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

MSTCRB: Predicting circRNA-RBP interaction by extracting multi-scale features based on transformer and attention mechanism

CircRNAs play vital roles in biological system mainly through binding RNA-binding protein (RBP), which is essential for regulating physiological processes in vivo and for identifying causal disease variants. Therefore, predicting interactions between circRNA and RBP is a critical step for the discovery of new therapeutic agents. Application of various deep-learning models in bioinformatics has significantly improved prediction and classification performance. However, most of existing prediction models are only applicable to specific type of RNA or RNA with simple characteristics. In this study, we proposed an attractive deep learning model, MSTCRB, based on transformer and attention mechanism for extracting multi-scale features to predict circRNA-RBP interactions. Therein, K-mer and KNF encoding are employed to capture the global sequence features of circRNA, NCP and DPCP encoding are utilized to extract local sequence features, and the CDPfold method is applied to extract structural features. In order to improve prediction performance, optimized transformer framework and attention mechanism were used to integrate these multi-scale features. We compared our model's performance with other five state-of-the-art methods on 37 circRNA datasets and 31 linear RNA datasets. The results show that the average AUC value of MSTCRB reaches 98.45 %, which is better than other comparative methods. All of above datasets are deposited in https://github.com/chy001228/MSTCRB_database.git and source code are available from https://github.com/chy001228/MSTCRB.git.

Mitochondrial genome-derived circRNAs: Orphan epigenetic regulators in molecular biology

Mitochondria are vital for cellular activities, influencing ATP production, Ca2+ signaling, and reactive oxygen species generation. It has been proposed that nuclear genome-derived circular RNAs (circRNAs) play a role in biological processes. For the first time, this study aims to comprehensively explore experimentally confirmed human mitochondrial genome-derived circRNAs (mt-circRNAs) via in-silico analysis. We utilized wide-ranging bioinformatics tools to anticipate their roles in molecular biology, involving miRNA sponging, protein antagonism, and peptide translation. Among five well-characterized mt-circRNAs, SCAR/mc-COX2 stands out as particularly significant with the potential to sponge around 41 different miRNAs, which target several genes mostly involved in endocytosis, MAP kinase, and PI3K-Akt pathways. Interestingly, circMNTND5 and mecciND1 specifically interact with miRNAs through their unique back-splice junction sequence. These exclusively targeted miRNAs (has-miR-5186, 6888-5p, 8081, 924, 672-5p) are predominantly associated with insulin secretion, proteoglycans in cancer, and MAPK signaling pathways. Moreover, all mt-circRNAs intricately affect the P53 pathway through miRNA sequestration. Remarkably, mc-COX2 and circMNTND5 appear to be involved in the RNA's biogenesis by antagonizing AGO1/2, EIF4A3, and DGCR8. All mt-circRNAs engaged with IGF2BP proteins crucial in redox signaling, and except mecciND1, they all potentially generate at least one protein resembling the immunoglobulin heavy chain protein. Given P53's function as a redox-sensitive transcription factor, and insulin's role as a crucial regulator of energy metabolism, their indirect interplay with mt-circRNAs could influence cellular outcomes. However, due to limited attention and infrequent data availability, it is advisable to conduct more thorough investigations to gain a deeper understanding of the functions of mt-circRNA.

Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network

Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding takes into account the physicochemical properties of circRNA sequences and employs four coding methods to extract sequence features. The hybrid deep structure comprises a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU). The CNN learns high-level abstract features, while the BiGRU captures long-term dependencies in the sequence. To assess the effectiveness of circ-FHN, we compared it to other computational methods on 16 datasets and conducted ablation experiments. Additionally, we conducted motif analysis. The results demonstrate that circ-FHN exhibits exceptional performance and surpasses other methods. circ-FHN is freely available at https://github.com/zhaoqi106/circ-FHN .

Research progress on prediction of RNA-protein binding sites in the past five years

Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018-2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.

iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites.

Gingipains may be one of the key virulence factors of Porphyromonas gingivalis to impair cognition and enhance blood-brain barrier permeability: An animal study

AIM

Blood-brain barrier (BBB) disorder is one of the early findings in cognitive impairments. We have recently found that Porphyromonas gingivalis bacteraemia can cause cognitive impairment and increased BBB permeability. This study aimed to find out the possible key virulence factors of P. gingivalis contributing to the pathological process.

MATERIALS AND METHODS

C57/BL6 mice were infected with P. gingivalis or gingipains or P. gingivalis lipopolysaccharide (P. gingivalis LPS group) by tail vein injection for 8 weeks. The cognitive behaviour changes in mice, the histopathological changes in the hippocampus and cerebral cortex, the alternations of BBB permeability, and the changes in Mfsd2a and Cav-1 levels were measured. The mechanisms of Ddx3x-induced regulation on Mfsd2a by arginine-specific gingipain A (RgpA) in BMECs were explored.

RESULTS

P. gingivalis and gingipains significantly promoted mice cognitive impairment, pathological changes in the hippocampus and cerebral cortex, increased BBB permeability, inhibited Mfsd2a expression and up-regulated Cav-1 expression. After RgpA stimulation, the permeability of the BBB model in vitro increased, and the Ddx3x/Mfsd2a/Cav-1 regulatory axis was activated.

CONCLUSIONS

Gingipains may be one of the key virulence factors of P. gingivalis to impair cognition and enhance BBB permeability by the Ddx3x/Mfsd2a/Cav-1 axis.

Extracellular vesicle-delivered hsa_circ_0090081 regulated by EIF4A3 enhances gastric cancer tumorigenesis

BACKGROUND

Circular RNA (circRNA) and extracellular vesicles (EVs) in tumors are crucial for the malignant phenotype of tumor cells. Nevertheless, the mechanisms and clinical effects of EV-delivered hsa_circ_0090081 in gastric cancer (GC) are unclear. This study aimed to reveal the effect of eukaryotic translation initiation factor 4A3 (EIF4A3)-mediated hsa_circ_0090081 expression and EV-delivered hsa_circ_0090081 on GC progression.

