Data Analysis and BioInformatics in real-time qPCR (4)
inegrative data analysis

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Molecular Regulatory Networks
Big Data in Transcriptomics & Molecular Biology

Integrated analysis of microRNA and mRNA expression

Various papers will be presented explaining the integrated analysis of expressed mRNA and microRNA. Most of the shown publications are connected to web based data mining tools for free access.
Tools for integrated analysis:

Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D
Nat Rev Genet. 2015 16(2): 85-97

Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.




Comprehensive comparison of microRNA target prediction methods

MicroRNAs (miRNAs) are short endogenous noncoding RNAs that bind to target mRNAs, usually resulting in degradation and translational repression. Identification of miRNA targets is crucial for deciphering functional roles of the numerous miRNAs that are rapidly generated by sequencing efforts. Computational prediction methods are widely used for high-throughput generation of putative miRNA targets.
Researcher from the University of Alberta review a comprehensive collection of 38 miRNA sequence-based computational target predictors in animals that were developed over the past decade. Their in-depth analysis considers all significant perspectives including the underlying predictive methodologies with focus on how they draw from the mechanistic basis of the miRNA-mRNA interaction. They also discuss ease of use, availability, impact of the considered predictors and the evaluation protocols that were used to assess them. The gene-level evaluation is based on three benchmark data sets that rely on different ways to annotate targets including biochemical assays, microarrays and pSILAC. The researchers offer practical advice on selection of appropriate predictors according to certain properties of miRNA sequences, characteristics of a specific application and desired levels of predictive quality. Finally, they discuss future work related to the design of new models, data quality, improved usability, need for standardized evaluation and ability to predict mRNA expression changes.
Methodologies and the corresponding mechanistic basis of miRNA–mRNA interaction used by the miRNA target predictors.

Comprehensive overview and assessment of computational prediction of microRNA targets in animals
Xiao Fan & Lukasz Kurgan
Brief Bioinform (2014)


MicroRNAs (miRNAs) are short endogenous noncoding RNAs that bind to target mRNAs, usually resulting in degradation and translational repression. Identification of miRNA targets is crucial for deciphering functional roles of the numerous miRNAs that are rapidly generated by sequencing efforts. Computational prediction methods are widely used for high-throughput generation of putative miRNA targets. We review a comprehensive collection of 38 miRNA sequence-based computational target predictors in animals that were developed over the past decade. Our in-depth analysis considers all significant perspectives including the underlying predictive methodologies with focus on how they draw from the mechanistic basis of the miRNA–mRNA interaction. We also discuss ease of use, availability, impact of the considered predictors and the evaluation protocols that were used to assess them. We are the first to comparatively and comprehensively evaluate seven representative methods when predicting miRNA targets at the duplex and gene levels. The gene-level evaluation is based on three benchmark data sets that rely on different ways to annotate targets including biochemical assays, microarrays and pSILAC. We offer practical advice on selection of appropriate predictors according to certain properties of miRNA sequences, characteristics of a specific application and desired levels of predictive quality. We also discuss future work related to the design of new models, data quality, improved usability, need for standardized evaluation and ability to predict mRNA expression changes.

miRWalk -- database:  prediction of possible miRNA binding sites by "walking" the genes of three genomes
Dweep H, Sticht C, Pandey P, Gretz N.
J Biomed Inform. 2011 Oct;44(5): 839-847

MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3'-UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3'-UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat. All mRNAs, mitochondrial genes and 10 kb upstream flanking regions of all known genes of human, mouse and rat were analyzed by using a newly developed algorithm named 'miRWalk' as well as with eight already established programs for putative miRNA binding sites. An automated and extensive text-mining search was performed on PubMed database to extract validated information on miRNAs. Combined information was put into a MySQL database. miRWalk presents predicted and validated information on miRNA-target interaction. Such a resource enables researchers to validate new targets of miRNA not only on 3'-UTR, but also on the other regions of all known genes. The 'Validated Target module' is updated every month and the 'Predicted Target module' is updated every 6 months. miRWalk is freely available at http://mirwalk.uni-hd.de

Quantification of miRNA-mRNA interactions
Muniategui A, Nogales-Cadenas R, Vázquez M, L Aranguren X, Agirre X, Luttun A, Prosper F, Pascual-Montano A, Rubio A.
Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.
PLoS One. 2012;7(2):e30766. Epub 2012 Feb 14.
There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es

miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es

Posttranscriptional Regulatory Networks:  From Expression Profi ling to Integrative Analysis of mRNA and MicroRNA Data
Swanhild U. Meyer, Katharina Stoecker, Steffen Sass, Fabian J. Theis and Michael W. Pfaffl
Chapter 15  in  Quantitative Real-Time PCR: Methods and Protocols 2014   (Methods in Molecular Biology)
by Roberto Biassoni, Alessandro Raso

Protein coding RNAs are posttranscriptionally regulated by microRNAs, a class of small noncoding RNAs. Insights in messenger RNA (mRNA) and microRNA (miRNA) regulatory interactions facilitate the understanding of fi ne-tuning of gene expression and might allow better estimation of protein synthesis. However, in silico predictions of mRNA–microRNA interactions do not take into account the specifi c transcriptomic status of the biological system and are biased by false positives. One possible solution to predict rather reliable mRNA-miRNA relations in the specifi c biological context is to integrate real mRNA and miRNA transcriptomic data as well as in silico target predictions. This chapter addresses the workfl ow and methods one can apply for expression profi ling and the integrative analysis of mRNA and miRNA data, as well as how to analyze and interpret results, and how to build up models of posttranscriptional regulatory networks.

Integrative Analysis of MicroRNA and mRNA Data Reveals an Orchestrated Function of MicroRNAs in Skeletal Myocyte Differentiation in Response to TNF-α or IGF1.
Meyer SU, Sass S, Mueller NS, Krebs S, Bauersachs S, Kaiser S, Blum H, Thirion C, Krause S, Theis FJ, Pfaffl MW
PLoS One. 2015 10(8):e0135284 -- eCollection 2015

INTRODUCTION: Skeletal muscle cell differentiation is impaired by elevated levels of the inflammatory cytokine tumor necrosis factor-α (TNF-α) with pathological significance in chronic diseases or inherited muscle disorders. Insulin like growth factor-1 (IGF1) positively regulates muscle cell differentiation. Both, TNF-α and IGF1 affect gene and microRNA (miRNA) expression in this process. However, computational prediction of miRNA-mRNA relations is challenged by false positives and targets which might be irrelevant in the respective cellular transcriptome context. Thus, this study is focused on functional information about miRNA affected target transcripts by integrating miRNA and mRNA expression profiling data.
METHODOLOGY & PRINCIPAL FINDINGS: Murine skeletal myocytes PMI28 were differentiated for 24 hours with concomitant TNF-α or IGF1 treatment. Both, mRNA and miRNA expression profiling was performed. The data-driven integration of target prediction and paired mRNA/miRNA expression profiling data revealed that i) the quantity of predicted miRNA-mRNA relations was reduced, ii) miRNA targets with a function in cell cycle and axon guidance were enriched, iii) differential regulation of anti-differentiation miR-155-5p and miR-29b-3p as well as pro-differentiation miR-335-3p, miR-335-5p, miR-322-3p, and miR-322-5p seemed to be of primary importance during skeletal myoblast differentiation compared to the other miRNAs, iv) the abundance of targets and affected biological processes was miRNA specific, and v) subsets of miRNAs may collectively regulate gene expression.
CONCLUSIONS: Joint analysis of mRNA and miRNA profiling data increased the process-specificity and quality of predicted relations by statistically selecting miRNA-target interactions. Moreover, this study revealed miRNA-specific predominant biological implications in skeletal muscle cell differentiation and in response to TNF-α or IGF1 treatment. Furthermore, myoblast differentiation-associated miRNAs are suggested to collectively regulate gene clusters and targets associated with enriched specific gene ontology terms or pathways. Predicted miRNA functions of this study provide novel insights into defective regulation at the transcriptomic level during myocyte proliferation and differentiation due to inflammatory stimuli.



