Identification of microRNA regulatory modules in Arabidopsis via a probabilistic graphical model

A probabilistic modeling framework for genomic networks incorporating sample heterogeneity

Probabilistic graphical models are powerful tools to quantify, visualize, and interpret network dependencies in complex biological systems such as high-throughput -omics. However, many graphical models assume sample homogeneity, limiting their effectiveness. We propose a flexible Bayesian approach called graphical regression (GraphR), which (1) incorporates sample heterogeneity at different scales through a regression-based formulation, (2) enables sparse sample-specific network estimation, (3) identifies and quantifies potential effects of heterogeneity on network structures, and (4) achieves computational efficiency via variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing state-of-the-art methods in terms of network structure recovery and computational cost across multiple settings. We use GraphR to analyze three multi-omic and spatial transcriptomic datasets to investigate inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules

The identification of miRNA regulatory modules can help decipher miRNAs combinatorial regulation effects on the pathogenesis underlying complex diseases, especially in cancer. By integrating miRNA/mRNA expression profiles and sequence-based predicted target site information, we develop a novel cluster-based computational method named CoModule for identifying miRNA regulatory modules (MRMs). The ultimate goal of CoModule is to detect the MRMs, in which the miRNAs in each module are expected to present cooperative mechanisms in regulating their targets mRNAs. Here, the co-expression of miRNAs are believed to present cooperative regulatory relationship, therefore, the critical step of CoModule is first to partition the miRNAs with similar expression into a cluster by employing rough set clustering. After gaining credible miRNA clusters, the targets of regulator are naturally added into corresponding clusters to produce the final miRNA regulatory modules. We apply this present method to ovarian cancer datasets and make a comparison with the other two existing prominent approaches. The results indicate that the modules identified by CoModule perform better than the other two methods ranging from the topological aspects to the biological function. Survival analysis detects a number of prognostic modules with statistical significance, which can help reveal the potential diagnostic for ovarian cancer.

hsa-miR-301a- and SOX10-dependent miRNA-TF-mRNA regulatory circuits in breast cancer

Breast cancer is the most common cancer among women and the molecular pathways that play main roles in breast cancer regulation are still not completely understood. MicroRNAs (miRNAs) and transcription factors (TFs) are important regulators of gene expression. It is important to unravel the relation of TFs, miRNAs, and their targets within regulatory networks to clarify the processes that cause breast cancer and the progression of it. In this study, mRNA and miRNA expression studies including breast tumors and normal samples were extracted from the GEO microarray database. Two independent mRNA studies and a miRNA study were selected and reanalyzed. Differentially expressed (DE) mRNAs and miRNAs between breast tumor and normal samples were listed by using BRBArray Tools. CircuitsDB2 analysis conducted with DE miRNAs and mRNAs resulted in 3 significant circuits that are SOX10- and hsamiR-301a-dependent. The following significant circuits were characterized and validated bioinformatically by using web-based tools: SOX10→hsa-miR-301a→HOXA3, SOX10→hsa-miR-301a→KIT, and SOX10→hsa-miR-301a→NFIB. It can be concluded that regulatory motifs involving miRNAs and TFs may be useful for understanding breast cancer regulation and for predicting new biomarkers.

Modular combinatorial binding among human trans-acting factors reveals direct and indirect factor binding

Background

The combinatorial binding of trans-acting factors (TFs) to the DNA is critical to the spatial and temporal specificity of gene regulation. For certain regulatory regions, more than one regulatory module (set of TFs that bind together) are combined to achieve context-specific gene regulation. However, previous approaches are limited to either pairwise TF co-association analysis or assuming that only one module is used in each regulatory region.

Results

We present a new computational approach that models the modular organization of TF combinatorial binding. Our method learns compact and coherent regulatory modules from in vivo binding data using a topic model. We found that the binding of 115 TFs in K562 cells can be organized into 49 interpretable modules. Furthermore, we found that tens of thousands of regulatory regions use multiple modules, a structure that cannot be observed with previous hard clustering based methods. The modules discovered recapitulate many published protein-protein physical interactions, have consistent functional annotations of chromatin states, and uncover context specific co-binding such as gene proximal binding of NFY + FOS + SP and distal binding of NFY + FOS + USF. For certain TFs, the co-binding partners of direct binding (motif present) differs from those of indirect binding (motif absent); the distinct set of co-binding partners can predict whether the TF binds directly or indirectly with up to 95% accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory modules.

Conclusions

Our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of combinatorial binding.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3434-3) contains supplementary material, which is available to authorized users.