METHODS

qRT-PCR was conducted to clarify hsa_circ_0090081 and EIF4A3 levels in GC tissues. Transmission electronic microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blotting identified the EVs isolated from GC cells by ultracentrifugation. The roles of hsa_circ_0090081, EIF4A3, and EV-delivered hsa_circ_0090081 in GC cells were analyzed using Transwell, EdU, and CCK-8 assays. The regulatory role between EIF4A3 and hsa_circ_0090081 was investigated using RIP, qRT-PCR, and Pearson's analysis.

RESULTS

Our study showed that hsa_circ_0090081 and EIF4A3 were highly expressed in GC, and hsa_circ_0090081 was associated with poor prognosis. Data revealed that hsa_circ_0090081 inhibition restrained GC cell proliferation, invasion, and migration. Additionally, EIF4A3 could bind to the pre-mRNA of PHEX (linear form of hsa_circ_0090081) to enhance hsa_circ_0090081 expression in GC cells. Moreover, EIF4A3 overexpression nullified the malignant phenotypic suppression caused by hsa_circ_0090081 silencing in GC cells. Furthermore, EVs secreted by GC cells delivered hsa_circ_0090081 to facilitate the malignant progression of targeted GC cells.

CONCLUSION

This study showed that hsa_circ_0090081 was enhanced by EIF4A3 to play a promotive role in GC development. The results may help understand the mechanism of EIF4A3 and EV-delivered hsa_circ_0090081 and offer a valuable GC therapeutic target.

Computational approaches and challenges in the analysis of circRNA data

Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs

m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications, a critical gap remains in the prediction of circRNA m6A modifications. Therefore, accurate identification and prediction of m6A sites are imperative for understanding RNA function and regulation. This study presents a novel hybrid model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) for precise m6A methylation site prediction in circular RNAs (circRNAs) based on data from HEK293 cells. This model exploits the synergy between CNN's ability to extract intricate sequence features and BiLSTM's strength in capturing long-range dependencies. Furthermore, the integrated attention mechanism empowers the model to pinpoint critical biological information for studying circRNA m6A methylation. Our model, exhibiting over 78% prediction accuracy on independent datasets, offers not only a valuable tool for scientific research but also a strong foundation for future biomedical applications. This work not only furthers our understanding of gene expression regulation but also opens new avenues for the exploration of circRNA methylation in biological research.

CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy. CRIECNN employs advanced feature extraction methods and evaluates four distinct sequence datasets and encoding techniques (BERT, Doc2Vec, KNF, EIIP). The model consists of an ensemble convolutional neural network, a BiLSTM, and a self-attention mechanism for feature refinement. Our results demonstrate that CRIECNN outperforms state-of-the-art methods in accuracy and performance, effectively predicting circRNA-RBP interactions from both full-length sequences and fragments. This novel strategy makes an enormous advancement in the prediction of circRNA-RBP interactions, improving our understanding of circRNAs and their regulatory roles.

Decoding protein binding landscape on circular RNAs with base-resolution transformer models

Circular RNAs (circRNAs), a class of endogenous RNA with a covalent loop structure, can regulate gene expression by serving as sponges for microRNAs and RNA-binding proteins (RBPs). To date, most computational methods for predicting RBP binding sites on circRNAs focus on circRNA fragments instead of circRNAs. These methods detect whether a circRNA fragment contains binding sites, but cannot determine where are the binding sites and how many binding sites are on the circRNA transcript. We report a hybrid deep learning-based tool, CircSite, to predict RBP binding sites at single-nucleotide resolution and detect key contributed nucleotides on circRNA transcripts. CircSite takes advantage of convolutional neural networks (CNNs) and Transformer for learning local and global representations of circRNAs binding to RBPs, respectively. We construct 37 datasets of circRNAs interacting with proteins for benchmarking and the experimental results show that CircSite offers accurate predictions of RBP binding nucleotides and detects key subsequences aligning well with known binding motifs. CircSite is an easy-to-use online webserver for predicting RBP binding sites on circRNA transcripts and freely available at http://www.csbio.sjtu.edu.cn/bioinf/CircSite/.

CircSI-SSL: circRNA-binding site identification based on self-supervised learning

MOTIVATION

In recent years, circular RNAs (circRNAs), the particular form of RNA with a closed-loop structure, have attracted widespread attention due to their physiological significance (they can directly bind proteins), leading to the development of numerous protein site identification algorithms. Unfortunately, these studies are supervised and require the vast majority of labeled samples in training to produce superior performance. But the acquisition of sample labels requires a large number of biological experiments and is difficult to obtain.

RESULTS

To resolve this matter that a great deal of tags need to be trained in the circRNA-binding site prediction task, a self-supervised learning binding site identification algorithm named CircSI-SSL is proposed in this article. According to the survey, this is unprecedented in the research field. Specifically, CircSI-SSL initially combines multiple feature coding schemes and employs RNA_Transformer for cross-view sequence prediction (self-supervised task) to learn mutual information from the multi-view data, and then fine-tuning with only a few sample labels. Comprehensive experiments on six widely used circRNA datasets indicate that our CircSI-SSL algorithm achieves excellent performance in comparison to previous algorithms, even in the extreme case where the ratio of training data to test data is 1:9. In addition, the transplantation experiment of six linRNA datasets without network modification and hyperparameter adjustment shows that CircSI-SSL has good scalability. In summary, the prediction algorithm based on self-supervised learning proposed in this article is expected to replace previous supervised algorithms and has more extensive application value.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/cc646201081/CircSI-SSL.