Walking the interactome to identify human -- miRNA-disease associations through the functional link between miRNA targets and disease genes
Hongbo Shi, Juan Xu, Guangde Zhang, Liangde Xu, Chunquan Li, Li Wang, Zheng Zhao, Wei Jiang, Zheng Guo and Xia Li
BMC Systems Biology 2013, 7:101

Background: MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed.
Results: We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms.
Conclusions: In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.
Keywords: MiRNA, Disease genes, Random walk analysis, MiRNA-disease network


The multilayered complexity of ceRNA crosstalk and competition.
Yvonne Tay, John Rinn & Pier Paolo Pandolfi
Nature (2014) 505, 344–352

Recent reports have described an intricate interplay among diverse RNA species, including protein-coding messenger RNAs and non-coding RNAs such as long non-coding RNAs, pseudogenes and circular RNAs. These RNA transcripts act as competing endogenous RNAs (ceRNAs) or natural microRNA sponges — they communicate with and co-regulate each other by competing for binding to shared microRNAs, a family of small non-coding RNAs that are important post-transcriptional regulators of gene expression. Understanding this novel RNA crosstalk will lead to significant insight into gene regulatory networks and have implications in human development and disease.


Joint analysis of miRNA and mRNA expression data
Muniategui A, Pey J, Planes F, Rubio A.
Brief Bioinform. 2012 Jun 12.

miRNAs are small RNA molecules ('22 nt) that interact with their target mRNAs inhibiting translation or/and cleavaging the target mRNA. This interaction is guided by sequence complentarity and results in the reduction of mRNA and/or protein levels. miRNAs are involved in key biological processes and different diseases. Therefore, deciphering miRNA targets is crucial for diagnostics and therapeutics. However, miRNA regulatory mechanisms are complex and there is still no high-throughput and low-cost miRNA target screening technique. In recent years, several computational methods based on sequence complementarity of the miRNA and the mRNAs have been developed. However, the predicted interactions using these computational methods are inconsistent and the expected false positive rates are still large. Recently, it has been proposed to use the expression values of miRNAs and mRNAs (and/or proteins) to refine the results of sequence-based putative targets for a particular experiment. These methods have shown to be effective identifying the most prominent interactions from the databases of putative targets. Here, we review these methods that combine both expression and sequence-based putative targets to predict miRNA targets.


NEWS AND VIEWS -- miRNAs versus oncogenes: the power of social networking
Marcos Malumbres
Cell Division and Cancer group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

microRNAs (miRNAs) are small, non-coding RNAs that regulate the expression of proteins through specific target sites in the corresponding transcripts. These small RNAs may function as oncogenes or tumor suppressors by modulating the levels of critical proteins, and their relevance in human disease and therapy is now under intense investigation. The human genome encodes about 1500 miRNAs that are thought to regulate more than 30% of protein-coding genes. Individual miRNAs can target multiple genes and each protein-coding gene can be regulated by several miRNAs, making the analysis
of these networks difficult to explore. In a recent article published in Molecular Systems Biology, Uhlmann et al (2012) report a combined strategy to analyze the multiple miRNA–protein interactions that regulate cell proliferation in response to epidermal growth factor receptor (EGFR), an oncogenic pathway highly relevant in breast cancer. This analysis provides an unprecedented view of the combinatorial effort of miRNAs to control a signaling pathway at different levels. As oncogenic pathways are often resistant to the inhibition of individual regulators, this analysis also provides the molecular basis for selecting individual miRNAs, or a set of a few miRNAs, whose combined activity may be strong enough to treat breast tumors.

ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
Chao Wu, Eric E. Bardes, Anil G. Jegga and Bruce J. Aronow
Nucleic Acids Research, 2014, Vol. 12, Web Server issue W107–W113

Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying organismal development, physiology and disease. However, current miR–mRNA target prediction platforms rank miR targets based on estimated strength of physical interactions and lack the ability to rank interactants as a function of their potential to impact a given biological system. To address this, we have developed ToppMiR -- http://toppmir.cchmc.org -- a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a userspecified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape.

Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer.
Uhlmann S, Mannsperger H, Zhang JD, Horvat EÁ, Schmidt C, Küblbeck M, Henjes F, Ward A, Tschulena U, Zweig K, Korf U, Wiemann S, Sahin O.
Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
Mol Syst Biol. 2012 8: 570

The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.

A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans.
Jovanovic M, Reiter L, Picotti P, Lange V, Bogan E, Hurschler BA, Blenkiron C, Lehrbach NJ, Ding XC, Weiss M, Schrimpf SP, Miska EA, Grosshans H, Aebersold R, Hengartner MO.
Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
Nat Methods. 2010 Oct;7(10): 837-842

Efficient experimental strategies are needed to validate computationally predicted microRNA (miRNA) target genes. Here we present a large-scale targeted proteomics approach to validate predicted miRNA targets in Caenorhabditis elegans. Using selected reaction monitoring (SRM), we quantified 161 proteins of interest in extracts from wild-type and let-7 mutant worms. We demonstrate by independent experimental downstream analyses such as genetic interaction, as well as polysomal profiling and luciferase assays, that validation by targeted proteomics substantially enriched for biologically relevant let-7 interactors. For example, we found that the zinc finger protein ZTF-7 was a bona fide let-7 miRNA target. We also validated predicted miR-58 targets, demonstrating that this approach is adaptable to other miRNAs. We propose that targeted mass spectrometry can be applied generally to validate candidate lists generated by computational methods or in large-scale experiments, and that the described strategy should be readily adaptable to other organisms.

