miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships

Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis

BACKGROUND

Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy.

RESULTS

This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge.

CONCLUSION

Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BACKGROUND

RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.

RESULTS

Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.

CONCLUSIONS

Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Evidence that Transcriptional Alterations in Sarcoptes scabiei Are under Tight Post-Transcriptional (microRNA) Control

Here, we explored transcriptomic differences among early egg (Ee), late egg (Le) and adult female (Af) stages of the scabies mite, Sarcoptes scabiei, using an integrative bioinformatic approach. We recorded a high, negative correlation between miRNAs and genes with decreased mRNA transcription between the developmental stages, indicating substantial post-transcriptional repression; we also showed a positive correlation between miRNAs and genes with increased mRNA transcription, suggesting indirect post-transcriptional regulation. The alterations in mRNA transcription between the egg and adult female stages of S. scabiei were inferred to be linked to metabolism (including carbohydrate and lipid degradation, amino acid and energy metabolism), environmental information processing (e.g., signal transduction and signalling molecules), genetic information processing (e.g., transcription and translation) and/or organismal systems. Taken together, these results provide insight into the transcription of this socioeconomically important parasitic mite, with a particular focus on the egg stage. This work encourages further, detailed laboratory studies of miRNA regulation across all developmental stages of S. scabiei and might assist in discovering new intervention targets in the egg stage of S. scabiei.

Characterization of microRNA and gene expression in the cochlea of an echolocating bat (Rhinolophus affinis)

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression and play key roles in many biological processes, such as development and response to multiple stresses. However, little is known about their roles in generating novel phenotypes and phenotypic variation during the course of animal evolution. Here, we, for the first time, characterized the miRNAs of the cochlea in an echolocating bat (Rhinolophus affinis). We sampled eight individuals from two R. affinis subspecies with significant echolocation call frequency differences. We identified 365 miRNAs and 121 of them were novel. By searching sequences of these miRNAs precursors in multiple high-quality mammal genomes, we found one specific miRNA shared by all echolocating bats but not present in all other nonecholocating mammals. The targeted genes of this miRNA included several known hearing genes (e.g., KCNQ4 and GJB6). Together with the matched mRNA-seq data, we identified 1766 differentially expressed genes (DEGs) between the two subspecies and 555 of them were negatively regulated by differentially expressed miRNAs (DEMs). We found that almost half of known hearing genes in the list of all DEGs were regulated negatively by DEMs, suggesting an important role of miRNAs in call frequency variation of the two subspecies. These targeted DEGs included several important hearing genes (e.g., Piezo1, Piezo2, and CDH23) that have been shown to be important in ultrasonic hearing of echolocating mammals.

Analysing miRNA-Target Gene Networks in Inflammatory Bowel Disease and Other Complex Diseases Using Transcriptomic Data

Patients with inflammatory bowel disease (IBD) are known to have perturbations in microRNA (miRNA) levels as well as altered miRNA regulation. Although experimental methods have provided initial insights into the functional consequences that may arise due to these changes, researchers are increasingly utilising novel bioinformatics approaches to further dissect the role of miRNAs in IBD. The recent exponential increase in transcriptomics datasets provides an excellent opportunity to further explore the role of miRNAs in IBD pathogenesis. To effectively understand miRNA-target gene interactions from gene expression data, multiple database resources are required, which have become available in recent years. In this technical note, we provide a step-by-step protocol for utilising these state-of-the-art resources, as well as systems biology approaches to understand the role of miRNAs in complex disease pathogenesis. We demonstrate through a case study example how to combine the resulting miRNA-target gene networks with transcriptomics data to find potential disease-specific miRNA regulators and miRNA-target genes in Crohn’s disease. This approach could help to identify miRNAs that may have important disease-modifying effects in IBD and other complex disorders, and facilitate the discovery of novel therapeutic targets.

Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes

MOTIVATION

microRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using 'pseudotime' concept have inspired us to develop a pseudotime-based method to infer the miRNA-mRNA relationships characterizing a biological process by taking into account the temporal aspect of the process.

RESULTS

We have developed a novel approach, called pseudotime causality, to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition, a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilizing the pseudotemporal information from the data helps reveal the gene regulation in a biological process much better than using the static information.

AVAILABILITY AND IMPLEMENTATION

R scripts and datasets can be found at https://github.com/AndresMCB/PTC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.

