MAP Quest

Construct the miRNA-gene regulatory network by uploading your expression data matrix.


Choose your file:  


You can upload your expression matrix here and run the analysis. We encourage the normalized expression matrix rather than the raw reads. The expression matrix should merge the data of miRNAs and mRNAs following the same sample order. The column names should be samples and row names should be the gene symbols. And the following rows above the expression matrix should contain the classification information of samples, e.g., the tumor/treatment/experimental/cluster123. This data will be used in learning the correlations between miRNAs and mRNAs and contruscting the regulatory network. In default settings, all of your differentially expressed mRNAs and miRNAs will be analyzed. You can also input your own gene list in the advanced options. Please download the example file (right and save) to see the file format.



Optional: compute the differential expression of miRNAs and genes between sample types

cutoff absolute(log2FC):  (0 ~ 50)

cutoff P-value:  (0 ~ 1)

Genes: (Blank the inputs):

(Load differentially expressed genes, or load: WNT, mTOR, Apoptosis, Notch_signaling, TGF-beta_signaling, VEGF_signaling, Cell_adhesion, NOD-like, Jak-STAT Cell cycle pathway genes, or input the gene names by yourself. )

Input mRNAs of your interest or differentially expressed mRNAs in your samples. Gene names/EntrezGene Ids should be separated by semicolons, commas or returns, e.g. FGF1;MUC18. When no genes are given, all of the target genes corresponding to the given miRNAs list will be used to construct the network.



miRNAs: (Blank the inputs):

(Load differentially expressed miRNAs, or load examples: hsa-let-7a-3p; hsa-miR-15a-5p; hsa-miR-125a-5p; hsa-miR-181b-5p; hsa-miR-200c-3p; hsa-miR-361-5p; hsa-miR-590-3p; , or input the miRNA names by yourself.)

Input miRNAs of your interest or differentially expressed miRNAs in your samples. miRNAs names/miRBase ids should be separated by semicolons, commas or returns, e.g. hsa-mir-576;hsa-mir-140;hsa-miR-6740-5p. When no miRNAs are given, all of the miRNAs predicted to target the given gene list will be used to construct the network.


Validated miRNA-gene interactions:
mir2Disease miRNAWalker2 miRTarBase
Predicted miRNA-gene interactions:
miRanda mirMAP miRNATAP miRNAWalker2
MirTarget PITA TargetScan
Gene-gene interactions:
STRING Pathway commons HumanNet

Select methods/algorithms predicting miRNA-mRNA and gene-gene interactions and datasets verified by experiments. These interactions will be presented as edges in the output network.


Select your own data files.

Notice: It takes several seconds to do the computation, please kindly wait.

MatrixEQTL    

absolute(correlation beta):  
(0.05 ~ 1)
P-value:  
(1e-9 ~ 0.05)

Correlation between lncRNA-gene to infer sponge regulation     

Correlation P-value cutoff:  
(1e-9 ~ 0.05)

Filter the result by differential expression

Necessary number of interaction evidences  

(mir2Disease ,miRNAWalker2, miRTarBase, MirTarget ,PITA, TargetScan, miRanda, mirMAP, miRNATAP)


Multiple Regression (OLS model)     

Reference:

Agarwal, V., et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015;4:e05005.

Betel, D., et al. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome biology 2010;11(8):R90.

Dweep, H. and Gretz, N. miRWalk2. 0: a comprehensive atlas of microRNA-target interactions. Nature methods 2015;12(8):697-697.

Hsu, S.-D., et al. miRTarBase: a database curates experimentally validated microRNA¨Ctarget interactions. Nucleic acids research 2010:gkq1107.

Jiang, Q., et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic acids research 2009;37(suppl 1):D98-D104.

Kertesz, M., et al. The role of site accessibility in microRNA target recognition. Nature genetics 2007;39(10):1278-1284.

Pajak, M. and Simpson, T.I. miRNAtap. db: microRNA Targets-Aggregated Predictions database use. 2014.

Vejnar, C.E. and Zdobnov, E.M. MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic acids research 2012;40(22):11673-11683.

Wong, N. and Wang, X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic acids research 2014:gku1104.