Systematic transcriptome wide analysis of lncRNA-miRNA interactions.
Jalali S, Bhartiya D, Lalwani MK, Sivasubbu S, Scaria V.
GN Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
PLoS One. 2013;8(2): e53823

BACKGROUND: Long noncoding RNAs (lncRNAs) are a recently discovered class of non-protein coding RNAs, which have now increasingly been shown to be involved in a wide variety of biological processes as regulatory molecules. The functional role of many of the members of this class has been an enigma, except a few of them like Malat and HOTAIR. Little is known regarding the regulatory interactions between noncoding RNA classes. Recent reports have suggested that lncRNAs could potentially interact with other classes of non-coding RNAs including microRNAs (miRNAs) and modulate their regulatory role through interactions. We hypothesized that lncRNAs could participate as a layer of regulatory interactions with miRNAs. The availability of genome-scale datasets for Argonaute targets across human transcriptome has prompted us to reconstruct a genome-scale network of interactions between miRNAs and lncRNAs.
RESULTS: We used well characterized experimental Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) datasets and the recent genome-wide annotations for lncRNAs in public domain to construct a comprehensive transcriptome-wide map of miRNA regulatory elements. Comparative analysis revealed that in addition to targeting protein-coding transcripts, miRNAs could also potentially target lncRNAs, thus participating in a novel layer of regulatory interactions between noncoding RNA classes. Furthermore, we have modeled one example of miRNA-lncRNA interaction using a zebrafish model. We have also found that the miRNA regulatory elements have a positional preference, clustering towards the mid regions and 3' ends of the long noncoding transcripts. We also further reconstruct a genome-wide map of miRNA interactions with lncRNAs as well as messenger RNAs.
CONCLUSIONS: This analysis suggests widespread regulatory interactions between noncoding RNAs classes and suggests a novel functional role for lncRNAs. We also present the first transcriptome scale study on miRNA-lncRNA interactions and the first report of a genome-scale reconstruction of a noncoding RNA regulatory interactome involving lncRNAs.

Integrative Analysis of miRNA and inflammatory gene expression after acute particulate matter exposure.
Motta V, Angelici L, Nordio F, Bollati V, Fossati S, Frascati F, Tinaglia V, Bertazzi PA, Battaglia C, Baccarelli AA.
Exposure, Epidemiology and Risk Program, Department of Environmental Health, Laboratory of Environmental Epigenetics, Harvard School of Public Health, Boston, Massachusetts 02115, USA
Toxicol Sci. 2013 132(2): 307-316

MicroRNAs (miRNAs) are environmentally sensitive inhibitors of gene expression that may mediate the effects of metal-rich particulate matter (PM) and toxic metals on human individuals. Previous environmental miRNA studies have investigated a limited number of candidate miRNAs and have not yet evaluated the functional effects on gene expression. In this study, we wanted to identify PM-sensitive miRNAs using microarray profiling on matched baseline and postexposure RNA from foundry workers with well-characterized exposure to metal-rich PM and to characterize miRNA relations with expression of candidate inflammatory genes. We applied microarray analysis of 847 human miRNAs and real-time PCR analysis of 18 candidate inflammatory genes on matched blood samples collected from foundry workers at baseline and after 3 days of work (postexposure). We identified differentially expressed miRNAs (fold change [FC] > 2 and p < 0.05) and correlated their expression with the inflammatory associated genes. We performed in silico network analysis in MetaCore v6.9 to characterize the biological pathways connecting miRNA-mRNA pairs. Microarray analysis identified four miRNAs that were differentially expressed in postexposure compared with baseline samples, including miR-421 (FC = 2.81, p < 0.001), miR-146a (FC = 2.62, p = 0.007), miR-29a (FC = 2.91, p < 0.001), and let-7g (FC = 2.73, p = 0.019). Using false discovery date adjustment for multiple comparisons, we found 11 miRNA-mRNA correlated pairs involving the 4 differentially expressed miRNAs and candidate inflammatory genes. In silico network analysis with MetaCore database identified biological interactions for all the 11 miRNA-mRNA pairs, which ranged from direct mRNA targeting to complex interactions with multiple intermediates. Acute PM exposure may affect gene regulation through PM-responsive miRNAs that directly or indirectly control inflammatory gene expression.

An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells.
Nam S, Long X, Kwon C, Kim S, Nephew KP.
Cancer Genomics Branch, National Cancer Center, Goyang-si, Gyeonggi-do, 410-769, Korea
BMC Genomics. 2012 ;13: 732

BACKGROUND: A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs) into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines) from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an "integrative network". We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity.
RESULTS: Based on the integrative network, we extracted "substructures" (network clusters) representing the drug resistant states (tamoxifen- or fulvestrant-resistance cells) compared to drug sensitive state (parental MCF7 cells). We identified un-described network clusters that contribute to antiestrogen resistance consisting of miR-146a, -27a, -145, -21, -155, -15a, -125b, and let-7s, in addition to the previously described miR-221/222.
CONCLUSIONS: By integrating miRNA-related network, gene/miRNA expression and text-mining, the current study provides a computational-based systems biology approach for further investigating the molecular mechanism underlying antiestrogen resistance in breast cancer cells. In addition, new miRNA clusters that contribute to antiestrogen resistance were identified, and they warrant further investigation.


microRNA dysregulation in prostate cancer: network analysis reveals preferential regulation of highly connected nodes.
Budd WT, Weaver DE, Anderson J, Zehner ZE.
Doctoral Program in Integrative Life Science, Virginia Commonwealth University, P.O. Box 842030, Richmond, VA 23284, USA.
Chem Biodivers. 2012 May;9(5): 857-867

microRNAs (miRNAs) are small RNAs shown to contribute to a number of cellular processes including cell growth, differentiation, and apoptosis. MiRNAs regulate gene expression of their targets post-transcriptionally by binding to messenger RNA (mRNA), causing translational inhibition or mRNA degradation. Dysregulation of miRNA expression can promote cancer formation and progression. Research has largely focused on the function and expression of single miRNAs. However, complex physiological processes require the interaction, regulation and coordination of many molecules including miRNAs and proteins. Highly connected molecules often serve important roles in the cell. A protein-protein interaction network of established miRNA targets confirmed these proteins to be highly connected and essential to the cell, affecting tumorigenesis, cell growth/proliferation, cellular death, cell assembly, and maintenance pathways. This analysis showed that miRNAs contribute to the overall health of the prostate, and their aberrant expression destabilized homeostatic balance. This integrative network approach can reveal important miRNAs and proteins in prostate cancer that will be useful to identify specific disease biomarkers, which may be used as targets for therapeutics or drugs in themselves.


Integrative analysis of gene and miRNA expression profiles with transcription factor-miRNA feed-forward loops identifies regulators in human cancers.
Yan Z, Shah PK, Amin SB, Samur MK, Huang N, Wang X, Misra V, Ji H, Gabuzda D, Li C.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA.
Nucleic Acids Res. 2012 Sep 1;40(17): e135

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF-target gene and miRNA-mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi

Bioinformatics Prediction and Experimental Validation of MicroRNAs Involved in Cross-Kingdom Interaction.
Pirrò S, Minutolo A, Galgani A, Potestà M, Colizzi V, Montesano C
J Comput Biol. 2016 Jul 18.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that act as efficient post-transcriptional regulators of gene expression. In 2012, the first cross-kingdom miRNA-based interaction had been evidenced, demonstrating that exogenous miRNAs act in a manner of mammalian functional miRNAs. Starting from this evidence, we defined the concept of cross-kingdom functional homology between plant and mammalian miRNAs as a needful requirement for vegetal miRNA to explicit a regulation mechanism into the host mammalian cell, comparable to the endogenous one. Then, we proposed a new dedicated algorithm to compare plant and mammalian miRNAs, searching for functional sequence homologies between them, and we developed a web software called MirCompare. We also predicted human genes regulated by the selected plant miRNAs, and we determined the role of exogenous miRNAs in the perturbation of intracellular interaction networks. Finally, as already performed by Pirrò and coworkers, the ability of MirCompare to select plant miRNAs with functional homologies with mammalian ones has been experimentally confirmed by evaluating the ability of mol-miR168a to downregulate the protein expression of SIRT1, when its mimic is transfected into human hepatoma cell line G2 (HEPG2) cells.
This tool is implemented into a user-friendly web interface, and the access is free to public through the website  http://160.80.35.140/MirCompare


Tools for integrated analysis


Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as RNA-Seq significantly increased the number of known miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNAs is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorithms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and in this review, the authors aim to summarize several commonly used tools in metazoan miRNA discovery.

