piRNN: deep learning algorithm for piRNA prediction

Data sets

In our program, C. elegans, D. melanogaster, rat and human piRNAs were used for training models. C. elegans, rat and human data were downloaded from piRBase (Zhang et al., 2014). D. melanogaster data were downloaded from piRNABank (Lakshmi & Agrawal, 2008). In total, 28,219 C. elegans piRNA sequences, 22,336 D. melanogaster piRNA sequences, 63,182 rat piRNA sequences, and 32,826 human piRNA sequences were used as the positive datasets. To develop a powerful DNA/RNA sequence classifier, one of the most critical and challenging problems is how to generate a valid negative dataset that can be used effectively in model training and testing. Previous researches such as PseKNC (Chen et al., 2014) and Pse-in-One (Liu et al., 2015) provided comprehensive methods that can be used to generate pseudo DNA/RNA sequences (i.e., negative dataset) for computational biology research. Based on the previous study (Brayet et al., 2014), the negative datasets of each species consist of three parts: (1) corresponding fake-piRNA sequences generated using the first order Markov model where the probability distribution came from the positive datasets, (2) corresponding mature microRNA sequences downloaded from miRBase (Kozomara & Griffiths-Jones, 2014), and (3) tRNA fragments of certain lengths randomly cut from tRNA sequences downloaded from Genomic tRNA database (Chan & Lowe, 2009). Accordingly, both positive and negative datasets have the same sequence numbers, respectively, for each species. Each species’ dataset, both positive and negative data, was divided into two parts: training-testing dataset (90%) and validation dataset (10%). All models were trained using 10-fold cross validation with the training-testing data set. The validation dataset was used for comparison with other programs. More details can be found in the Supplemental File.

Sequence feature extraction

Previous studies demonstrate that sequence-derived features can be efficiently used for developing different types of DNA/RNA sequence classification programs (Zhang et al., 2017a, 2017b). In our program, the sequence feature consists of two parts. The first part is k-mer (k = 1, 2, 3, 4, 5) motif frequencies. In total, 1,364 (sum of 4¹, 4², 4³, 4⁴, and 4⁵) values were extracted from each sequence. For the second part, we counted the k-mer motifs around the first and 10th base because piRNA has a high probability starting with a “T/U” and containing an “A” at 10th position (Hirakata & Siomi, 2016). Specifically, if the sequence starts with a “T/U” and/or has an “A” in the 10th position, then we updated the second part feature vectors into the first vectors as shown in Fig. 1. For training, the total 1,364 vectors were transformed into a 4 × 341 matrix (Fig. 1).

CNN architecture

A CNN is a feed-forward artificial neural network that has been widely used in image recognition. In this paper, we have implemented a CNN for piRNA identification by using TensorFlow (Abadi et al., 2016) and Keras (Chollet, 2015). The whole program was implemented in Python 3.5. The architecture of piRNN is showed in Fig. 2. The input of each sequence is a 4 × 341 matrix. Each element of this matrix represents k-mer motifs frequencies as shown in Fig. 1. Two one-hot vectors ([0, 1], [1, 0]) were used to label the piRNA and non-piRNA sequences. The first part of our network is two convolution layers. A total of 32 2 × 2 filters were used in these two convolution layers to scan the input matrixes. The second part is a max pooling layer with 2 × 2 pooling size. A dropout layer with 0.25 dropout ratio is connected to the max pooling layer. The third part is a dense layer with 512 nodes. We applied a 50% dropout after the dense layer to prevent over fitting. The last layer is the outputs layer with two nodes that correspond to the piRNAs and non-piRNAs.

