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These applications fall over diverse classes, however they are mainly designed to infer the effect of several miRNAs over the transcriptomic or proteomic outputs. Typically they compare the expression levels of miRNAs and relate them with the mRNA levels of their predicted or validated targets in a particular experiment. There are also applications that analyze the combined effect of several miRNAs over gene targets. To run the applications the user must tipically supply expression data for miRNAs and genes or proteins, or a list of miRNAs and/or targets.

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Reference

miRNet

 An excellent and easy-to-use tool with comprehensive support for statistical analysis and functional interpretation of data generated in miRNAs studies. Allows graphical representations of miRNA-mRNA interactions and a great variery of inputs. Uses validated miRNA targets.

Human, mouse, rat, chicken, cow, zebrafish, fly, C. elegans and S. mansoni

 PMID: 27105848

BioVLab-MMIA

 BioVLAB-MMIA-NGS is Cloud-based miRNA mRNA integraed analysis system using NGS data. System computes differentially/significantly expressed miRNAs (DEmiRNAs) and mRNAs/genes (DEGs), and with targeting information, DEGs targeted by DEmiRNAs and having negative correlation between them are extracted. Human, mouse, Rhesus monkey and Rice PMID: 25270639

miRTarVis+

 miRTarVis+ has an intuitive interface to support the analysis pipeline of load, filter, predict, and visualize. It can predict targets of miRNA by adopting Bayesian inference and maximal information-based nonparametric exploration (MINE) analyses as well as conventional correlation and mutual information analyses. miRTarVis+ supports an integrative analysis of multiple prediction results by providing an overview of multiple prediction results and then allowing users to examine a selected miRNA-mRNA network in an interactive treemap and node-link diagram. Human and mouse PMID: 28600227

miRror2

 For a set of miRNAs, it reports a ranked list of gene-targets according to their likelihood to be targeted by this miRNA set. For a set of genes, it reports a ranked list of miRNAs according to their likelihood to coordinately target this gene set. Human, mouse, rat, fly, zebrafish and C. elegans PMID: 24907353

Mirvestigator

 This portal will analyze a list of co-expressed genes and it will return the most likely miRNA regulating these genes. The server computes the over-represented sequence motif in the 3'-UTR of the input list of genes and will compare it with a list of miRNA seeding sequences using a hidden Markov model.  Human, mouse, rat, chicken, zebrafish and fly PMID: 21602264

miRONTOP

 It is an online java web tool that integrates DNA microarrays or high-throughput sequencing data to identify the potential implication of miRNAs on a specific biological system. Human, mouse, rat and Sus scrofa PMID: 20959382

Sylarray

 Web server for the automatic detection of miRNA effects over expression data.  Human, mouse, rat, fly, zebrafish and C. elegans PMID: 20871108

ProteomiRExpress

 Integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed. Human and mouse PMID: 23924514

miRTrail

 miRTrail allows you to easily analyse for potential relationships between a set of miRNAs and a set of mRNAs. Human, mouse and zebrafish PMID: 22356618

miRConnX

 An 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. Human PMID: 21558324

Talasso

 Integrative analysis tool for the determination of miRNA-mRNA relationships. It uses several predictors that can be easily customized by the user. Human PMID: 22348024

Magia2

 It was designed to cope with low sensitivity of target prediction algorithms by exploiting the integration of target predictions with miRNA and gene expression profiles to improve the detection of functional miRNA–mRNA relationships. Human and mouse PMID: 22618880

dChip-GemiNi

 The dChip-GemiNI method identifies candidate transcription factor and microRNA regulators by statistically ranking computationally predicted feed forward loops that these regulators form with their common target genes. Therefore, dChip-GemiNI not only integrates gene and miRNA expression profiles but also the computationally derived information about transcription factor-target and microRNA-target interactions and generates biologically testable hypothesis. Human PMID: 22645320