Identification and characterization of differentially expressed circular RNAs in extraocular muscle of oculomotor nerve palsy

BACKGROUND

Oculomotor nerve palsy (ONP) is a neuroparalytic disorder resulting in dysfunction of innervating extraocular muscles (EOMs), of which the pathological characteristics remain underexplored.

METHODS

In this study, medial rectus muscle tissue samples from four ONP patients and four constant exotropia (CXT) patients were collected for RNA sequencing. Differentially expressed circular RNAs (circRNAs) were identified and included in functional enrichment analysis, followed by interaction analysis with microRNAs and mRNAs as well as RNA binding proteins. Furthermore, RT-qPCR was used to validate the expression level of the differentially expressed circRNAs.

RESULTS

A total of 84 differentially expressed circRNAs were identified from 10,504 predicted circRNAs. Functional enrichment analysis indicated that the differentially expressed circRNAs significantly correlated with skeletal muscle contraction. In addition, interaction analyses showed that up-regulated circRNA_03628 was significantly interacted with RNA binding protein AGO2 and EIF4A3 as well as microRNA hsa-miR-188-5p and hsa-miR-4529-5p. The up-regulation of circRNA_03628 was validated by RT-qPCR, followed by further elaboration of the expression, location and clinical significance of circRNA_03628 in EOMs of ONP.

CONCLUSIONS

Our study may shed light on the role of differentially expressed circRNAs, especially circRNA_03628, in the pathological changes of EOMs in ONP.

Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network

Introduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites. These methods predict whether the CircRNA sequence includes protein binding sites from the sequence level, and primarily concentrate on analysing the sequence specificity of CircRNA-protein binding. For model performance, these methods are unsatisfactory in accurately predicting motif sites that have special functions in gene expression. Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN). Results: CPBFCN provides a new path to predict CircRNA motifs. Based on the MEME tool, the existing CircRNA-related and protein-related database, we analysed the motif functions discovered by CPBFCN. We also investigated the correlation between CircRNA sponge and motif distribution. Furthermore, by comparing the motif distribution with different input sequence lengths, we found that some motifs in the flanking sequences of CircRNA-protein binding region may contribute to CircRNA-protein binding. Conclusion: This study contributes to identify circRNA-protein binding and provides help in understanding the role of circRNA-protein binding in gene expression regulation.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

ASCRB: Multi-view based attentional feature selection for CircRNA-binding site prediction

CircRNA is a non-coding RNA with a special circular structure, which plays a key role in a variety of life activities by interacting with RNA-binding proteins through CircRNA binding sites. Therefore, accurately identifying CircRNA binding sites is of great importance for gene regulation. In previous studies, most of the methods are based on single-view or multi-view features. Considering that single-view methods provide less effective information, the current mainstream methods mainly focus on extracting rich relevant features by constructing multiple views. However, the increasing number of views leads to a large amount of redundant information, which is detrimental to the detection of CircRNA binding sites. Therefore, to solve this problem, we propose to use the channel attention mechanism to further obtain useful multi-view features by filtering out invalid information in each view. First, we use five feature encoding schemes to construct multi-view. Then, we calibrate the features by generating the global representation of each view, filtering out redundant information to retain important feature information. Finally, features obtained from multiple views are fused to detect RNA binding sites. To validate the effectiveness of the method, we compared its performance on 37 CircRNA-RBP datasets with existing methods. Experimental results show that the average AUC performance of our method is 93.85%, which is better than the current state-of-the-art methods. We also provide the source code, which can be accessed at https://github.com/dxqllp/ASCRB for access.

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

BACKGROUND

Circular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the prevention and treatment of diseases. Existing models for circRNA-RBP identification usually adopt convolution neural network (CNN), recurrent neural network (RNN), or their variants as feature extractors. Most of them have drawbacks such as poor parallelism, insufficient stability, and inability to capture long-term dependencies.

METHODS

In this paper, we propose a new method completely using the self-attention mechanism to capture deep semantic features of RNA sequences. On this basis, we construct a CircSSNN model for the cirRNA-RBP identification. The proposed model constructs a feature scheme by fusing circRNA sequence representations with statistical distributions, static local contexts, and dynamic global contexts. With a stable and efficient network architecture, the distance between any two positions in a sequence is reduced to a constant, so CircSSNN can quickly capture the long-term dependencies and extract the deep semantic features.

RESULTS

Experiments on 37 circRNA datasets show that the proposed model has overall advantages in stability, parallelism, and prediction performance. Keeping the network structure and hyperparameters unchanged, we directly apply the CircSSNN to linRNA datasets. The favorable results show that CircSSNN can be transformed simply and efficiently without task-oriented tuning.

CONCLUSIONS

In conclusion, CircSSNN can serve as an appealing circRNA-RBP identification tool with good identification performance, excellent scalability, and wide application scope without the need for task-oriented fine-tuning of parameters, which is expected to reduce the professional threshold required for hyperparameter tuning in bioinformatics analysis.

CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion

Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful learning ability, the various deep learning frameworks have been used to predict the binding sites of RNA-binding protein (RPB) on circRNAs. These methods usually perform only single-level feature extraction of sequence information. However, the feature acquisition may be inadequate for single-level extraction. Generally, the features of deep and shallow layers of neural network can complement each other and are both important for binding site prediction tasks. Based on this concept, we propose a method that combines deep and shallow features, namely CRBP-HFEF. Specifically, features are first extracted and expanded for different levels of network. Then, the expanded deep and shallow features are fused and fed into the classification network, which finally determines whether they are binding sites. Compared to several existing methods, the experimental results on multiple datasets show that the proposed method achieves significant improvements in a number of metrics (with an average AUC of 0.9855). Moreover, much sufficient ablation experiments are also performed to verify the effectiveness of the hierarchical feature expansion strategy.

