miRNA Sequence Information Research Articles

DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks.

MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are involved in regulating various physiological processes by regulating the gene expression. The subcellular localization of miRNAs plays a crucial role in the discovery of their biological functions. Although several computational methods based on miRNA functional similarity networks have been presented to identify the subcellular localization of miRNAs, it remains difficult for these approaches to effectively extract well-referenced miRNA functional representations due to insufficient miRNA-disease association representation and disease semantic representation. Currently, there has been a significant amount of research on miRNA-disease associations, making it possible to address the issue of insufficient miRNA functional representation. In this work, a novel model is established, named DAmiRLocGNet, based on graph convolutional network (GCN) and autoencoder (AE) for identifying the subcellular localizations of miRNA. The DAmiRLocGNet constructs the features based on miRNA sequence information, miRNA-disease association information and disease semantic information. GCN is utilized to gather the information of neighboring nodes and capture the implicit information of network structures from miRNA-disease association information and disease semantic information. AE is employed to capture sequence semantics from sequence similarity networks. The evaluation demonstrates that the performance of DAmiRLocGNet is superior to other competing computational approaches, benefiting from implicit features captured by using GCNs. The DAmiRLocGNet has the potential to be applied to the identification of subcellular localization of other non-coding RNAs. Moreover, it can facilitate further investigation into the functional mechanisms underlying miRNA localization. The source code and datasets are accessed at http://bliulab.net/DAmiRLocGNet.

Adaptive deep propagation graph neural network for predicting miRNA-disease associations.

A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations. In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs. Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.

Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects.

MicroRNAs (miRNAs) are 19-24 nucleotide (nt)-long noncoding, single-stranded RNA molecules that play significant roles in regulating the gene expression, growth, and development of plants and animals. From the year that miRNAs were first discovered until the beginning of the twenty-first century, researchers used experimental methods such as cloning and sequencing to identify new miRNAs and their roles in the posttranscriptional regulation of protein synthesis. Later, in the early 2000s, informatics approaches to the discovery of new miRNAs began to be implemented. With increasing knowledge about miRNA, more efficient algorithms have been developed for computational miRNA prediction. The miRNA research community, hoping for greater coverage and faster results, has shifted from cumbersome and expensive traditional experimental approaches to computational approaches. These computational methods started with homology-based comparisons of known miRNAs with orthologs in the genomes of other species; this method could identify a known miRNA in new species. Second-generation sequencing and next-generation sequencing of mRNA at different developmental stages and in specific tissues, in combination with a better search and alignment algorithm, have accelerated the process of predicting novel miRNAs in a particular species. Using the accumulated annotated miRNA sequence information, researchers have been able to design ab initio algorithms for miRNA prediction independent of genome sequence knowledge. Here, the methods recently used for miRNA computational prediction are summarized and classified into the following four categories: homology-based, target-based, scoring-based, and machine-learning-based approaches. Finally, the future developmental directions of miRNA prediction methods are discussed.

Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge.

The emergence of a large amount of pharmacological, genomic, and network knowledge data provides new challenges and opportunities for drug discovery and development. Identification of real small-molecule drug (SM)-miRNA associations is not only important in the development of effective drug repositioning but also crucial in providing a better understanding of the mechanisms by which small-molecule drugs achieve the purpose of treating diseases by regulating miRNA expression. However, challenges remain in accurately determining potential associations between small molecules and miRNAs using information from multiomics data. In this study, we adopted a novel framework called SMAJL to improve the prediction of small molecule-miRNA associations with joint learning. First, we use enhancing matrix completions to obtain the network knowledge of small molecule-miRNA associations. Then, we extract the information of small-molecule fingerprints and miRNA sequences into feature vectors to obtain small-molecule structure and miRNA sequence information. Finally, we incorporate small-molecule structure information, miRNA sequence data, and heterogeneous network knowledge into a joint learning model based on a Restricted Boltzmann Machine (RBM) to predict association scores. To validate the effectiveness of our method, the SMAJL model is compared with four state-of-the-art methods in terms of 5-fold cross-validation. The results demonstrate that the AUC and AUPRC of the SMAJL are obviously superior to those of other comparison methods. The SMAJL model also achieved great results in terms of robustness and case studies, further demonstrating its strong predictive power.

Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model

In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.

Evidence of the Regulatory Roles of Candidate miRNAs During Somatic Embryogenesis in Lilium davidii var. unicolor

