CRIP: predicting circRNA-RBP-binding sites using a codon-based encoding and hybrid deep neural networks

RNA-binding protein recognition based on multi-view deep feature and multi-label learning

RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict whether a specific RBP can bind to an RNA and the position of the binding site using binary classification model. However, most of the existing methods do not take into account the binding similarity and correlation between different RBPs. While methods employing multiple labels and Long Short Term Memory Network (LSTM) are proposed to consider binding similarity between different RBPs, the accuracy remains low due to insufficient feature learning and multi-label learning on RNA sequences. In response to this challenge, the concept of RNA-RBP Binding Network (RRBN) is proposed in this paper to provide theoretical support for multi-label learning to identify RBPs that can bind to RNAs. It is experimentally shown that the RRBN information can significantly improve the prediction of unknown RNA-RBP interactions. To further improve the prediction accuracy, we present the novel computational method iDeepMV which integrates multi-view deep learning technology under the multi-label learning framework. iDeepMV first extracts data from the views of amino acid sequence and dipeptide component based on the RNA sequences as the original view. Deep neural network models are then designed for the respective views to perform deep feature learning. The extracted deep features are fed into multi-label classifiers which are trained with the RNA-RBP interaction information for the three views. Finally, a voting mechanism is designed to make comprehensive decision on the results of the multi-label classifiers. Our experimental results show that the prediction performance of iDeepMV, which combines multi-view deep feature learning models with RNA-RBP interaction information, is significantly better than that of the state-of-the-art methods. iDeepMV is freely available at http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV for academic use. The code is freely available at http://github.com/uchihayht/iDeepMV.

Identifying Circular RNA and Predicting Its Regulatory Interactions by Machine Learning

Circular RNA (circRNA) is a closed long non-coding RNA (lncRNA) formed by covalently closed loops through back-splicing. Emerging evidence indicates that circRNA can influence cellular physiology through various molecular mechanisms. Thus, accurate circRNA identification and prediction of its regulatory information are critical for understanding its biogenesis. Although several computational tools based on machine learning have been proposed for circRNA identification, the prediction accuracy remains to be improved. Here, first we present circLGB, a machine learning-based framework to discriminate circRNA from other lncRNAs. circLGB integrates commonly used sequence-derived features and three new features containing adenosine to inosine (A-to-I) deamination, A-to-I density and the internal ribosome entry site. circLGB categorizes circRNAs by utilizing a LightGBM classifier with feature selection. Second, we introduce circMRT, an ensemble machine learning framework to systematically predict the regulatory information for circRNA, including their interactions with microRNA, the RNA binding protein, and transcriptional regulation. Feature sets including sequence-based features, graph features, genome context, and regulatory information features were modeled in circMRT. Experiments on public and our constructed datasets show that the proposed algorithms outperform the available state-of-the-art methods. circLGB is available at http://www.circlgb.com. Source codes are available at https://github.com/Peppags/circLGB-circMRT.

AC-Caps: Attention Based Capsule Network for Predicting RBP Binding Sites of LncRNA

Long non-coding RNA(lncRNA) is one of the non-coding RNAs longer than 200 nucleotides and it has no protein encoding function. LncRNA plays a key role in many biological processes. Studying the RNA-binding protein (RBP) binding sites on the lncRNA chain helps to reveal epigenetic and post-transcriptional mechanisms, to explore the physiological and pathological processes of cancer, and to discover new therapeutic breakthroughs. To improve the recognition rate of RBP binding sites and reduce the experimental time and cost, many calculation methods based on domain knowledge to predict RBP binding sites have emerged. However, these prediction methods are independent of nucleotides and do not take into account nucleotide statistics. In this paper, we use a high-order statistical-based encoding scheme, then the encoded lncRNA sequences are fed into a hybrid deep learning architecture named AC-Caps. It consists of a joint processing layer(composed of attention mechanism and convolutional neural network) and a capsule network. The AC-Caps model was evaluated using 31 independent experimental data sets from 12 lncRNA-binding proteins. In experiments, our method achieves excellent performance, with an average area under the curve (AUC) of 0.967 and an average accuracy (ACC) of 92.5%, which are 0.014, 2.3%, 0.261, 28.9%, 0.189, and 21.8% higher than HOCCNNLB, iDeepS, and DeepBind, respectively. The results show that the AC-Caps method can reliably process the large-scale RBP binding site data on the lncRNA chain, and the prediction performance is better than existing deep-learning models. The source code of AC-Caps and the datasets used in this paper are available at https://github.com/JinmiaoS/AC-Caps .