Daphnia magna modifies its gene expression extensively in response to caloric restriction revealing a novel effect on haemoglobin isoform preference

Caloric restriction (CR) produces clear phenotypic effects within and between generations of the model crustacean Daphnia magna. We have previously established that micro-RNAs and cytosine methylation change in response to CR in this organism, and we demonstrate here that CR has a dramatic effect on gene expression. Over 6,000 genes were differentially expressed between CR and well-fed D. magna, with a bias towards up-regulation of genes under caloric restriction. We identified a highly expressed haemoglobin gene that responds to CR by changing isoform proportions. Specifically, a transcript containing three haem-binding erythrocruorin domains was strongly down-regulated under CR in favour of transcripts containing fewer or no such domains. This change in the haemoglobin mix is similar to the response to hypoxia in Daphnia, which is mediated through the transcription factor hypoxia-inducible factor 1, and ultimately the mTOR signalling pathway. This is the first report of a role for haemoglobin in the response to CR. We also observed high absolute expression of superoxide dismutase (SOD) in normally fed individuals, which contrasts with observations of high SOD levels under CR in other taxa. However, key differentially expressed genes, like SOD, were not targeted by differentially expressed micro-RNAs. Whether the link between haemoglobin and CR occurs in other organisms, or is related to the aquatic lifestyle, remains to be tested. It suggests that one response to CR may be to simply transport less oxygen and lower respiration.

MiRACLe: an individual-specific approach to improve microRNA-target prediction based on a random contact model

Deciphering microRNA (miRNA) targets is important for understanding the function of miRNAs as well as miRNA-based diagnostics and therapeutics. Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA-mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https://github.com/PANWANG2014/miRACLe.

CBNA: A control theory based method for identifying coding and non-coding cancer drivers

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.

Cancer is a disease of cells in human body and it causes a high rate of deaths worldwide. There has been evidence that coding and non-coding RNAs are key players in the initialisation and progression of cancer. These coding and non-coding RNAs are considered as cancer drivers. To design better diagnostic and therapeutic plans for cancer patients, we need to know the roles of cancer drivers in cancer development as well as their regulatory mechanisms in the human body. In this study, we propose a novel framework to identify coding and non-coding cancer drivers (i.e. miRNA cancer drivers). The proposed framework is applied to the breast cancer dataset for identifying drivers of breast cancer. Comparing our method with existing methods in predicting coding cancer drivers, our method shows a better performance. Several miRNA cancer drivers predicted by our method have already been validated by literature. The predicted cancer drivers by our method could be a potential source for further wet-lab experiments to discover the causes of cancer. In addition, the proposed method can be used to detect drivers of cancer subtypes and drivers of the epithelial-mesenchymal transition in cancer.

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes.

miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

BACKGROUND

MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.

RESULTS

Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.

CONCLUSIONS

Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ .

Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction

BACKGROUND

microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments.

RESULTS

In this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method.

CONCLUSIONS

This research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.

miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase

BACKGROUND

miRBase is the primary repository for published miRNA sequence and annotation data, and serves as the "go-to" place for miRNA research. However, the definition and annotation of miRNAs have been changed significantly across different versions of miRBase. The changes cause inconsistency in miRNA related data between different databases and articles published at different times. Several tools have been developed for different purposes of querying and converting the information of miRNAs between different miRBase versions, but none of them individually can provide the comprehensive information about miRNAs in miRBase and users will need to use a number of different tools in their analyses.

RESULTS

We introduce miRBaseConverter, an R package integrating the latest miRBase version 22 available in Bioconductor to provide a suite of functions for converting and retrieving miRNA name (ID), accession, sequence, species, version and family information in different versions of miRBase. The package is implemented in R and available under the GPL-2 license from the Bioconductor website ( http://bioconductor.org/packages/miRBaseConverter/ ). A Shiny-based GUI suitable for non-R users is also available as a standalone application from the package and also as a web application at http://nugget.unisa.edu.au:3838/miRBaseConverter . miRBaseConverter has a built-in database for querying miRNA information in all species and for both pre-mature and mature miRNAs defined by miRBase. In addition, it is the first tool for batch querying the miRNA family information. The package aims to provide a comprehensive and easy-to-use tool for miRNA research community where researchers often utilize published miRNA data from different sources.

CONCLUSIONS

The Bioconductor package miRBaseConverter and the Shiny-based web application are presented to provide a suite of functions for converting and retrieving miRNA name, accession, sequence, species, version and family information in different versions of miRBase. The package will serve a wide range of applications in miRNA research and could provide a full view of the miRNAs of interest.

