MAP Quest

Identify the active miRNA-target regulation and infer the regulatory network
in context-specific manner (cancer types or subtypes) by input miRNA symbols and/or gene symbols.


Choose a cancer type of your interest. The public data of the cancer type will be used in learning the correlation between miRNAs and mRNAs (and/or lncRNA) and contruscting the regulatory network. TCGA RNA-seq data and GSE microarray datasets have been applied in this work so far. And the differential expression results of miRNAs and mRNAs have been calculated between high-stage tumors and low-stage tumors.


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, Epithelial mesenchymal transition, Hippo, MAPK, p53, PI3K-Akt, PPAR, Ras, 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:
MirTarget PITA TargetScan
miRanda mirMAP miRNATAP miRNAWalker2
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.


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 target 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.