microRNAs (miRNAs) play an essential role in the regulation of large gene networks involved in plant somatic embryogenesis (SEG), but the regulatory mechanism of SEG mediated by miRNAs remains unclear. We used small RNA (sRNA) and transcriptome sequencing methods to identify and profile conserved miRNAs and target genes in five classic embryogenic cultures of Lilium davidii var. unicolor. A total of 35,450 unigenes and 1,727,322 sRNA sequences were obtained from the high-throughput sequencing data. After sequence alignment, 314 known miRNAs belonging to more than 47 miRNA families, 44 novel miRNAs and 32 miRNA*s were identified in L. davidii var. unicolor. miR159 was the largest miRNA family and exhibited the highest expression, and 711 target genes were predicted as the corresponding targets for 273 known miRNAs. In addition, we identified miRNAs that are differentially expressed during SEG and explored the expression profiles of 12 miRNAs and 8 targets. The corresponding relationships between the lda-miR167-WRKY/BGAL, lda-miR319-PCF, lda-miR395-FtsZ and lda-miR408/lda-miR397-LAC pairs were ultimately validated via 5′ rapid amplification of cDNA ends (5′ RACE). Furthermore, we summarized candidate miRNA sequence information, monitored the dynamic expression profiles of miRNAs and tested the interactions between miRNAs and their targets. The data revealed the complex regulatory roles of miRNAs during SEG in L. davidii var. unicolor, and these findings contribute to the miRNA functional annotation of Liliaceae and help elucidate the miRNA-mediated regulatory network during plant SEG.

MicroRNAs Importance: Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random Forest Regression and Sequence Data (Adv. Theory Simul. 9/2019)

In article number 1900083, Chunmei Cui, Qinghua Cui, and co-workers design a ‘computational model’, MIC, to quantify miRNA essentiality and identify the important ones from a huge number of miRNAs in human genome using miRNA sequence information. For the cover figure, the MIC model is like a superman (or archmage/magician) who can precisely select the miRNAs he/she needs from a huge number of miRNAs in the ball.

MLMDA: a machine learning approach to predict and validate MicroRNA\u2013disease associations by integrating of heterogenous information sources

BackgroundEmerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease.MethodsIn this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA–disease associations.ResultsThe experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA–disease association databases.ConclusionsThese prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/.

LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.

Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.

CGMDA: An Approach to Predict and Validate MicroRNA-Disease Associations by Utilizing Chaos Game Representation and LightGBM

Recent studies have shown that microRNAs (miRNAs) play an important role in complex human diseases. Identifying potential miRNA-disease associations is useful for understanding the pathogenesis. However, there are currently only a few methods proposed to predict miRNA-disease association based on sequence information. And these methods can only quantify nonlinear sequence relationships without taking linear sequence information into account. In this work, we designed a computational method for predicting miRNA-disease association based on chaos game representation, called CGMDA, to overcome these problems. CGMDA combines association information with miRNA sequence information, miRNA functional information and disease semantic information to improve prediction accuracy. In particular, we use chaos game representation (CGR) technology for the first time to transform miRNA sequence information into image information and extract its features. In the cross-validation experiment, CGMDA achieved a mean the area under the receiver operating characteristic curve (AUC) of 0.9099 on the HMDD v3.0 data set. To better evaluate the performance of CGMDA, we compared it to different classifiers and related prediction methods. In addition, CGMDA is applied to three human complex diseases. The results showed that of the top 40 disease-related miRNAs predicted, 39 (Breast Neoplasm), 39 (Lymphoma) and 38 (Colon Neoplasm) were validated by experiments in case studies. These experimental results show that CGMDA is a reliable tool and has potential application prospects in assisting early diagnosis and treatment of prognosis.

Role of miRNA in carcinogenesis and biomarker selection: a methodological view

miRNAs, their involvement in cancer development and their potential to be robust biomarkers of diagnosis, staging, prognosis and response to therapy are reviewed. In small RNA animal biogenesis, miRNA genes in the nucleus are transcribed to generate long primary transcripts (pri-miRNAs), which are first cropped by RNase-III-type enzyme Drosha to release hairpin intermediates (pre-miRNAs) in the nucleus. Pre-miRNA is then exported to the cytoplasm by exportin-5. Following arrival in the cytoplasm, pre-miRNAs are subjected to the second processing step (dicing) to release the mature miRNA duplex, which is then separated: one strand becomes the mature miRNAand the other is degraded. These tiny miRNAs induce messenger degradation, translational repression or both. However, there is no evidence to demonstrate that these two mechanisms exist in the regulation of the same gene. Since a miRNA can target numerous mRNAs, often in combination with other miRNAs, these miRNAs operate a highly complex regulatory network. The specific function in most mammalian miRNAs is unknown. However, data suggest that miRNA genes, approximately 1% of all human genes, regulate protein production for 20–30% or more of all genes. miRNA expression profiles are effective for classifying solid and hematologic human cancers, and have shown great promise for early cancer detection. This is of great importance for effective treatment before the cells metastasize; therefore, tumors can be surgically resected. Computer-based prediction approaches of miRNAs and their targets, and biological validation techniques for ascertaining these predictions, currently play a central role in the discovery of miRNAs and in elucidating their function. Guidelines have been established for the identification and annotation of new miRNAs to distinguish them from other RNAs, especially siRNAs. These guidelines take into account factors such as transcript structure, conservation and processing, and a centralized, searchable database of all possible miRNA sequence information and annotation for humans and of more than 38 other species. Two approaches are used to characterize miRNAs: studying expression of known miRNAs by hybridization-based techniques (e.g., northern blots, RNase protection, primer extension, real-time, quantitative PCR and microarrays) or discovery of novel miRNAs molecules by cloning and sequencing. Owing to their adaptability and high throughput, microarrays may prove to be the preferred platform for whole-genome miRNA expression analysis.