Matrix factorization with neural network for predicting circRNA-RBP interactions

Background

Circular RNA (circRNA) has been extensively identified in cells and tissues, and plays crucial roles in human diseases and biological processes. circRNA could act as dynamic scaffolding molecules that modulate protein-protein interactions. The interactions between circRNA and RNA Binding Proteins (RBPs) are also deemed to an essential element underlying the functions of circRNA. Considering cost-heavy and labor-intensive aspects of these biological experimental technologies, instead, the high-throughput experimental data has enabled the large-scale prediction and analysis of circRNA-RBP interactions.

Results

A computational framework is constructed by employing Positive Unlabeled learning (P-U learning) to predict unknown circRNA-RBP interaction pairs with kernel model MFNN (Matrix Factorization with Neural Networks). The neural network is employed to extract the latent factors of circRNA and RBP in the interaction matrix, the P-U learning strategy is applied to alleviate the imbalanced characteristics of data samples and predict unknown interaction pairs. For this purpose, the known circRNA-RBP interaction data samples are collected from the circRNAs in cancer cell lines database (CircRic), and the circRNA-RBP interaction matrix is constructed as the input of the model. The experimental results show that kernel MFNN outperforms the other deep kernel models. Interestingly, it is found that the deeper of hidden layers in neural network framework does not mean the better in our model. Finally, the unlabeled interactions are scored using P-U learning with MFNN kernel, and the predicted interaction pairs are matched to the known interactions database. The results indicate that our method is an effective model to analyze the circRNA-RBP interactions.

Conclusion

For a poorly studied circRNA-RBP interactions, we design a prediction framework only based on interaction matrix by employing matrix factorization and neural network. We demonstrate that MFNN achieves higher prediction accuracy, and it is an effective method.

PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs

Motivation

Different from traditional linear RNAs (containing 5′ and 3′ ends), circular RNAs (circRNAs) are a special type of RNAs that have a closed ring structure. Accumulating evidence has indicated that circRNAs can directly bind proteins and participate in a myriad of different biological processes.

Results

For identifying the interaction of circRNAs with 37 different types of circRNA-binding proteins (RBPs), we develop an ensemble neural network, termed PASSION, which is based on the concatenated artificial neural network (ANN) and hybrid deep neural network frameworks. Specifically, the input of the ANN is the optimal feature subset for each RBP, which has been selected from six types of feature encoding schemes through incremental feature selection and application of the XGBoost algorithm. In turn, the input of the hybrid deep neural network is a stacked codon-based scheme. Benchmarking experiments indicate that the ensemble neural network reaches the average best area under the curve (AUC) of 0.883 across the 37 circRNA datasets when compared with XGBoost, k-nearest neighbor, support vector machine, random forest, logistic regression and Naive Bayes. Moreover, each of the 37 RBP models is extensively tested by performing independent tests, with the varying sequence similarity thresholds of 0.8, 0.7, 0.6 and 0.5, respectively. The corresponding average AUC obtained are 0.883, 0.876, 0.868 and 0.883, respectively, highlighting the effectiveness and robustness of PASSION. Extensive benchmarking experiments demonstrate that PASSION achieves a competitive performance for identifying binding sites between circRNA and RBPs, when compared with several state-of-the-art methods.

Availability and implementation

A user-friendly web server of PASSION is publicly accessible at http://flagship.erc.monash.edu/PASSION/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational predictive approaches for interaction and structure of aptamers

Aptamers are short single-strand sequences that can bind to their specific targets with high affinity and specificity. Usually, aptamers are selected experimentally via systematic evolution of ligands by exponential enrichment (SELEX), an evolutionary process that consists of multiple cycles of selection and amplification. The SELEX process is expensive, time-consuming, and its success rates are relatively low. To overcome these difficulties, in recent years, several computational techniques have been developed in aptamer sciences that bring together different disciplines and branches of technologies. In this paper, a complementary review on computational predictive approaches of the aptamer has been organized. Generally, the computational prediction approaches of aptamer have been proposed to carry out in two main categories: interaction-based prediction and structure-based predictions. Furthermore, the available software packages and toolkits in this scope were reviewed. The aim of describing computational methods and tools in aptamer science is that aptamer scientists might take advantage of these computational techniques to develop more accurate and more sensitive aptamers.