Computational approaches for circRNAs prediction and in silico characterization

Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported.

CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features

Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.

Characterization of circular RNAs with advanced sequencing technologies in human complex diseases

Circular RNAs (circRNAs) are one category of non-coding RNAs that do not possess 5' caps and 3' free ends. Instead, they are derived in closed circle forms from pre-mRNAs by a non-canonical splicing mechanism named "back-splicing." CircRNAs were discovered four decades ago, initially called "scrambled exons." Compared to linear RNAs, the expression levels of circRNAs are considerably lower, and it is challenging to identify circRNAs specifically. Thus, the biological relevance of circRNAs has been underappreciated until the advancement of next generation sequencing (NGS) technology. The biological insights of circRNAs, such as their tissue-specific expression patterns, biogenesis factors, and functional effects in complex diseases, namely human cancers, have been extensively explored in the last decade. With the invention of the third generation sequencing (TGS) with longer sequencing reads and newly designed strategies to characterize full-length circRNAs, the panorama of circRNAs in human complex diseases could be further unveiled. In this review, we first introduce the history of circular RNA detection. Next, we describe widely adopted NGS-based methods and the recently established TGS-based approaches capable of characterizing circRNAs in full-length. We then summarize data resources and representative circRNA functional studies related to human complex diseases. In the last section, we reviewed computational tools and discuss the potential advantages of utilizing advanced sequencing approaches to a functional interpretation of full-length circRNAs in complex diseases. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.

Artificial intelligence methods enhance the discovery of RNA interactions

Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.

A pseudo-Siamese framework for circRNA-RBP binding sites prediction integrating BiLSTM and soft attention mechanism

Circular RNAs (circRNAs) are widely expressed in tissues and play a key role in diseases through interacting with RNA binding proteins (RBPs). Since the high cost of traditional technology, computational methods are developed to identify the binding sites between circRNAs and RBPs. Unfortunately, these methods suffer from the insufficient learning of features and the single classification of output. To address these limitations, we propose a novel method named circ-pSBLA which constructs a pseudo-Siamese framework integrating Bi-directional long short-term memory (BiLSTM) network and soft attention mechanism for circRNA-RBP binding sites prediction. Softmax function and CatBoost are adopted to classify, respectively, and then a pseudo-Siamese framework is constructed. circ-pSBLA combines them to get final output. To validate the effectiveness of circ-pSBLA, we compare it with other state-of-the-art methods and carry out an ablation experiment on 17 sub-datasets. Moreover, we do motif analysis on 3 sub-datasets. The results show that circ-pSBLA achieves superior performance and outperforms other methods. All supporting source codes can be downloaded from https://github.com/gyj9811/circ-pSBLA.

A web server for identifying circRNA-RBP variable-length binding sites based on stacked generalization ensemble deep learning network

Circular RNA (circRNA) can exert biological functions by interacting with RNA-binding protein (RBP), and some deep learning-based methods have been developed to predict RBP binding sites on circRNA. However, most of these methods identify circRNA-RBP binding sites are only based on single data resource and cannot provide exact binding sites, only providing the probability value of a sequence fragment. To solve these problems, we propose a binding sites localization algorithm that fuses binding sites from multiple databases, and further design a stacked generalization ensemble deep learning model named CirRBP to identify RBP binding sites on circRNA. The CirRBP is trained by combining the binding sites from multiple databases and makes predictions by weighted aggregating the predictions of each sub-model. The results show that the CirRBP outperforms any sub-model and existing online prediction model. For better access to our research results, we develop an open-source web application called CRWS (CircRNA-RBP Web Server). Its back-end learning model of the CRWS is a stacked generalization ensemble learning model CirRBP based on different deep learning frameworks. Given a full-length circRNA or fragment sequence and a target RBP, the CRWS can analyze and provide the exact potential binding sites of the target RBP on the given sequence through the binding sites localization algorithm, and visualize it. In addition, the CRWS can discover the most widely distributed motif in each RBP dataset. Up to now, CRWS is the first significant online tool that uses multi-source data to train models and predict exact binding sites. CRWS is now publicly and freely available without login requirement at: http://www.bioinformatics.team.

circRIP: an accurate tool for identifying circRNA-RBP interactions

Circular ribonucleic acids (RNAs) (circRNAs) are formed by covalently linking the downstream splice donor and the upstream splice acceptor. One of the most important functions of circRNAs is mainly exerted through binding RNA-binding proteins (RBPs). However, there is no efficient algorithm for identifying genome-wide circRNA-RBP interactions. Here, we developed a unique algorithm, circRIP, for identifying circRNA-RBP interactions from RNA immunoprecipitation sequencing (RIP-Seq) data. A simulation test demonstrated the sensitivity and specificity of circRIP. By applying circRIP, we identified 95 IGF2BP3-binding circRNAs based on the IGF2BP3 RIP-Seq dataset. We further identified 2823 and 1333 circRNAs binding to >100 RBPs in K562 and HepG2 cell lines, respectively, based on enhanced cross-linking immunoprecipitation (eCLIP) data, demonstrating the significance to survey the potential interactions between circRNAs and RBPs. In this study, we provide an accurate and sensitive tool, circRIP (https://github.com/bioinfolabwhu/circRIP), to systematically identify RBP and circRNA interactions from RIP-Seq and eCLIP data, which can significantly benefit the research community for the functional exploration of circRNAs.

DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning

RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence. Facing this challenge, we present a novel method called DeepBtoD, which predicts RBPs directly from RNA sequences. First, a [Formula: see text]-BtoD encoding is designed, which takes into account the composition of [Formula: see text]-nucleotides and their relative positions and forms a local module. Second, we designed a multi-scale convolutional module embedded with a self-attentive mechanism, the ms-focusCNN, which is used to further learn more effective, diverse, and discriminative high-level features. Finally, global information is considered to supplement local modules with ensemble learning to predict whether the target RNA binds to RBPs. Our preliminary 24 independent test datasets show that our proposed method can classify RBPs with the area under the curve of 0.933. Remarkably, DeepBtoD shows competitive results across seven state-of-the-art methods, suggesting that RBPs can be highly recognized by integrating local [Formula: see text]-BtoD and global information only from RNA sequences. Hence, our integrative method may be useful to improve the power of RBPs prediction, which might be particularly useful for modeling protein-nucleic acid interactions in systems biology studies. Our DeepBtoD server can be accessed at http://175.27.228.227/DeepBtoD/.

ENNGene: an Easy Neural Network model building tool for Genomics

Background

The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field.

Results

Here we present ENNGene—Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein.

Conclusions

As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08414-x.

SAAED: Embedding and Deep Learning Enhance Accurate Prediction of Association Between circRNA and Disease

Emerging evidence indicates that circRNA can regulate various diseases. However, the mechanisms of circRNA in these diseases have not been fully understood. Therefore, detecting potential circRNA–disease associations has far-reaching significance for pathological development and treatment of these diseases. In recent years, deep learning models are used in association analysis of circRNA–disease, but a lack of circRNA–disease association data limits further improvement. Therefore, there is an urgent need to mine more semantic information from data. In this paper, we propose a novel method called Semantic Association Analysis by Embedding and Deep learning (SAAED), which consists of two parts, a neural network embedding model called Entity Relation Network (ERN) and a Pseudo-Siamese network (PSN) for analysis. ERN can fuse multiple sources of data and express the information with low-dimensional embedding vectors. PSN can extract the feature between circRNA and disease for the association analysis. CircRNA–disease, circRNA–miRNA, disease–gene, disease–miRNA, disease–lncRNA, and disease–drug association information are used in this paper. More association data can be introduced for analysis without restriction. Based on the CircR2Disease benchmark dataset for evaluation, a fivefold cross-validation experiment showed an AUC of 98.92%, an accuracy of 95.39%, and a sensitivity of 93.06%. Compared with other state-of-the-art models, SAAED achieves the best overall performance. SAAED can expand the expression of the biological related information and is an efficient method for predicting potential circRNA–disease association.

HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network

Identifying genome-wide binding events between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) can greatly facilitate our understanding of functional mechanisms within circRNAs. Thanks to the development of cross-linked immunoprecipitation sequencing technology, large amounts of genome-wide circRNA binding event data have accumulated, providing opportunities for designing high-performance computational models to discriminate RBP interaction sites and thus to interpret the biological significance of circRNAs. Unfortunately, there are still no computational models sufficiently flexible to accommodate circRNAs from different data scales and with various degrees of feature representation. Here, we present HCRNet, a novel end-to-end framework for identification of circRNA-RBP binding events. To capture the hierarchical relationships, the multi-source biological information is fused to represent circRNAs, including various natural language sequence features. Furthermore, a deep temporal convolutional network incorporating global expectation pooling was developed to exploit the latent nucleotide dependencies in an exhaustive manner. We benchmarked HCRNet on 37 circRNA datasets and 31 linear RNA datasets to demonstrate the effectiveness of our proposed method. To evaluate further the model's robustness, we performed HCRNet on a full-length dataset containing 740 circRNAs. Results indicate that HCRNet generally outperforms existing methods. In addition, motif analyses were conducted to exhibit the interpretability of HCRNet on circRNAs. All supporting source code and data can be downloaded from https://github.com/yangyn533/HCRNet and https://doi.org/10.6084/m9.figshare.16943722.v1. And the web server of HCRNet is publicly accessible at http://39.104.118.143:5001/.

EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network

MOTIVATION

RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding event detection methods have been developed to predict RBPs. Unfortunately, the existing computational methods usually suffer some limitations, such as high-dimensionality, data sparsity and low model performance.

RESULTS

Deep convolution neural network has a useful advantage for solving high-dimensional and sparse data. To improve further the performance of deep convolution neural network, we propose evolutionary deep convolutional neural network (EDCNN) to identify protein-RNA interactions by synergizing evolutionary optimization with gradient descent to enhance deep conventional neural network. In particular, EDCNN combines evolutionary algorithms and different gradient descent models in a complementary algorithm, where the gradient descent and evolution steps can alternately optimize the RNA-binding event search. To validate the performance of EDCNN, an experiment is conducted on two large-scale CLIP-seq datasets, and results reveal that EDCNN provides superior performance to other state-of-the-art methods. Furthermore, time complexity analysis, parameter analysis and motif analysis are conducted to demonstrate the effectiveness of our proposed algorithm from several perspectives.

AVAILABILITY AND IMPLEMENTATION

The EDCNN algorithm is available at GitHub: https://github.com/yaweiwang1232/EDCNN. Both the software and the supporting data can be downloaded from: https://figshare.com/articles/software/EDCNN/16803217.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level

The interaction between proteins and RNA is closely related to various human diseases. Computer-aided drug design can be facilitated by detecting the RNA sites that bind proteins. However, due to the aggregation of binding sites in RNA sequences, high sample similarity occurs when extracting RNA fragments by using a sliding window. Considering these problems, we present a method, DFpin, to predict protein-interacting nucleotides in RNA. To retain more key nucleotide sites, we used the redundancy method based on feature similarity, that is, feature redundancy is removed based on the RNA mono-nucleotide composition to maintain the diversity of RNA samples and avoid the residue of redundant data. In addition, to extract key abstract features and avoid over-fitting, we used the cascade structure of a deep forest model to predict protein-interacting nucleotides. Overall, DFpin demonstrated excellent classification with 85.4% accuracy and 93.3% area under the curve. Compared with other methods, the accuracy of DFpin was better, suggesting that feature-based redundancy removal and deep forest can help predict nucleotides of protein interactions. The source code and all dataset are available at: https://github.com/zhaoxj-tech/DFpin.git.