A network analysis of miRNA mediated gene regulation of rice: crosstalk among biological processes

To understand the network architecture of miRNA mediated regulations at the genomic and functional levels of rice, we have made an unambiguous annotation of the experimentally verified miRNAs, predicted their targets and the possible biological functions they can affect. Some functions, namely translational and protein modifications and photosynthesis are targeted by higher percentage of miRNA. Using transformation procedures, we constructed a genome scale miRNA-miRNA functional synergistic network (MFSN). The analysis of MFSN modules help to identify miRNAs co-regulating target genes having several interrelated biological processes. Some of these target genes are also co-expressed under particular conditions. For example, the genes co-expressed under drought conditions as well as those targeted by miRNAs present in a MFSN module have interdependent biological processes namely, photosynthesis, cell-wall biogenesis, root development and xylan synthesis. The stress-induced miRNAs and their distributions, and the presence of transcription factors in the target set of MFSN modules were also analyzed.

Identification of miRNA-mRNA regulatory modules by exploring collective group relationships

Background

microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into gene regulation and cell functions. Several computational approaches have been developed to discover miRNA-mRNA regulatory modules (MMRMs) with a common aim to elucidate miRNA-mRNA regulatory relationships. However, most existing methods do not consider the collective relationships between a group of miRNAs and the group of targeted mRNAs in the process of discovering MMRMs. Our aim is to develop a framework to discover MMRMs and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships.

Results

We propose DIscovering COllective group RElationships (DICORE), an effective computational framework for revealing miRNA-mRNA regulatory relationships. We utilize the notation of collective group relationships to build the computational framework. The method computes the collaboration scores of the miRNAs and mRNAs on the basis of their interactions with mRNAs and miRNAs, respectively. Then it determines the groups of miRNAs and groups of mRNAs separately based on their respective collaboration scores. Next, it calculates the strength of the collective relationship between each pair of miRNA group and mRNA group using canonical correlation analysis, and the group pairs with significant canonical correlations are considered as the MMRMs. We applied this method to three gene expression datasets, and validated the computational discoveries.

Conclusions

Analysis of the results demonstrates that a large portion of the regulatory relationships discovered by DICORE is consistent with the experimentally confirmed databases. Furthermore, it is observed that the top mRNAs that are regulated by the miRNAs in the identified MMRMs are highly relevant to the biological conditions of the given datasets. It is also shown that the MMRMs identified by DICORE are more biologically significant and functionally enriched.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2300-z) contains supplementary material, which is available to authorized users.

Identification of subtype specific miRNA-mRNA functional regulatory modules in matched miRNA-mRNA expression data: multiple myeloma as a case

Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs) through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM), to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.

Systematic exploration of autonomous modules in noisy microRNA-target networks for testing the generality of the ceRNA hypothesis

BACKGROUND

In the competing endogenous RNA (ceRNA) hypothesis, different transcripts communicate through a competition for their common targeting microRNAs (miRNAs). Individual examples have clearly shown the functional importance of ceRNA in gene regulation and cancer biology. It remains unclear to what extent gene expression levels are regulated by ceRNA in general. One major hurdle to studying this problem is the intertwined connections in miRNA-target networks, which makes it difficult to isolate the effects of individual miRNAs.

RESULTS

Here we propose computational methods for decomposing a complex miRNA-target network into largely autonomous modules called microRNA-target biclusters (MTBs). Each MTB contains a relatively small number of densely connected miRNAs and mRNAs with few connections to other miRNAs and mRNAs. Each MTB can thus be individually analyzed with minimal crosstalk with other MTBs. Our approach differs from previous methods for finding modules in miRNA-target networks by not making any pre-assumptions about expression patterns, thereby providing objective information for testing the ceRNA hypothesis. We show that the expression levels of miRNAs and mRNAs in an MTB are significantly more anti-correlated than random miRNA-mRNA pairs and other validated and predicted miRNA-target pairs, demonstrating the biological relevance of MTBs. We further show that there is widespread correlation of expression between mRNAs in same MTBs under a wide variety of parameter settings, and the correlation remains even when co-regulatory effects are controlled for, which suggests potential widespread expression buffering between these mRNAs, which is consistent with the ceRNA hypothesis. Lastly, we also propose a potential use of MTBs in functional annotation of miRNAs.

CONCLUSIONS

MTBs can be used to help identify autonomous miRNA-target modules for testing the generality of the ceRNA hypothesis experimentally. The identified modules can also be used to test other properties of miRNA-target networks in general.