Critical function of circular RNAs in lung cancer

Lung cancer is one of the main causes of cancer-related death in the world, especially due to its frequency and ineffective therapeutically approaches in the late stages of the disease. Despite the recent advent of promising new targeted therapies, lung cancer diagnostic strategies still have difficulty in identifying the disease at an early stage. Therefore, the characterizations of more sensible and specific cancer biomarkers have become an important goal for clinicians. Circular RNAs (circRNAs), a type of RNA with covalently closed continuous loop structures that display high structural resistance and tissue specificity pointed toward a potential biomarker role. Current investigations have identified that circRNAs have a prominent function in the regulation of oncogenic pathways, by regulating gene expression both at transcriptional and post-transcriptional level. The aim of this review is to provide novel information regarding the implications of circRNAs in lung cancer, with an emphasis on the role in disease development and progression. Initially, we explored the potential utility of circRNAs as biomarkers, focusing on function, mechanisms, and correlation with disease progression in lung cancer. Further, we will describe the interaction between circRNAs and other non-coding species of RNA (particularly microRNA) and their biological significance in lung cancer. Describing the nature of these interactions and their therapeutic potential will provide additional insight regarding the altered molecular landscape of lung cancer and consolidate the potential clinical value of these circular transcripts. This article is categorized under: RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Circular RNA-protein interactions: functions, mechanisms, and identification

Circular RNAs (circRNAs) are covalently closed, endogenous RNAs with no 5' end caps or 3' poly(A) tails. These RNAs are expressed in tissue-specific, cell-specific, and developmental stage-specific patterns. The biogenesis of circRNAs is now known to be regulated by multiple specific factors; however, circRNAs were previously thought to be insignificant byproducts of splicing errors. Recent studies have demonstrated their activity as microRNA (miRNA) sponges as well as protein sponges, decoys, scaffolds, and recruiters, and some circRNAs even act as translation templates in multiple pathophysiological processes. CircRNAs bind and sequester specific proteins to appropriate subcellular positions, and they participate in modulating certain protein-protein and protein-RNA interactions. Conversely, several proteins play an indispensable role in the life cycle of circRNAs from biogenesis to degradation. However, the exact mechanisms of these interactions between proteins and circRNAs remain unknown. Here, we review the current knowledge regarding circRNA-protein interactions and the methods used to identify and characterize these interactions. We also summarize new insights into the potential mechanisms underlying these interactions.

Circular RNA circSLC26A4 Accelerates Cervical Cancer Progression via miR-1287-5p/HOXA7 Axis

Circular RNAs (circRNAs) are group of noncoding RNAs derived from back-splicing events. Accumulating evidence certifies the critical roles of circRNAs in human tumorigenesis. However, the role and biogenesis of circRNAs in cervical cancer are still unclear. Here, a novel identified circRNA, circSLC26A4, was found to be upregulated in cervical cancer tissue and cells. Clinically, the high expression of circSLC26A4 was related to the poor survival of cervical cancer patients. Functionally, cellular experiments indicated that circSLC26A4 knockdown repressed the proliferation, invasion, and tumor growth in vitro and in vivo. Furthermore, circSLC26A4 acted as the sponge of miR-1287-5p; moreover, miR-1287-5p targeted the 3′ UTR of HOXA7 mRNA. Mechanistically, RNA binding protein (RBP) quaking (QKI) was identified to interact with the QKI response elements (QREs) in SLC26A4 gene introns, thereby promoting circSLC26A4 biogenesis. In conclusion, these findings demonstrate that circSLC26A4 facilitates cervical cancer progression through the QKI/circSLC26A4/miR-1287-5p/HOXA7 axis, which might bring novel therapeutic strategies for cervical cancer.

CircSLNN: Identifying RBP-Binding Sites on circRNAs via Sequence Labeling Neural Networks

The interactions between RNAs and RNA binding proteins (RBPs) are crucial for understanding post-transcriptional regulation mechanisms. A lot of computational tools have been developed to automatically predict the binding relationship between RNAs and RBPs. However, most of the methods can only predict the presence or absence of binding sites for a sequence fragment, without providing specific information on the position or length of the binding sites. Besides, the existing tools focus on the interaction between RBPs and linear RNAs, while the binding sites on circular RNAs (circRNAs) have been rarely studied. In this study, we model the prediction of binding sites on RNAs as a sequence labeling problem, and propose a new model called circSLNN to identify the specific location of RBP-binding sites on circRNAs. CircSLNN is driven by pretrained RNA embedding vectors and a composite labeling model. On our constructed circRNA datasets, our model has an average F₁ score of 0.790. We assess the performance on full-length RNA sequences, the proposed model outperforms previous classification-based models by a large margin.