Model training

A total of 10-fold cross validation was used for model training in four species individually. A small batch size of 32 was applied in all training processes. During the 10-fold training for all four species, the training process will be stopped if the value of cost function did not decrease after 10 epochs. Since the input of our program is a small matrix (4 × 341) and it is easy to get overfitting in CNN training, we used a simple CNN architecture that only contains two convolution layers, one max pooling layer, and one dense layer, with two dropout layers (see Fig. 2). All parameters used in training were set in terms of the default values from Keras document (https://keras.io/). The four species models were trained by using the same CNN architecture because we are aiming to establish a piRNA prediction tool that can be easily used by biologists. It is worth mentioning that piRNN allows re-training to get better models if more data is available.

Classification performance

After training, four species–specific models are tested using their relevant test datasets to evaluate model performance. Figure 3 shows the performance of piRNN. Accuracy (ACC), precision (Pre), sensitivity (Sn), specificity (Sp), and Matthews correlation coefficient (MCC) were used for performance evaluation. These measurements were calculated from the testing datasets by 10-fold cross validation. Since piRNA is small non-coding RNA only with ∼30 nt in length, a large number of k in feature extraction would produce many useless features (value equals 0). Based on the testing results and taking into account the calculation cost and operation time, we selected k = 5 lastly for our program.

Comparison with other methods

Previous piRNA prediction/identification programs can be classified into two categories or groups. The first one is those that only focused on predicting transposon-related piRNAs, such as piRNAPredictor (Li et al., 2016), Piano (Wang et al., 2014), etc. These programs usually can achieve very high accuracy and precision. Since the transposon-related piRNAs are only a small portion of total piRNAs, especially in mammals, it is easy to train a model with good performance on small datasets. In this context, non-transposon-related piRNAs (over half of total piRNAs) would be excluded from the prediction results (Beyret, Liu & Lin, 2012). Since piRNAs are also showed to silence certain mRNAs (Rouget et al., 2010), these programs have obvious limitation in piRNA prediction. The second group of programs can be used to predict total piRNAs from small RNA data. Example include piRNApredictor (Zhang, Wang & Kang, 2011), piRPred (Brayet et al., 2014), and IpiRId (Boucheham et al., 2017). Combined with genomic features like piRNA genome locations and epigenetic data, piRPred and IpiRId achieved better performance than the piRNApredictor in piRNA identification. Since they need genomic features for prediction, it is hard to apply these programs to non-model organisms that lack enough genomics resources.

For performance comparison, we compared our program with Piano (Wang et al., 2014), 2L-piRNA (Liu, Yang & Chou, 2017), and piRNApredictor (Zhang, Wang & Kang, 2011). Piano is the first piRNA prediction program that only focused on transposon-related piRNAs. piRNApredictor is the first software that can predict piRNAs using small RNA-Seq data. piRPred (Brayet et al., 2014), Pibomd (Liu, Ding & Gong, 2014), V-ELMpiRNAPred (Pian et al., 2017), and IpiRId (Boucheham et al., 2017) are web-based tools that are unable to execute locally. piRNAPredictor (Li et al., 2016) does not provide any standalone downloadable programs. Therefore, we only selected Piano, 2L-piRNA, and piRNApredictor for the performance comparison. A pervious study shows that D. melanogaster has different length distribution from mammal piRNAs (Wang et al., 2014), so we only used human data that is representative of mammal piRNAs and D. melanogaster piRNAs for comparison. Our comparison results are listed in Table 1. piRNN shows the best performance for all five measurements for both species, with accuracy, precision, sensitivity, specificity, and Matthews correlation coefficient of over 90% (see Table 1). Compared to the results provided in the paper of IpiRId (accuracy for human and fruit fly is 90.09 ± 0.25 and 92.59 ± 1.87, respectively), our program piRNN also demonstrated better performance results than IpiRId on both fruit fly and human data. Considering the fact that our program is the one with the best performance and only utilizes k-mer features without other data (e.g., genome location or epigenetic data) in piRNA prediction, piRNN is more user-friendly for biologists to apply on new data. Clearly, piRNN is a useful tool for piRNA prediction in non-model organisms where genomics resources are limited.