Identifying direct miRNA-mRNA causal regulatory relationships in heterogeneous data

Discovering the regulatory relationships between microRNAs (miRNAs) and mRNAs is an important problem that interests many biologists and medical researchers. A number of computational methods have been proposed to infer miRNA-mRNA regulatory relationships, and are mostly based on the statistical associations between miRNAs and mRNAs discovered in observational data. The miRNA-mRNA regulatory relationships identified by these methods can be both direct and indirect regulations. However, differentiating direct regulatory relationships from indirect ones is important for biologists in experimental designs. In this paper, we present a causal discovery based framework (called DirectTarget) to infer direct miRNA-mRNA causal regulatory relationships in heterogeneous data, including expression profiles of miRNAs and mRNAs, and miRNA target information. DirectTarget is applied to the Epithelial to Mesenchymal Transition (EMT) datasets. The validation by experimentally confirmed target databases suggests that the proposed method can effectively identify direct miRNA-mRNA regulatory relationships. To explore the upstream regulators of miRNA regulation, we further identify the causal feedforward patterns (CFFPs) of TF-miRNA-mRNA to provide insights into the miRNA regulation in EMT. DirectTarget has the potential to be applied to other datasets to elucidate the direct miRNA-mRNA causal regulatory relationships and to explore the regulatory patterns.

C2Analyzer: Co-target-co-function analyzer

MicroRNAs (miRNAs) interact with their target mRNAs and regulate biological processes at post-transcriptional level. While one miRNA can target many mRNAs, a single mRNA can also be targeted by a set of miRNAs. The targeted mRNAs may be involved in different biological processes that are described by gene ontology (GO) terms. The major challenges involved in analyzing these multitude regulations include identification of the combinatorial regulation of miRNAs as well as determination of the co-functionally-enriched miRNA pairs. The C2Analyzer: Co-target–Co-function Analyzer, is a Perl-based, versatile and user-friendly web tool with online instructions. Based on the hypergeometric analysis, this novel tool can determine whether given pairs of miRNAs are co-functionally enriched. For a given set of GO term(s), it can also identify the set of miRNAs whose targets are enriched in the given GO term(s). Moreover, C2Analyzer can also identify the co-targeting miRNA pairs, their targets and GO processes, which they are involved in. The miRNA–miRNA co-functional relationship can also be saved as a .txt file, which can be used to further visualize the co-functional network by using other software like Cytoscape. C2Analyzer is freely available at www.bioinformatics.org/c2analyzer.

Understanding principles of miRNA target recognition and function through integrated biological and bioinformatics approaches

In recent times, microRNA (miRNA) have emerged as primary regulators of fundamental biological processes including cellular differentiation, proliferation, apoptosis, as well as synaptic plasticity. However, miRNAs bind their targets with only partial complementarity, making it very challenging to determine exactly how a miRNA is functioning in specific biological environments. This review discusses key principles of miRNA target recognition and function which have emerged through the progressive advancement of biological and bioinformatics approaches. Ultimately, the integration of gene expression and biochemical methods with sequence- and systems-based bioinformatics approaches will reveal profound insights regarding the importance of target contextual features in determining miRNA target recognition and regulatory outcome, as well as the importance of RNA interaction networks in enabling miRNA to regulate different target genes and functions in specific biological contexts. There is therefore a demand for the elegant design of future experiments such that principles of context-specific miRNA target recognition and regulatory outcome can be accurately modeled in normal developmental and disease states.

Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms

MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states. A lot of research has tried to discover microRNAs and target gene interactions by implementing machine learning classifiers with target prediction algorithms. However, high rates of false positives have been reported as a result of undetermined factors which will affect recognition. Therefore, integrating diverse techniques could improve the prediction. In this paper we propose identifying microRNAs target of Arabidopsis thaliana by integrating prediction scores from PITA, miRanda and RNAHybrid algorithms used as a feature vector of microRNA-target interactions, and then implementing SVM, random forest tree and neural network machine learning algorithms to make final predictions by majority voting. Furthermore, microRNA target genes are linked with their protein-protein interaction (PPI) partners. We focus on plant resistance genes and transcription factor information to provide new insights into plant pathogen interaction networks. Downstream pathways are characterized by the Jaccard coefficient, which is implemented based on Gene Ontology. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/At_miRNA/.

Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning

BACKGROUND

Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes.

RESULTS

We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits.

CONCLUSIONS

Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.

Identifying dysfunctional miRNA-mRNA regulatory modules by inverse activation, cofunction, and high interconnection of target genes: a case study of glioblastoma

BACKGROUND

Accumulating evidence demonstrates that complex diseases may arise from cooperative effects of multiple dysfunctional miRNAs. Thus, identifying abnormal functions cooperatively regulated by multiple miRNAs is useful for understanding the pathogenesis of complex diseases.