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.

More and more evidences show that circular RNA can directly bind to proteins and participate in countless different biological processes. The calculation method can quickly and accurately predict the binding site of circular RNA and RBP. In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL. It can effectively learn high-level feature representations. The performance of the model was verified through comparative experiments of different feature extraction algorithms, different deep learning models and classifier models. Moreover, the CRBPDL model was applied to 31 linear RNAs, and the effectiveness of our method was proved by comparison with the results of current excellent algorithms. It is expected that the CRBPDL model can effectively predict the binding site of circular RNA-RBP and provide reliable candidates for further biological experiments.

Prediction of RBP binding sites on circRNAs using an LSTM-based deep sequence learning architecture

Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.

Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning

RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational models have been proposed for understanding the binding preferences of RBPs. These methods required integrating multiple features with raw RNA sequences such as secondary structure and their performances can be further improved. In this paper, we propose an efficient and simple convolution neural network, RBPCNN, that relies on the combination of the raw RNA sequence and evolutionary information. We show that conservation scores (evolutionary information) for the RNA sequences can significantly improve the overall performance of the proposed predictor. In addition, the automatic extraction of the binding sequence motifs can enhance our understanding of the binding specificities of RBPs. The experimental results show that RBPCNN outperforms significantly the current state-of-the-art methods. More specifically, the average area under the receiver operator curve was improved by 2.67 percent and the mean average precision was improved by 8.03 percent. The datasets and results can be downloaded from https://home.jbnu.ac.kr/NSCL/RBPCNN.htm.

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditional machine learning and deep learning, have been developed to predict the interactions between RNAs and RBPs, and most of them are focused on linear RNAs. At present, few studies have been done on the binding relationships between circRNAs and RBPs. Thus, in-depth research is urgently needed. In the existing circRNA-RBP binding site prediction methods, circRNA sequences are the main research subjects, but the relevant characteristics of circRNAs have not been fully exploited, such as the structure and composition information of circRNA sequences. Some methods have extracted different views to construct recognition models, but how to efficiently use the multi-view data to construct recognition models is still not well studied. Considering the above problems, this paper proposes a multi-view classification method called DMSK based on multi-view deep learning, subspace learning and multi-view classifier for the identification of circRNA-RBP interaction sites. In the DMSK method, first, we converted circRNA sequences into pseudo-amino acid sequences and pseudo-dipeptide components for extracting high-dimensional sequence features and component features of circRNAs, respectively. Then, the structure prediction method RNAfold was used to predict the secondary structure of the RNA sequences, and the sequence embedding model was used to extract the context-dependent features. Next, we fed the above four views' raw features to a hybrid network, which is composed of a convolutional neural network and a long short-term memory network, to obtain the deep features of circRNAs. Furthermore, we used view-weighted generalized canonical correlation analysis to extract four views' common features by subspace learning. Finally, the learned subspace common features and multi-view deep features were fed to train the downstream multi-view TSK fuzzy system to construct a fuzzy rule and fuzzy inference-based multi-view classifier. The trained classifier was used to predict the specific positions of the RBP binding sites on the circRNAs. The experiments show that the prediction performance of the proposed method DMSK has been improved compared with the existing methods. The code and dataset of this study are available at https://github.com/Rebecca3150/DMSK.

Emerging Role of Circular RNA-Protein Interactions

Circular RNAs (circRNAs) are emerging as novel regulators of gene expression in various biological processes. CircRNAs regulate gene expression by interacting with cellular regulators such as microRNAs and RNA binding proteins (RBPs) to regulate downstream gene expression. The accumulation of high-throughput RNA-protein interaction data revealed the interaction of RBPs with the coding and noncoding RNAs, including recently discovered circRNAs. RBPs are a large family of proteins known to play a critical role in gene expression by modulating RNA splicing, nuclear export, mRNA stability, localization, and translation. However, the interaction of RBPs with circRNAs and their implications on circRNA biogenesis and function has been emerging in the last few years. Recent studies suggest that circRNA interaction with target proteins modulates the interaction of the protein with downstream target mRNAs or proteins. This review outlines the emerging mechanisms of circRNA-protein interactions and their functional role in cell physiology.

Recognizing binding sites of poorly characterized RNA-binding proteins on circular RNAs using attention Siamese network

Circular RNAs (circRNAs) interact with RNA-binding proteins (RBPs) to play crucial roles in gene regulation and disease development. Computational approaches have attracted much attention to quickly predict highly potential RBP binding sites on circRNAs using the sequence or structure statistical binding knowledge. Deep learning is one of the popular learning models in this area but usually requires a lot of labeled training data. It would perform unsatisfactorily for the less characterized RBPs with a limited number of known target circRNAs. How to improve the prediction performance for such small-size labeled characterized RBPs is a challenging task for deep learning-based models. In this study, we propose an RBP-specific method iDeepC for predicting RBP binding sites on circRNAs from sequences. It adopts a Siamese neural network consisting of a lightweight attention module and a metric module. We have found that Siamese neural network effectively enhances the network capability of capturing mutual information between circRNAs with pairwise metric learning. To further deal with the small-sample size problem, we have performed the pretraining using available labeled data from other RBPs and also demonstrate the efficacy of this transfer-learning pipeline. We comprehensively evaluated iDeepC on the benchmark datasets of RBP-binding circRNAs, and the results suggest iDeepC achieving promising results on the poorly characterized RBPs. The source code is available at https://github.com/hehew321/iDeepC.