Selected computational tools for miRNA prediction and their main characteristics
Tool Website Year
miRscan genes.mit.edu/mirscan 2003
miRSeeker 2003
miRAlign bioinfo.au.tsinghua.edu.cn/miralign 2005
Phylogenetic shadowing 2005
ProMiR bi.snu.ac.kr/ProMiR 2005
Triplet-SVM bioinfo.au.tsinghua.edu.cn/software/mirnasvm 2005
miR-abela www.mirz.unibas.ch/cgi/pred_miRNA_genes.cgi 2005
RNAmicro www.bioinf.uni-leipzig.de/~jana/index.php/jana-hertel-software/65-jana-hertel-rnamicro 2006
miRFinder www.bioinformatics.org/mirfinder 2007
miPred www.bioinf.seu.edu.cn/miRNA 2007
MiRRim www.ncrna.org/software/miRRim 2007
miRDeep www.mdc-berlin.de/en/research/research_teams/systems_biology_of_gene_regulatory_elements/projects/miRDeep 2008
miRanalyzer web.bioinformatics.cicbiogune.es/microRNA/miRanalyser.php 2009
SSCprofiler mirna.imbb.forth.gr/SSCprofiler.html 2009
HHMMiR http://www.benoslab.pitt.edu/kadriAPBC2009.html 2009
  • Gomes CP, Cho JH, Hood L, Franco OL, Pereira RW, Wang K. (2013)  A Review of Computational Tools in microRNA Discovery.Front Genet 4, 81.


A Review of Computational Tools in microRNA Discovery
Clarissa P. C. Gomes, Ji-Hoon Cho, Leroy Hood, Octávio L. Franco, Rinaldo W. Pereira, and Kai Wang
Front Genet. 2013; 4: 81.

Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as next-generation sequencing significantly increased the number of known miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNAs is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorithms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and in this review, we aim to summarize several commonly used tools in metazoan miRNA discovery.

Quantifying the strength of miRNA-target interactions.
Breda J, Rzepiela AJ, Gumienny R, van Nimwegen E, Zavolan M
Methods. 2015 Sep 1;85: 90-99

We quantify the strength of miRNA-target interactions with MIRZA, a recently introduced biophysical model. We show that computationally predicted energies of interaction correlate strongly with the energies of interaction estimated from biochemical measurements of Michaelis-Menten constants. We further show that the accuracy of the MIRZA model can be improved taking into account recently emerged experimental data types. In particular, we use chimeric miRNA-mRNA sequences to infer a MIRZA-CHIMERA model and we provide a framework for inferring a similar model from measurements of rate constants of miRNA-mRNA interaction in the context of Argonaute proteins. Finally, based on a simple model of miRNA-based regulation, we discuss the importance of interaction energy and its variability between targets for the modulation of miRNA target expression in vivo.


miRviewer: A multispecies microRNA homologous viewer
Adam Kiezun, Shay Artzi, Shira Modai, Naama Volk, Ofer Isakov and Noam Shomron
BMC Research Notes 2012, 5:92
miRviewer is available at: http://people.csail.mit.edu/akiezun/miRviewer

Background - MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression via binding to the 3' ends of mRNAs. MiRNAs have been associated with many cellular events ascertaining their central role in gene regulation. In order to better understand miRNAs of interest it is of utmost importance to learn about the genomic conservation of these genes. Findings The miRviewer web-server, presented here, encompasses all known (and some novel) miRNAs of currently fully annotated animal genomes in a visual 'birds-eye' view representation. miRviewer provides a graphical outlook of the current miRNA world together with sequence alignments and secondary structures of each miRNA. As a test case we experimentally examined the expression of several miRNAs in various animals.
Conclusions - miRviewer completes the homologous miRNA space with hundreds of unreported miRNAs and is available at: http://people.csail.mit.edu/akiezun/miRviewer

miRvar: A comprehensive database for genomic variations in microRNAs
Deeksha Bhartiya, Saurabh V. Laddha, Arijit Mukhopadhyay, Vinod Scaria*
Human Mutation, Volume 32, Issue 6, pages E2226–E2245, June 2011
The data is made available on the Leiden Open source Variation Database platform (LOVD) at   http://genome.igib.res.in/mirlovd

microRNAs are a recently discovered and well studied class of small noncoding functional RNAs. The regulatory role of microRNAs (miRNAs) has been well studied in a wide variety of biological processes but there have been no systematic effort to understand and analyze the genetic variations in miRNA loci and study its functional consequences. We have comprehensively curated genetic variations in miRNA loci in the human genome and established a computational pipeline to assess potential functional consequences of these variants along with methods for systematic curation and reporting of variations in these loci. The data is made available on the Leiden Open (source) Variation Database (LOVD) platform at http://genome.igib.res.in/mirlovd  to provide ease of aggregation and analysis and is open for community curation efforts.

MicroRNA target site identification by integrating sequence and binding information
Majoros WH, Lekprasert P, Mukherjee N, Skalsky RL, Corcoran DL, Cullen BR, Ohler U.
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, USA
Nat Methods. 2013 May 26. doi: 10.1038/nmeth.2489

High-throughput sequencing has opened numerous possibilities for the identification of regulatory RNA-binding events. Cross-linking and immunoprecipitation of Argonaute proteins can pinpoint a microRNA (miRNA) target site within tens of bases but leaves the identity of the miRNA unresolved. A flexible computational framework, microMUMMIE, integrates sequence with cross-linking features and reliably identifies the miRNA family involved in each binding event. It considerably outperforms sequence-only approaches and quantifies the prevalence of noncanonical binding modes
.