METHODS

In this study, we proposed a multistep method to identify dysfunctional miRNA-mRNA regulatory modules (dMiMRMs) in a specific disease, in which a group of miRNAs cooperatively regulate a group of target genes involved in a specific function. We identified dysfunctional miRNAs, which were differentially expressed and inversely regulated most of their target genes, by integrating paired miRNA and mRNA expression profiles and miRNA target information. Then, we identified cooperative functional units, in each of which a pair of miRNAs cooperatively repressed function-enriched and highly interconnected target genes. Finally, the cooperative functional units were assembled into dMiMRMs.

RESULTS

We applied our method to glioblastoma (GBM) and identified GBM-associated dMiMRMs at the population, subtype, and individual levels. We identified 5 common dMiMRMs using all GBM samples, 3 of which were associated with the prognosis in patients with GBM and were better predictors of prognosis than were miRNAs or mRNAs alone. By applying our approach to GBM subtypes, we found consistent dMiMRMs across GBM subtypes, and some subtype-specific dMiMRMs were observed. Furthermore, personalized dMiMRMs were identified, suggesting significant individual differences in different patients with GBM.

CONCLUSIONS

Our method provides the capability to identify miRNA-mediated dysfunctional mechanisms underlying complex diseases.

Mining featured patterns of MiRNA interaction based on sequence and structure similarity

MicroRNA (miRNA) is an endogenous small noncoding RNA that plays an important role in gene expression through the post-transcriptional gene regulation pathways. There are many literature works focusing on predicting miRNA targets and exploring gene regulatory networks of miRNA families. We suggest, however, the study to identify the interaction between miRNAs is insufficient. This paper presents a framework to identify relationships between miRNAs using joint entropy, to investigate the regulatory features of miRNAs. Both the sequence and secondary structure are taken into consideration to make our method more relevant from the biological viewpoint. Further, joint entropy is applied to identify correlated miRNAs, which are more desirable from the perspective of the gene regulatory network. A data set including Drosophila melanogaster and Anopheles gambiae is used in the experiment. The results demonstrate that our approach is able to identify known miRNA interaction and uncover novel patterns of miRNA regulatory network.

Inferring microRNA-mRNA causal regulatory relationships from expression data

MOTIVATION

microRNAs (miRNAs) are known to play an essential role in the post-transcriptional gene regulation in plants and animals. Currently, several computational approaches have been developed with a shared aim to elucidate miRNA-mRNA regulatory relationships. Although these existing computational methods discover the statistical relationships, such as correlations and associations between miRNAs and mRNAs at data level, such statistical relationships are not necessarily the real causal regulatory relationships that would ultimately provide useful insights into the causes of gene regulations. The standard method for determining causal relationships is randomized controlled perturbation experiments. In practice, however, such experiments are expensive and time consuming. Our motivation for this study is to discover the miRNA-mRNA causal regulatory relationships from observational data.

RESULTS

We present a causality discovery-based method to uncover the causal regulatory relationship between miRNAs and mRNAs, using expression profiles of miRNAs and mRNAs without taking into consideration the previous target information. We apply this method to the epithelial-to-mesenchymal transition (EMT) datasets and validate the computational discoveries by a controlled biological experiment for the miR-200 family. A significant portion of the regulatory relationships discovered in data is consistent with those identified by experiments. In addition, the top genes that are causally regulated by miRNAs are highly relevant to the biological conditions of the datasets. The results indicate that the causal discovery method effectively discovers miRNA regulatory relationships in data. Although computational predictions may not completely replace intervention experiments, the accurate and reliable discoveries in data are cost effective for the design of miRNA experiments and the understanding of miRNA-mRNA regulatory relationships.

Discovering functional microRNA-mRNA regulatory modules in heterogeneous data

microRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translation inhibition. They are pivotal regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate many targets that are affected by miRNAs, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles and sequence information to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. In this chapter, we first briefly review the computational methods for discovering miRNA targets and miRNA-mRNA regulatory modules, and then focus on a method of identifying functional miRNA-mRNA regulatory modules by integrating multiple data sets from different sources.

Identifying miRNAs, targets and functions

microRNAs (miRNAs) are small endogenous non-coding RNAs that function as the universal specificity factors in post-transcriptional gene silencing. Discovering miRNAs, identifying their targets and further inferring miRNA functions have been a critical strategy for understanding normal biological processes of miRNAs and their roles in the development of disease. In this review, we focus on computational methods of inferring miRNA functions, including miRNA functional annotation and inferring miRNA regulatory modules, by integrating heterogeneous data sources. We also briefly introduce the research in miRNA discovery and miRNA-target identification with an emphasis on the challenges to computational biology.