Deep sequence modelling for predicting COVID-19 mRNA vaccine degradation

The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 and requires urgent global effort to accelerate the development of a vaccine to stop the daily infections and deaths. Several types of vaccine have been designed to teach the immune system how to fight off certain kinds of pathogens. mRNA vaccines are the most important candidate vaccines because of their capacity for rapid development, high potency, safe administration and potential for low-cost manufacture. mRNA vaccine acts by training the body to recognize and response to the proteins produced by disease-causing organisms such as viruses or bacteria. This type of vaccine is the fastest candidate to treat COVID-19 but it currently facing several limitations. In particular, it is a challenge to design stable mRNA molecules because of the inefficient in vivo delivery of mRNA, its tendency for spontaneous degradation and low protein expression levels. This work designed and implemented a sequence deep model based on bidirectional GRU and LSTM models applied on the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences responsible for degradation by predicting five reactivity values for every position in the sequence. Four of these values determine the likelihood of degradation with/without magnesium at high pH (pH 10) and high temperature (50 degrees Celsius) and the fifth reactivity value is used to determine the likely secondary structure of the RNA sample. The model relies on two types of features, namely numerical and categorical features, where the categorical features are extracted from the mRNA sequences, structure and predicted loop. These features are represented and encoded by numbers, and then, the features are extracted using embedding layer learning. There are five numerical features depending on the likelihood for each pair of nucleotides in the RNA. The model gives promising results because it predicts the five reactivity values with a validation mean columnwise root mean square error (MCRMSE) of 0.125 using LSTM model with augmentation and the codon encoding method. Codon encoding outperforms Base encoding in MCRMSE validation error using the LSTM model meanwhile Base encoding outperforms codon encoding due to less over-fitting and the difference between the training and validation loss error is 0.008.

Identification of Enzalutamide Resistance-Related circRNA-miRNA-mRNA Regulatory Networks in Patients with Prostate Cancer

Purpose

This study aimed to identify enzalutamide resistant-related (EnzR-related) circRNAs and to characterize and validate circRNA-miRNA-mRNA ceRNA regulatory network and corresponding prognostic signature of prostate cancer patients.

Methods

We obtained circRNA expression microarray from the Gene Expression Omnibus (GEO) database and performed differential expression analysis to identify EnzR-related circRNAs using the limma package. The miRNA and mRNA expression profiling were downloaded and performed differential expression analysis, then overlapped with predicted candidates. Next, we established circRNA-miRNA-mRNA ceRNA network and PPI network utilized Cytoscape software and STRING database, respectively. In addition, univariate and Lasso Cox regression analyses were applied to generate a prognostic signature. Receiver operating characteristic (ROC) curves and Kaplan–Meier analysis were used to evaluate the reliability and sensitivity of the signature. Ultimately, we chose hsa_circ_0047641 to validate the feasibility of the EnzR-related ceRNA regulatory pathway using qRT-PCR, CCK8 and Transwell assays.

Results

We identified 13 EnzR-related circRNAs and constructed a ceRNA regulatory network that contained two downregulated circRNAs (has-circ-00000919 and has-circ-0000036) and two upregulated circRNAs (has-circ-0047641 and has-circ-0068697), and their sponged 6 miRNAs and 167 targeted mRNAs. Subsequently, these targeted mRNAs were performed to implement PPI analysis and to identify 10 Hub genes. Functional enrichment analysis provided new ways to seek potential biological functions. Besides, we established a prognostic signature of PCa patients based on 8 prognostic-associated mRNAs. We confirmed that the survival rates of PCa patients with high-risk subgroup were slightly lower than those with low-risk subgroup in the TCGA dataset (p<0.001), and ROC curves revealed that the AUC value for prognostic signature was 0.816. Finally, the functional analysis suggested that knockdown of hsa_circ_0047641 could inhibit the progression of PCa and could reverse Enz-resistance in vitro.

Conclusion

We identified 13 EnzR-related circRNAs, and constructed and confirmed that EnzR-related circRNA-miRNA-mRNA ceRNA network and corresponding prognostic signature could be a useful prognostic biomarker and therapeutic target.

Methods to study circRNA-protein interactions

Circular RNAs (circRNAs) have been studied extensively in the last few years, uncovering functional roles in a diverse range of cell types and organisms. As shown for a few cases, these functions may be mediated by trans-acting factors, in particular RNA-binding proteins (RBPs). However, the specific interaction partners for most circRNAs remain unknown. This is mainly due to technical difficulties in their identification and in differentiating between interactors of circRNAs and their linear counterparts. Here we review the currently used methodology to systematically study circRNA-protein complexes (circRNPs), focusing either on a specific RNA or protein, both on the gene-specific or global level, and discuss advantages and challenges of the available approaches.

CircRNA-Protein Interactions in Muscle Development and Diseases

Circular RNA (circRNA) is a kind of novel endogenous noncoding RNA formed through back-splicing of mRNA precursor. The biogenesis, degradation, nucleus-cytoplasm transport, location, and even translation of circRNA are controlled by RNA-binding proteins (RBPs). Therefore, circRNAs and the chaperoned RBPs play critical roles in biological functions that significantly contribute to normal animal development and disease. In this review, we systematically characterize the possible molecular mechanism of circRNA-protein interactions, summarize the latest research on circRNA-protein interactions in muscle development and myocardial disease, and discuss the future application of circRNA in treating muscle diseases. Finally, we provide several valid prediction methods and experimental verification approaches. Our review reveals the significance of circRNAs and their protein chaperones and provides a reference for further study in this field.

