for identifying piRNAs) can be used to identify piRNAs and their function types. 2L-piRNA yielded an accuracy of 86.1% for identifying piRNAs and non-piRNAs, and achieved an accuracy of 77.6% for identifying piRNAs with the function of instructing target mRNA deadenylation and piRNAs without the function of instructing target mRNA deadenylation.  
Aiming to improve the prediction accuracy, we developed a novel predictor, 2L-piRNAPred (2-layer integrated program for identifying piRNAs in the first layer and determining if piRNAs have the function of instructing target mRNA deadenylation in the second layer.), by considering single nucleotide composition (SNC), the 16 dinucleotides compositions (DNC), six physicochemical properties in RNA, the position specificities of nucleotides both in N-terminal and C-terminal, and the proportions of similar peptide sequences of both N-terminal and C-terminal in the positive and negative samples. Consequently, F-Score was utilized to select the most efficient unique type of features. Furthermore, all the optimized feature vectors were combined to build our prediction model based on a support vector machine classifier. Both the jackknife test and 5-fold cross-validation running 10 times were implemented to test the stability and robustness of the model. In addition, the major comparison with the previous work based on 2L-piRNA showed that our model, 2L-piRNAPred, is superior both in sensitivity and specificity for the first layer and the second layer.