Inferring functional miRNA-mRNA regulatory modules in epithelial-mesenchymal transition with a probabilistic topic model

MicroRNAs (miRNAs) play important roles in gene regulatory networks. In this paper, we propose a probabilistic topic model to infer regulatory networks of miRNAs and their target mRNAs for specific biological conditions at the post-transcriptional level, so-called functional miRNA-mRNA regulatory modules (FMRMs). The probabilistic model used in this paper can effectively capture the relationship between miRNAs and mRNAs in specific cellular conditions. Furthermore, the proposed method identifies negatively and positively correlated miRNA-mRNA pairs which are associated with epithelial, mesenchymal, and other condition in EMT (epithelial-mesenchymal transition) data set, respectively. Results on EMT data sets show that the inferred FMRMs can potentially construct the biological chain of 'miRNA→mRNA→condition' at the post-transcriptional level.

Identification of microRNA-mRNA modules using microarray data

BACKGROUND

MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs.

RESULTS

We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest.

CONCLUSIONS

Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.

Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation

MOTIVATION

MicroRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in.

RESULTS

We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules.

Analysis of antisense expression by whole genome tiling microarrays and siRNAs suggests mis-annotation of Arabidopsis orphan protein-coding genes

Background

MicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20–22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.

Principal Findings

We explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis ‘orphan’ hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the “ancient” (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for “new” rapidly-evolving MIRNA genes.

Conclusions

Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.

Computational methods for the identification of microRNA targets

MicroRNAs are pivotal regulators of development and cellular homeostasis. They act as post-transcriptional regulators, which control the stability and translation efficiency of their target mRNAs. The prediction of microRNA targets and detection of microRNA-mRNA regulatory modules (MRMs) are crucial components for understanding of microRNA functions. Numerous computational methods for microRNA target prediction have been developed. Computationally-predicted targets have been recently used in the integrative analysis of microRNA and mRNA expression analysis to identify microRNA targets and MRMs. In this article we review these recent developments in the integrative analysis methods. We also discuss the remaining challenges and our insights on future directions.

Computational discovery of miR-TF regulatory modules in human genome

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. They play an important role in several biological processes such as cell development and differentiation. Similar to transcription factors (TFs), miRNAs regulate gene expression in a combinatorial fashion, i.e., an individual miRNA can regulate multiple genes, and an individual gene can be regulated by multiple miRNAs. The functions of TFs in biological regulatory networks have been well explored. And, recently, a few studies have explored miRNA functions in the context of gene regulation networks. However, how TFs and miRNAs function together in the gene regulatory network has not yet been examined. In this paper, we propose a new computational method to discover the gene regulatory modules that consist of miRNAs, TFs, and genes regulated by them. We analyzed the regulatory associations among the sets of predicted miRNAs and sets of TFs on the sets of genes regulated by them in the human genome. We found 182 gene regulatory modules of combinatorial regulation by miRNAs and TFs (miR-TF modules). By validating these modules with the Gene Ontology (GO) and the literature, it was found that our method allows us to detect functionally-correlated gene regulatory modules involved in specific biological processes. Moreover, our miR-TF modules provide a global view of coordinated regulation of target genes by miRNAs and TFs.

Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers

BACKGROUND

Hepatitis C virus (HCV) is a major cause of chronic liver disease by infecting over 170 million people worldwide. Recent studies have shown that microRNAs (miRNAs), a class of small non-coding regulatory RNAs, are involved in the regulation of HCV infection, but their functions have not been systematically studied. We propose an integrative strategy for identifying the miRNA-mRNA regulatory modules that are associated with HCV infection. This strategy combines paired expression profiles of miRNAs and mRNAs and computational target predictions. A miRNA-mRNA regulatory module consists of a set of miRNAs and their targets, in which the miRNAs are predicted to coordinately regulate the level of the target mRNA.

RESULTS

We simultaneously profiled the expression of cellular miRNAs and mRNAs across 30 HCV positive or negative human liver biopsy samples using microarray technology. We constructed a miRNA-mRNA regulatory network, and using a graph theoretical approach, identified 38 miRNA-mRNA regulatory modules in the network that were associated with HCV infection. We evaluated the direct miRNA regulation of the mRNA levels of targets in regulatory modules using previously published miRNA transfection data. We analyzed the functional roles of individual modules at the systems level by integrating a large-scale protein interaction network. We found that various biological processes, including some HCV infection related canonical pathways, were regulated at the miRNA level during HCV infection.

CONCLUSION

Our regulatory modules provide a framework for future experimental analyses. This report demonstrates the utility of our approach to obtain new insights into post-transcriptional gene regulation at the miRNA level in complex human diseases.