The GAUGAA Motif Is Responsible for the Binding between circSMARCA5 and SRSF1 and Related Downstream Effects on Glioblastoma Multiforme Cell Migration and Angiogenic Potential

Circular RNAs (circRNAs) are a large class of RNAs with regulatory functions within cells. We recently showed that circSMARCA5 is a tumor suppressor in glioblastoma multiforme (GBM) and acts as a decoy for Serine and Arginine Rich Splicing Factor 1 (SRSF1) through six predicted binding sites (BSs). Here we characterized RNA motifs functionally involved in the interaction between circSMARCA5 and SRSF1. Three different circSMARCA5 molecules (Mut1, Mut2, Mut3), each mutated in two predicted SRSF1 BSs at once, were obtained through PCR-based replacement of wild-type (WT) BS sequences and cloned in three independent pcDNA3 vectors. Mut1 significantly decreased its capability to interact with SRSF1 as compared to WT, based on the RNA immunoprecipitation assay. In silico analysis through the “Find Individual Motif Occurrences” (FIMO) algorithm showed GAUGAA as an experimentally validated SRSF1 binding motif significantly overrepresented within both predicted SRSF1 BSs mutated in Mut1 (q-value = 0.0011). U87MG and CAS-1, transfected with Mut1, significantly increased their migration with respect to controls transfected with WT, as revealed by the cell exclusion zone assay. Immortalized human brain microvascular endothelial cells (IM-HBMEC) exposed to conditioned medium (CM) harvested from U87MG and CAS-1 transfected with Mut1 significantly sprouted more than those treated with CM harvested from U87MG and CAS-1 transfected with WT, as shown by the tube formation assay. qRT-PCR showed that the intracellular pro- to anti-angiogenic Vascular Endothelial Growth Factor A (VEGFA) mRNA isoform ratio and the amount of total VEGFA mRNA secreted in CM significantly increased in Mut1-transfected CAS-1 as compared to controls transfected with WT. Our data suggest that GAUGAA is the RNA motif responsible for the interaction between circSMARCA5 and SRSF1 as well as for the circSMARCA5-mediated control of GBM cell migration and angiogenic potential.

DeCban: Prediction of circRNA-RBP Interaction Sites by Using Double Embeddings and Cross-Branch Attention Networks

Circular RNAs (circRNAs), as a rising star in the RNA world, play important roles in various biological processes. Understanding the interactions between circRNAs and RNA binding proteins (RBPs) can help reveal the functions of circRNAs. For the past decade, the emergence of high-throughput experimental data, like CLIP-Seq, has made the computational identification of RNA-protein interactions (RPIs) possible based on machine learning methods. However, as the underlying mechanisms of RPIs have not been fully understood yet and the information sources of circRNAs are limited, the computational tools for predicting circRNA-RBP interactions have been very few. In this study, we propose a deep learning method to identify circRNA-RBP interactions, called DeCban, which is featured by hybrid double embeddings for representing RNA sequences and a cross-branch attention neural network for classification. To capture more information from RNA sequences, the double embeddings include pre-trained embedding vectors for both RNA segments and their converted amino acids. Meanwhile, the cross-branch attention network aims to address the learning of very long sequences by integrating features of different scales and focusing on important information. The experimental results on 37 benchmark datasets show that both double embeddings and the cross-branch attention model contribute to the improvement of performance. DeCban outperforms the mainstream deep learning-based methods on not only prediction accuracy but also computational efficiency. The data sets and source code of this study are freely available at: https://github.com/AaronYll/DECban.

Identifying the sequence specificities of circRNA-binding proteins based on a capsule network architecture

Background

Circular RNAs (circRNAs) are widely expressed in cells and tissues and are involved in biological processes and human diseases. Recent studies have demonstrated that circRNAs can interact with RNA-binding proteins (RBPs), which is considered an important aspect for investigating the function of circRNAs.

Results

In this study, we design a slight variant of the capsule network, called circRB, to identify the sequence specificities of circRNAs binding to RBPs. In this model, the sequence features of circRNAs are extracted by convolution operations, and then, two dynamic routing algorithms in a capsule network are employed to discriminate between different binding sites by analysing the convolution features of binding sites. The experimental results show that the circRB method outperforms the existing computational methods. Afterwards, the trained models are applied to detect the sequence motifs on the seven circRNA-RBP bound sequence datasets and matched to known human RNA motifs. Some motifs on circular RNAs overlap with those on linear RNAs. Finally, we also predict binding sites on the reported full-length sequences of circRNAs interacting with RBPs, attempting to assist current studies. We hope that our model will contribute to better understanding the mechanisms of the interactions between RBPs and circRNAs.

Conclusion

In view of the poor studies about the sequence specificities of circRNA-binding proteins, we designed a classification framework called circRB based on the capsule network. The results show that the circRB method is an effective method, and it achieves higher prediction accuracy than other methods.

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

BACKGROUND

RNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive.

RESULTS

Here we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence.

CONCLUSIONS

RBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/ .

iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network

Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those computational methods often suffer from the low discriminative power of feature representations, numerical instability and poor scalability. To address those limitations, we propose a novel computational method called iCircRBP-DHN using deep hierarchical network for discriminating circRNA-RBP binding sites. The network architecture can be regarded as a deep multi-scale residual network followed by bidirectional gated recurrent units (BiGRUs) with the self-attention mechanism, which can simultaneously extract local and global contextual information. Meanwhile, we propose novel encoding schemes by integrating CircRNA2Vec and the K-tuple nucleotide frequency pattern to represent different degrees of nucleotide dependencies. To validate the effectiveness of our proposed iCircRBP-DHN, we compared its performance with other computational methods on 37 circRNAs datasets and 31 linear RNAs datasets, respectively. The experimental results reveal that iCircRBP-DHN can achieve superior performance over those state-of-the-art algorithms. Moreover, we perform motif analysis on circRNAs bound by those different RBPs, demonstrating that our proposed CircRNA2Vec encoding scheme can be promising. The iCircRBP-DHN method is made available at https://github.com/houzl3416/iCircRBP-DHN.