Novel microRNAs are associated with population divergence in transcriptional response to thermal stress in an intertidal copepod

The role of gene expression in adaptation to differing thermal environments has been assayed extensively. Yet, in most natural systems, analyses of gene expression reveal only one level of the complexity of regulatory machineries. MicroRNAs (miRNAs) are small noncoding RNAs which are key components of many gene regulatory networks, and they play important roles in a variety of cellular pathways by modulating post-transcriptional quantities of mRNA available for protein synthesis. The characterization of miRNA loci and their regulatory dynamics in nonmodel systems are still largely understudied. In this study, we examine the role of miRNAs in response to high thermal stress in the intertidal copepod Tigriopus californicus. Allopatric populations of this species show varying levels of local adaptation with respect to thermal regimes, and previous studies showed divergence in gene expression between populations from very different thermal environments. We examined the transcriptional response to temperature stress in two populations separated by only 8 km by utilizing RNA-seq to quantify both mRNA and miRNA levels. Using the currently available genome sequence, we first describe the repertoire of miRNAs in T. californicus and assess the degree to which transcriptional response to temperature stress is governed by miRNA activity. The two populations showed large differences in the number of genes involved, the magnitude of change in commonly used genes and in the number of miRNAs involved in transcriptional modulation during stress. Our results suggest that an increased level of regulatory network complexity may underlie improved survivorship under thermal stress in one of the populations.

Molecular alterations during larval development of Haemonchus contortus in vitro are under tight post-transcriptional control

In this study, we explored the molecular alterations in the developmental switch from the L3 to the exsheathed L3 (xL3) and to the L4 stage of Haemonchus contortus in vitro using an integrated transcriptomic, proteomic and bioinformatic approach. Totals of 9,754 mRNAs, 88 microRNAs (miRNAs) and 1,591 proteins were identified, and 6,686 miRNA-mRNA pairs inferred in all larval stages studied. Approximately 16% of transcripts in the combined transcriptome (representing all three larval stages) were expressed as proteins, and there were positive correlations (r = 0.39-0.44) between mRNA transcription and protein expression in the three distinct developmental stages of the parasite. Of the predicted targets, 1,019 (27.0%) mRNA transcripts were expressed as proteins, and there was a negative correlation (r = -0.60 to -0.50) in the differential mRNA transcription and protein expression between developmental stages upon pairwise comparison. The changes in transcription (mRNA and miRNA) and protein expression from the free-living to the parasitic life cycle phase of H. contortus related to enrichments in biological pathways associated with metabolism (e.g., carbohydrate and lipid degradation, and amino acid metabolism), environmental information processing (e.g., signal transduction, signalling molecules and interactions) and/or genetic information processing (e.g., transcription and translation). Specifically, fatty acid degradation, steroid hormone biosynthesis and the Rap1 signalling pathway were suppressed, whereas transcription, translation and protein processing in the endoplasmic reticulum were upregulated during the transition from the free-living L3 to the parasitic xL3 and L4 stages of the nematode in vitro. Dominant post-transcriptional regulation was inferred to elicit these changes, and particular miRNAs (e.g., hco-miR-34 and hco-miR-252) appear to play roles in stress responses and/or environmental adaptations during developmental transitions of H. contortus. Taken together, these integrated results provide a comprehensive insight into the developmental biology of this important parasite at the molecular level in vitro. The approach applied here to H. contortus can be readily applied to other parasitic nematodes.

Network analysis of EMT and MET micro-RNA regulation in breast cancer

Over the last years, microRNAs (miRs) have shown to be crucial for breast tumour establishment and progression. To understand the influence that miRs have over transcriptional regulation in breast cancer, we constructed mutual information networks from 86 TCGA matched breast invasive carcinoma and control tissue RNA-Seq and miRNA-Seq sequencing data. We show that miRs are determinant for tumour and control data network structure. In tumour data network, miR-200, miR-199 and neighbour miRs seem to cooperate on the regulation of the acquisition of epithelial and mesenchymal traits by the biological processes: Epithelial-Mesenchymal Transition (EMT) and Mesenchymal to Epithelial Transition (MET). Despite structural differences between tumour and control networks, we found a conserved set of associations between miR-200 family members and genes such as VIM, ZEB-1/2 and TWIST-1/2. Further, a large number of miRs observed in tumour network mapped to a specific chromosomal location in DLK1-DIO3 (Chr14q32); some of those miRs have also been associated with EMT and MET regulation. Pathways related to EMT and TGF-beta reinforce the relevance of miR-200, miR-199 and DLK1-DIO3 cluster in breast cancer. With this approach, we stress that miR inclusion in gene regulatory network construction improves our understanding of the regulatory mechanisms underlying breast cancer biology.