MAGIA, a web-based tool for miRNA and Genes Integrated Analysis
Sales G, Coppe A, Bisognin A, Biasiolo M, Bortoluzzi S, Romualdi C.
Department of Biology, University of Padua, via U. Bassi 58/B, 35121 Padova, Italy.
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W352-9. Epub 2010 May 19.
MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia

MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia



The microRNA body map: dissecting microRNA function through integrative genomics
Mestdagh P, Lefever S, Pattyn F, Ridzon D, Fredlund E, Fieuw A, Ongenaert M, Vermeulen J, De Paepe A, Wong L, Speleman F, Chen C, Vandesompele J.
Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium and Life Technologies, Foster City, CA, USA
Nucleic Acids Res. 2011 Nov 1;39(20):e136

While a growing body of evidence implicates regulatory miRNA modules in various aspects of human disease and development, insights into specific miRNA function remain limited. Here, we present an innovative approach to elucidate tissue-specific miRNA functions that goes beyond miRNA target prediction and expression correlation. This approach is based on a multi-level integration of corresponding miRNA and mRNA gene expression levels, miRNA target prediction, transcription factor target prediction and mechanistic models of gene network regulation. Predicted miRNA functions were either validated experimentally or compared to published data. The predicted miRNA functions are accessible in the miRNA bodymap, an interactive online compendium and mining tool of high-dimensional newly generated and published miRNA expression profiles. The miRNA bodymap enables prioritization of candidate miRNAs based on their expression pattern or functional annotation across tissue or disease subgroup. The miRNA bodymap project provides users with a single one-stop data-mining solution and has great potential to become a community resource.

mirConnX: condition-specific mRNA-microRNA network integrator
Huang GT, Athanassiou C, Benos PV.
Joint CMU-Pitt PhD Program in Computational Biology, Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Nucleic Acids Res. 2011 Web Server issue: W416-23
mirConnX is freely available for academic use at http://www.benoslab.pitt.edu/mirconnx

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. Visualization of the network and subsequent analysis are provided via a very responsive graphic user interface. Two organisms are currently supported: Homo sapiens and Mus musculus. The intuitive user interface and large database make mirConnX a useful tool for clinical scientists for hypothesis generation and explorations. mirConnX is freely available for academic use at http://www.benoslab.pitt.edu/mirconnx



miRGator: an integrated system for functional annotation of microRNAs
Nam S, Kim B, Shin S, Lee S.
Division of Life and Pharmaceutical Sciences, Ewha Womans University, Seoul, Korea
Nucleic Acids Res. 2008 36(Database issue): D159-64
miRGator, available at: http://genome.ewha.ac.kr/miRGator/   http://mirgator.kobic.re.kr:8080/MEXWebApp/


MicroRNAs (miRNAs) constitute an important class of regulators that are involved in various cellular and disease processes. However, the functional significance of each miRNA is mostly unknown due to the difficulty in identifying target genes and the lack of genome-wide expression data combining miRNAs, mRNAs and proteins. We introduce a novel database, miRGator, that integrates the target prediction, functional analysis, gene expression data and genome annotation. MiRNA function is inferred from the list of target genes predicted by miRanda, PicTar and TargetScanS programs. Statistical enrichment test of target genes in each term is performed for gene ontology, pathway and disease annotations. Associated terms may provide valuable insights for the function of each miRNA. For the expression analysis, miRGator integrates public expression data of miRNA with those of mRNA and protein. Expression correlation between miRNA and target mRNA/proteins is evaluated and their expression patterns can be readily compared Our web implementation supports diverse query types including miRNA name, gene symbol, gene ontology, pathway and disease terms. Interfaces for exploring common targets or regulatory miRNAs and for profiling compendium expression data have been developed as well. Currently, miRGator, available at: http://genome.ewha.ac.kr/miRGator/ supports the human and mouse genomes.

miRGator v2.0: an integrated system for functional investigation of microRNAs
Cho S, Jun Y, Lee S, Choi HS, Jung S, Jang Y, Park C, Kim S, Lee S, Kim W.
Ewha Research Center for Systems Biology, Ewha Womans University, 11-1 Daehyun-dong, Seodaemun-gu, Seoul 120-750, Korea
Nucleic Acids Res. 2011 Jan;39(Database issue): D158-162
http://miRGator.kobic.re.kr

miRGator is an integrated database of microRNA (miRNA)-associated gene expression, target prediction, disease association and genomic annotation, which aims to facilitate functional investigation of miRNAs. The recent version of miRGator v2.0 contains information about (i) human miRNA expression profiles under various experimental conditions, (ii) paired expression profiles of both mRNAs and miRNAs, (iii) gene expression profiles under miRNA-perturbation (e.g. miRNA knockout and overexpression), (iv) known/predicted miRNA targets and (v) miRNA-disease associations. In total, >8000 miRNA expression profiles, ∼300 miRNA-perturbed gene expression profiles and ∼2000 mRNA expression profiles are compiled with manually curated annotations on disease, tissue type and perturbation. By integrating these data sets, a series of novel associations (miRNA-miRNA, miRNA-disease and miRNA-target) is extracted via shared features. For example, differentially expressed genes (DEGs) after miRNA knockout were systematically compared against miRNA targets. Likewise, differentially expressed miRNAs (DEmiRs) were compared with disease-associated miRNAs. Additionally, miRNA expression and disease-phenotype profiles revealed miRNA pairs whose expression was regulated in parallel in various experimental and disease conditions. Complex associations are readily accessible using an interactive network visualization interface. The miRGator v2.0 serves as a reference database to investigate miRNA expression and function http://miRGator.kobic.re.kr

Identification of microRNA-regulated gene networks by expression analysis of target genes
Vincenzo A Gennarino1, Giovanni D'Angelo1, Gopuraja Dharmalingam1, Serena Fernandez2, Giorgio Russolillo3, Remo Sanges4, Margherita Mutarelli1, Vincenzo Belcastro1, Andrea Ballabio5, Pasquale Verde2, Marco Sardiello1 and Sandro Banfi1,6
Genome Res. 2012. Published in Advance February 15, 2012

MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene co-targeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances, TGFβ signalling and its effects on cell proliferation, morphology and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.


miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes
Hsu PW, Huang HD, Hsu SD, Lin LZ, Tsou AP, Tseng CP, Stadler PF, Washietl S, Hofacker IL.
Nucleic Acids Res. 2006 Jan 1;34(Database issue): D135-139.
Institute of Bioinformatics, National Chiao Tung University, Hsin-Chu 300, Taiwan, ROC.

Recent work has demonstrated that microRNAs (miRNAs) are involved in critical biological processes by suppressing the translation of coding genes. This work develops an integrated database, miRNAMap, to store the known miRNA genes, the putative miRNA genes, the known miRNA targets and the putative miRNA targets. The known miRNA genes in four mammalian genomes such as human, mouse, rat and dog are obtained from miRBase, and experimentally validated miRNA targets are identified in a survey of the literature. Putative miRNA precursors were identified by RNAz, which is a non-coding RNA prediction tool based on comparative sequence analysis. The mature miRNA of the putative miRNA genes is accurately determined using a machine learning approach, mmiRNA. Then, miRanda was applied to predict the miRNA targets within the conserved regions in 3'-UTR of the genes in the four mammalian genomes. The miRNAMap also provides the expression profiles of the known miRNAs, cross-species comparisons, gene annotations and cross-links to other biological databases. Both textual and graphical web interface are provided to facilitate the retrieval of data from the miRNAMap. The database is freely available at http://mirnamap.mbc.nctu.edu.tw

miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes
Hsu SD, Chu CH, Tsou AP, Chen SJ, Chen HC, Hsu PW, Wong YH, Chen YH, Chen GH, Huang HD.
Institute of Bioinformatics, National Chiao Tung University, Hsin-Chu 300, Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan
Nucleic Acids Res. 2008 Jan;36(Database issue): D165-169
The miRNAMap 2.0 is now available at http://miRNAMap.mbc.nctu.edu.tw

