LSTM4piRNA: Efficient piRNA Detection in Large-Scale Genome Databases Using a Deep Learning-Based LSTM Network.

Piwi-interacting RNAs (piRNAs) are a new class of small, non-coding RNAs, crucial in the regulation of gene expression. Recent research has revealed links between piRNAs, viral defense mechanisms, and certain human cancers. Due to their clinical potential, there is a great interest in identifying piRNAs from large genome databases through efficient computational methods. However, piRNAs lack conserved structure and sequence homology across species, which makes piRNA detection challenging. Current detection algorithms heavily rely on manually crafted features, which may overlook or improperly use certain features. Furthermore, there is a lack of suitable computational tools for analyzing large-scale databases and accurately identifying piRNAs. To address these issues, we propose LSTM4piRNA, a highly efficient deep learning-based method for predicting piRNAs in large-scale genome databases. LSTM4piRNA utilizes a compact LSTM network that can effectively analyze RNA sequences from extensive datasets to detect piRNAs. It can automatically learn the dependencies among RNA sequences, and regularization is further integrated to reduce the generalization error. Comprehensive performance evaluations based on piRNAs from the piRBase database demonstrate that LSTM4piRNA outperforms current advanced methods and is well-suited for analysis with large-scale databases.

REDfold: accurate RNA secondary structure prediction using residual encoder-decoder network.

BACKGROUND: As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA secondary prediction is mainly based on the thermodynamic model with dynamic programming to find the optimal structure. However, the prediction performance based on the traditional approach is unsatisfactory for further research. Besides, the computational complexity of the structure prediction using dynamic programming is [Formula: see text]; it becomes [Formula: see text] for RNA structure with pseudoknots, which is computationally impractical for large-scale analysis. RESULTS: In this paper, we propose REDfold, a novel deep learning-based method for RNA secondary prediction. REDfold utilizes an encoder-decoder network based on CNN to learn the short and long range dependencies among the RNA sequence, and the network is further integrated with symmetric skip connections to efficiently propagate activation information across layers. Moreover, the network output is post-processed with constrained optimization to yield favorable predictions even for RNAs with pseudoknots. Experimental results based on the ncRNA database demonstrate that REDfold achieves better performance in terms of efficiency and accuracy, outperforming the contemporary state-of-the-art methods.