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.

Understanding how genetic variation between individuals determines variation in observable traits or disease risk is one of the core aims of genetics. It is known that genetic variation often affects gene regulatory DNA elements and directly causes variation in expression of nearby genes. This effect in turn cascades down to other genes via the complex pathways and gene interaction networks that ultimately govern how cells operate in an ever changing environment. In theory, when genetic variation and gene expression levels are measured simultaneously in a large number of individuals, the causal effects of genes on each other can be inferred using statistical models similar to those used in randomized controlled trials. We developed a novel method and ultra-fast software Findr which, unlike existing methods, takes into account the complex but unknown network context when predicting causality between specific gene pairs. Findr’s predictions have a significantly higher overlap with known gene networks compared to existing methods, using both simulated and real data. Findr is also nearly a million times faster, and hence the only software in its class that can handle modern datasets where the expression levels of ten-thousands of genes are simultaneously measured in hundreds to thousands of individuals.

MicroRNAs, Regulatory Networks, and Comorbidities: Decoding Complex Systems

MicroRNAs (miRNAs) are small noncoding RNAs involved in the posttranscriptional regulation of messenger RNAs (mRNAs). Each miRNA targets a specific set of mRNAs. Upon binding the miRNA inhibits mRNA translation or facilitate mRNA degradation. miRNAs are frequently deregulated in several pathologies including cancer and cardiovascular diseases. Since miRNAs have a crucial role in fine-tuning the expression of their targets, they have been proposed as biomarkers of disease progression and prognostication.In this chapter we discuss different approaches for computational predictions of miRNA targets based on sequence complementarity and integration of expression data. In the last section of the chapter we discuss new opportunities in the study of miRNA regulatory networks in the context of temporal disease progression and comorbidities.

DMirNet: Inferring direct microRNA-mRNA association networks

Background

MicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles. 1) Inferred correlations from the observed expression profiles using conventional correlation-based methods include numerous erroneous links or over-estimated edge weight due to the transitive information flow among direct associations. 2) Due to the high-dimension-low-sample-size problem on the microarray dataset, it is difficult to obtain an accurate and reliable estimate of the empirical correlations between all pairs of expression profiles. 3) Because the previously proposed computational methods usually suffer from varying performance across different datasets, a more reliable model that guarantees optimal or suboptimal performance across different datasets is highly needed.

Results

In this paper, we present DMirNet, a new framework for identifying direct miRNA-mRNA association networks. To tackle the aforementioned issues, DMirNet incorporates 1) three direct correlation estimation methods (namely Corpcor, SPACE, Network deconvolution) to infer direct miRNA-mRNA association networks, 2) the bootstrapping method to fully utilize insufficient training expression profiles, and 3) a rank-based Ensemble aggregation to build a reliable and robust model across different datasets.

Our empirical experiments on three datasets demonstrate the combinatorial effects of necessary components in DMirNet. Additional performance comparison experiments show that DMirNet outperforms the state-of-the-art Ensemble-based model [1] which has shown the best performance across the same three datasets, with a factor of up to 1.29. Further, we identify 43 putative novel multi-cancer-related miRNA-mRNA association relationships from an inferred Top 1000 direct miRNA-mRNA association network.

Conclusions

We believe that DMirNet is a promising method to identify novel direct miRNA-mRNA relations and to elucidate the direct miRNA-mRNA association networks. Since DMirNet infers direct relationships from the observed data, DMirNet can contribute to reconstructing various direct regulatory pathways, including, but not limited to, the direct miRNA-mRNA association networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0373-1) contains supplementary material, which is available to authorized users.

Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods

MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression. An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the complex biological processes at play during malignancy. Despite advances, determination of miR–target interactions (MTIs) and identification of functional modules composed of miRs and their specific targets remain a challenge. A large amount of data generated by high-throughput methods from various sources are available to investigate MTIs. The development of data-driven tools to harness these multi-dimensional data has resulted in significant progress over the past decade. In parallel, large-scale cancer genomic projects are allowing new insights into the commonalities and disparities of miR–target regulation across cancers. In the first half of this review, we explore methods for identification of pairwise MTIs, and in the second half, we explore computational tools for discovery of miR-regulatory modules in a cancer-specific and pan-cancer context. We highlight strengths and limitations of each of these tools as a practical guide for the computational biologists.