MicroRNAs (miRNAs) are small non-coding RNA molecules that can negatively regulate gene expression and thus control numerous cellular mechanisms. This work develops a resource, miRNAMap 2.0, for collecting experimentally verified microRNAs and experimentally verified miRNA target genes in human, mouse, rat and other metazoan genomes. Three computational tools, miRanda, RNAhybrid and TargetScan, were employed to identify miRNA targets in 3'-UTR of genes as well as the known miRNA targets. Various criteria for filtering the putative miRNA targets are applied to reduce the false positive prediction rate of miRNA target sites. Additionally, miRNA expression profiles can provide valuable clues on the characteristics of miRNAs, including tissue specificity and differential expression in cancer/normal cell. Therefore, quantitative polymerase chain reaction experiments were performed to monitor the expression profiles of 224 human miRNAs in 18 major normal tissues in human. The negative correlation between the miRNA expression profile and the expression profiles of its target genes typically helps to elucidate the regulatory functions of the miRNA. The interface is also redesigned and enhanced. The miRNAMap 2.0 is now available at http://miRNAMap.mbc.nctu.edu.tw

miRecords: an integrated resource for microRNA-target interactions
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T.
Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
Nucleic Acids Res. 2009 Jan;37(Database issue): D105-110
The miRecords is available at http://miRecords.umn.edu/miRecords

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes' expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA-target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA-target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA-target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords

MicroTar: predicting microRNA targets from RNA duplexes
Thadani R, Tammi MT.
BMC Bioinformatics. 2006 Suppl 5: S20
Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543

BACKGROUND: The accurate prediction of a comprehensive set of messenger RNAs (targets) regulated by animal microRNAs (miRNAs) remains an open problem. In particular, the prediction of targets that do not possess evolutionarily conserved complementarity to their miRNA regulators is not adequately addressed by current tools.
RESULTS: We have developed MicroTar, an animal miRNA target prediction tool based on miRNA-target complementarity and thermodynamic data. The algorithm uses predicted free energies of unbound mRNA and putative mRNA-miRNA heterodimers, implicitly addressing the accessibility of the mRNA 3' untranslated region. MicroTar does not rely on evolutionary conservation to discern functional targets, and is able to predict both conserved and non-conserved targets. MicroTar source code and predictions are accessible at http://tiger.dbs.nus.edu.sg/microtar/, where both serial and parallel versions of the program can be downloaded under an open-source licence.


miRTarBase: a database curates experimentally validated microRNA-target interactions
Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu MC, Huang CY, Tsou AP, Huang HD.
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Nucleic Acids Res. 2011 39(Database issue): D163-169
miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw

MicroRNAs (miRNAs), i.e. small non-coding RNA molecules (22 nt), can bind to one or more target sites on a gene transcript to negatively regulate protein expression, subsequently controlling many cellular mechanisms. A current and curated collection of miRNA-target interactions (MTIs) with experimental support is essential to thoroughly elucidating miRNA functions under different conditions and in different species. As a database, miRTarBase has accumulated more than 3500 MTIs by manually surveying pertinent literature after data mining of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assays, western blot, or microarray experiments with overexpression or knockdown of miRNAs. miRTarBase curates 3576 experimentally verified MTIs between 657 miRNAs and 2297 target genes among 17 species. miRTarBase contains the largest amount of validated MTIs by comparing with other similar, previously developed databases. The MTIs collected in the miRTarBase can also provide a large amount of positive samples to develop computational methods capable of identifying miRNA-target interactions. miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw/ and is updated frequently by continuously surveying research articles.




DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows
Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG.
Nucleic Acids Res. 2013 May 16
DIANA-Lab, Biomedical Sciences Research Center 'Alexander Fleming', 16672 Vari, Greece, Department of Computer and Communication Engineering, University of Thessaly, 382 21 Volos, Greece, IMIS Institute, 'Athena' Research Center, 11524 Athens, Greece, Laboratory for Experimental Surgery and Surgical Research 'N.S. Christeas', Medical School of Athens, University of Athens, 11527 Athens, Greece and National Center for Scientific Research DEMOKRITOS, Institute of Nuclear and Particle Physics, 15310 Aghia Paraskevi, Greece.

MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server (http://www.microrna.gr/webServer) is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANA-microT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA-gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANA-microT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.

DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association
Maragkakis M, Vergoulis T, Alexiou P, Reczko M, Plomaritou K, Gousis M, Kourtis K, Koziris N, Dalamagas T, Hatzigeorgiou AG.
Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 16672, Vari, Greece.
Nucleic Acids Res. 2011 39(Web Server issue): W145-148
The Web server is publicly accessible in http://www.microrna.gr/microT-v4

microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in http://www.microrna.gr/microT-v4

The DIANA-mirExTra web server: from gene expression data to microRNA function
Alexiou P, Maragkakis M, Papadopoulos GL, Simmosis VA, Zhang L, Hatzigeorgiou AG.
Biomedical Sciences Research Center "Alexander Fleming", Institute of Molecular Oncology, Varkiza, Greece.
PLoS One. 2010 Feb 11;5(2):e9171
Here, we introduce a user-friendly web-server, DIANA-mirExTra http://www.microrna.gr/mirextra

BACKGROUND: High-throughput gene expression experiments are widely used to identify the role of genes involved in biological conditions of interest. MicroRNAs (miRNA) are regulatory molecules that have been functionally associated with several developmental programs and their deregulation with diverse diseases including cancer.
METHODOLOGY/PRINCIPAL FINDINGS: Although miRNA expression levels may not be routinely measured in high-throughput experiments, a possible involvement of miRNAs in the deregulation of gene expression can be computationally predicted and quantified through analysis of overrepresented motifs in the deregulated genes 3' untranslated region (3'UTR) sequences. Here, we introduce a user-friendly web-server, DIANA-mirExTra http://www.microrna.gr/mirextra that allows the comparison of frequencies of miRNA associated motifs between sets of genes that can lead to the identification of miRNAs responsible for the deregulation of large numbers of genes. To this end, we have investigated different approaches and measures, and have practically implemented them on experimental data.
CONCLUSIONS/SIGNIFICANCE: On several datasets of miRNA overexpression and repression experiments, our proposed approaches have successfully identified the deregulated miRNA. Beyond the prediction of miRNAs responsible for the deregulation of transcripts, the web-server provides extensive links to DIANA-mirPath, a functional analysis tool incorporating miRNA targets in biological pathways. Additionally, in case information about miRNA expression changes is provided, the results can be filtered to display the analysis for miRNAs of interest only.

DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways
Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T, Hatzigeorgiou AG.
Nucleic Acids Res. 2012 Jul 1;40(W1):W498-W504

MicroRNAs (miRNAs) are key regulators of diverse biological processes and their functional analysis has been deemed central in many research pipelines. The new version of DIANA-miRPath web server was redesigned from the ground-up. The user of DNA Intelligent Analysis (DIANA) DIANA-miRPath v2.0 can now utilize miRNA targets predicted with high accuracy based on DIANA-microT-CDS and/or experimentally verified targets from TarBase v6; combine results with merging and meta-analysis algorithms; perform hierarchical clustering of miRNAs and pathways based on their interaction levels; as well as elaborate sophisticated visualizations, such as dendrograms or miRNA versus pathway heat maps, from an intuitive and easy to use web interface. New modules enable DIANA-miRPath server to provide information regarding pathogenic single nucleotide polymorphisms (SNPs) in miRNA target sites (SNPs module) or to annotate all the predicted and experimentally validated miRNA targets in a selected molecular pathway (Reverse Search module). DIANA-miRPath v2.0 is an efficient and yet easy to use tool that can be incorporated successfully into miRNA-related analysis pipelines. It provides for the first time a series of highly specific tools for miRNA-targeted pathway analysis via a web interface and can be accessed at http://www.microrna.gr/miRPathv2.

MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression
Nam S, Li M, Choi K, Balch C, Kim S, Nephew KP.
Medical Sciences Program, Indiana University School of Medicine, Indianapolis, IN, USA.
Nucleic Acids Res. 2009 Jul;37(Web Server issue): W356-362
The novel web server http://cancer.informatics.indiana.edu/mmia

MicroRNAs (miRNAs) are small (19-24 nt), nonprotein-coding nucleic acids that regulate specific 'target' gene products via hybridization to mRNA transcripts, resulting in translational blockade or transcript degradation. Although miRNAs have been implicated in numerous developmental and adult diseases, their specific impact on biological pathways and cellular phenotypes, in addition to miRNA gene promoter regulation, remain largely unknown. To improve and facilitate research of miRNA functions and regulation, we have developed MMIA (microRNA and mRNA integrated analysis), a versatile and user-friendly web server. By incorporating three commonly used and accurate miRNA prediction algorithms, TargetScan, PITA and PicTar, MMIA integrates miRNA and mRNA expression data with predicted miRNA target information for analyzing miRNA-associated phenotypes and biological functions by gene set analysis, in addition to analysis of miRNA primary transcript gene promoters. To assign biological relevance to the integrated miRNA/mRNA profiles, MMIA uses exhaustive human genome coverage, including classification into various disease-associated genes as well as conventional canonical pathways and Gene Ontology. In summary, this novel web server http://cancer.informatics.indiana.edu/mmia  will provide life science researchers with a valuable tool for the study of the biological (and pathological) causes and effects of the expression of this class of interesting protein regulators.

mESAdb: microRNA expression and sequence analysis database
Kaya KD, Karakülah G, Yakicier CM, Acar AC, Konu O.
Department of Molecular Biology and Genetics, Bilkent University, 06800 Ankara, Turkey
Nucleic Acids Res. 2011 Jan;39(Database issue): D170-180
microRNA expression and sequence analysis database http://konulab.fen.bilkent.edu.tr/mirna/

microRNA expression and sequence analysis database http://konulab.fen.bilkent.edu.tr/mirna/ (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.

A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
Zhang S, Li Q, Liu J, Zhou XJ.
Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA
Bioinformatics. 2011 Jul 1;27(13): i401-409
The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/

MOTIVATION: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA-gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes.
RESULTS: Here we propose an effective data integration framework to identify the miRNA-gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA-gene interactions, and gene-gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics.
AVAILABILITY: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/

RNAhybrid: microRNA target prediction easy, fast and flexible
Krüger J, Rehmsmeier M.
Center for Biotechnology, CeBiTec, Universität Bielefeld, 33594 Bielefeld, Germany
Nucleic Acids Res. 2006 Jul 1;34(Web Server issue): W451-454
RNAhybrid is available at http://bibiserv.techfak.uni-bielefeld.de/rnahybrid

In the elucidation of the microRNA regulatory network, knowledge of potential targets is of highest importance. Among existing target prediction methods, RNAhybrid [M. Rehmsmeier, P. Steffen, M. Höchsmann and R. Giegerich (2004) RNA, 10, 1507-1517] is unique in offering a flexible online prediction. Recently, some useful features have been added, among these the possibility to disallow G:U base pairs in the seed region, and a seed-match speed-up, which accelerates the program by a factor of 8. In addition, the program can now be used as a webservice for remote calls from user-implemented programs. We demonstrate RNAhybrid's flexibility with the prediction of a non-canonical target site for Caenorhabditis elegans miR-241 in the 3'-untranslated region of lin-39. RNAhybrid is available at http://bibiserv.techfak.uni-bielefeld.de/rnahybrid

MirZ: an integrated microRNA expression atlas and target prediction resource
Hausser J, Berninger P, Rodak C, Jantscher Y, Wirth S, Zavolan M.
Biozentrum, Universität Basel and Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland
Nucleic Acids Res. 2009 Jul;37(Web Server issue): W266-272
MirZ web server is accessible at http://www.mirz.unibas.ch

MicroRNAs (miRNAs) are short RNAs that act as guides for the degradation and translational repression of protein-coding mRNAs. A large body of work showed that miRNAs are involved in the regulation of a broad range of biological functions, from development to cardiac and immune system function, to metabolism, to cancer. For most of the over 500 miRNAs that are encoded in the human genome the functions still remain to be uncovered. Identifying miRNAs whose expression changes between cell types or between normal and pathological conditions is an important step towards characterizing their function as is the prediction of mRNAs that could be targeted by these miRNAs. To provide the community the possibility of exploring interactively miRNA expression patterns and the candidate targets of miRNAs in an integrated environment, we developed the MirZ web server, which is accessible at http://www.mirz.unibas.ch The server provides experimental and computational biologists with statistical analysis and data mining tools operating on up-to-date databases of sequencing-based miRNA expression profiles and of predicted miRNA target sites in species ranging from Caenorhabditis elegans to Homo sapiens.

Identifying transcriptional start sites of human microRNAs based on high-throughput sequencing data
Chien CH, Sun YM, Chang WC, Chiang-Hsieh PY, Lee TY, Tsai WC, Horng JT, Tsou AP, Huang HD.
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Nucleic Acids Res. 2011 Nov;39(21): 9345-9356

MicroRNAs (miRNAs) are critical small non-coding RNAs that regulate gene expression by hybridizing to the 3'-untranslated regions (3'-UTR) of target mRNAs, subsequently controlling diverse biological processes at post-transcriptional level. How miRNA genes are regulated receives considerable attention because it directly affects miRNA-mediated gene regulatory networks. Although numerous prediction models were developed for identifying miRNA promoters or transcriptional start sites (TSSs), most of them lack experimental validation and are inadequate to elucidate relationships between miRNA genes and transcription factors (TFs). Here, we integrate three experimental datasets, including cap analysis of gene expression (CAGE) tags, TSS Seq libraries and H3K4me3 chromatin signature derived from high-throughput sequencing analysis of gene initiation, to provide direct evidence of miRNA TSSs, thus establishing an experimental-based resource of human miRNA TSSs, named miRStart. Moreover, a machine-learning-based Support Vector Machine (SVM) model is developed to systematically identify representative TSSs for each miRNA gene. Finally, to demonstrate the effectiveness of the proposed resource, an important human intergenic miRNA, hsa-miR-122, is selected to experimentally validate putative TSS owing to its high expression in a normal liver. In conclusion, this work successfully identified 847 human miRNA TSSs (292 of them are clustered to 70 TSSs of miRNA clusters) based on the utilization of high-throughput sequencing data from TSS-relevant experiments, and establish a valuable resource for biologists in advanced research in miRNA-mediated regulatory networks.

PolymiRTS Database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits
Jesse D. Ziebarth1,2, Anindya Bhattacharya1,2, Anlong Chen1,2,3 and Yan Cui1,2,*
1Department of Microbiology, Immunology and Biochemistry, 2Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN 38163, USA and 3College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Nucl. Acids Res. (2012) 40 (D1): D216-D221
The PolymiRTS database is available at http://compbio.uthsc.edu/miRSNP/

The polymorphism in microRNA target site (PolymiRTS) database aims to identify single-nucleotide polymorphisms (SNPs) that affect miRNA targeting in human and mouse. These polymorphisms can disrupt the regulation of gene expression by miRNAs and are candidate genetic variants responsible for transcriptional and phenotypic variation. The database is therefore organized to provide links between SNPs in miRNA target sites, cis-acting expression quantitative trait loci (eQTLs), and the results of genome-wide association studies (GWAS) of human diseases. Here, we describe new features that have been integrated in the PolymiRTS database, including: (i) polymiRTSs in genes associated with human diseases and traits in GWAS, (ii) polymorphisms in target sites that have been supported by a variety of experimental methods and (iii) polymorphisms in miRNA seed regions. A large number of newly identified microRNAs and SNPs, recently published mouse phenotypes, and human and mouse eQTLs have also been integrated into the database. The PolymiRTS database is available at http://compbio.uthsc.edu/miRSNP/

miRNEST database: an integrative approach in microRNA search and annotation
Michał Wojciech Szcześniak1,*, Sebastian Deorowicz2, Jakub Gapski1, Łukasz Kaczyński1 and Izabela Makałowska1,*
1Laboratory of Bioinformatics, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, 61-614 Poznan and 2Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Nucl. Acids Res. (2012) 40 (D1): D198-D204

Despite accumulating data on animal and plant microRNAs and their functions, existing public miRNA resources usually collect miRNAs from a very limited number of species. A lot of microRNAs, including those from model organisms, remain undiscovered. As a result there is a continuous need to search for new microRNAs. We present miRNEST (http://mirnest.amu.edu.pl), a comprehensive database of animal, plant and virus microRNAs. The core part of the database is built from our miRNA predictions conducted on Expressed Sequence Tags of 225 animal and 202 plant species. The miRNA search was performed based on sequence similarity and as many as 10 004 miRNA candidates in 221 animal and 199 plant species were discovered. Out of them only 299 have already been deposited in miRBase. Additionally, miRNEST has been integrated with external miRNA data from literature and 13 databases, which includes miRNA sequences, small RNA sequencing data, expression, polymorphisms and targets data as well as links to external miRNA resources, whenever applicable. All this makes miRNEST a considerable miRNA resource in a sense of number of species (544) that integrates a scattered miRNA data into a uniform format with a user-friendly web interface.


doRiNA: a database of RNA interactions in post-transcriptional regulation
Gerd Anders1, Sebastian D. Mackowiak2, Marvin Jens2, Jonas Maaskola2, Andreas Kuntzagk1, Nikolaus Rajewsky2,*, Markus Landthaler3,* and Christoph Dieterich1,*
1Bioinformatics in Quantitative Biology, 2Systems Biology of Gene Regulatory Elements and 3RNA Biology and post-transcriptional regulation, Berlin Institute for Medical Systems Biology, Max Delbrück Centre for Molecular Medicine, Robert-Rössle-Straße 10, 13125 Berlin, Germany
Nucl. Acids Res. (2012) 40 (D1): D180-D186

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.


starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data
Yang JH, Li JH, Shao P, Zhou H, Chen YQ, Qu LH.
Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou 510275, PR China.
Nucleic Acids Res. 2011 39(Database issue): D202-209

MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively. In this study, we introduce a novel database, starBase (sRNA target Base), which we have developed to facilitate the comprehensive exploration of miRNA-target interaction maps from CLIP-Seq and Degradome-Seq data. The current version includes high-throughput sequencing data generated from 21 CLIP-Seq and 10 Degradome-Seq experiments from six organisms. By analyzing millions of mapped CLIP-Seq and Degradome-Seq reads, we identified ∼1 million Ago-binding clusters and ∼2 million cleaved target clusters in animals and plants, respectively. Analyses of these clusters, and of target sites predicted by 6 miRNA target prediction programs, resulted in our identification of approximately 400,000 and approximately 66,000 miRNA-target regulatory relationships from CLIP-Seq and Degradome-Seq data, respectively. Furthermore, two web servers were provided to discover novel miRNA target sites from CLIP-Seq and Degradome-Seq data. Our web implementation supports diverse query types and exploration of common targets, gene ontologies and pathways. The starBase is available at http://starbase.sysu.edu.cn

NONCODE v3.0: integrative annotation of long noncoding RNAs
Dechao Bu1,2, Kuntao Yu1,2, Silong Sun1, Chaoyong Xie1,2, Geir Skogerbø3, Ruoyu Miao1,4, Hui Xiao1, Qi Liao1, Haitao Luo1, Guoguang Zhao1,2, Haitao Zhao4, Zhiyong Liu1, Changning Liu1, Runsheng Chen3,* and Yi Zhao1,*
1Bioinformatics Research Group, Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China, 2Graduate School of the Chinese Academy of Sciences, Beijing, PR China, 3Bioinformatics Laboratory and National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China and 4Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China
Nucl. Acids Res. (2012) 40 (D1): D210-D215
Access to the NONCODE database http://www.noncode.org

Facilitated by the rapid progress of high-throughput sequencing technology, a large number of long noncoding RNAs (lncRNAs) have been identified in mammalian transcriptomes over the past few years. LncRNAs have been shown to play key roles in various biological processes such as imprinting control, circuitry controlling pluripotency and differentiation, immune responses and chromosome dynamics. Notably, a growing number of lncRNAs have been implicated in disease etiology. With the increasing number of published lncRNA studies, the experimental data on lncRNAs (e.g. expression profiles, molecular features and biological functions) have accumulated rapidly. In order to enable a systematic compilation and integration of this information, we have updated the NONCODE database http://www.noncode.org to version 3.0 to include the first integrated collection of expression and functional lncRNA data obtained from re-annotated microarray studies in a single database. NONCODE has a user-friendly interface with a variety of search or browse options, a local Genome Browser for visualization and a BLAST server for sequence-alignment search. In addition, NONCODE provides a platform for the ongoing collation of ncRNAs reported in the literature. All data in NONCODE are open to users, and can be downloaded through the website or obtained through the SOAP API and DAS services.