Welcome to miR2Gene

The tool for gene pattern discovery by enrichment analysis of their microRNA regulators ia a web-accessible program, which is used to mine potential patterns for one single gene, multiple genes, and KEGG pathway genes based on miRNA set enrichment analysis of the miRNAs regulating given genes. For any given gene, multiple genes, or one pathway, miR2Gene is able to:

• explore enriched miRNA function class regulating given genes or pathways
• discover enriched miRNA family regulating given genes or pathways
• identify enriched miRNA cluster regulating given genes or pathways
• determine enriched miRNA disease class regulating given genes or pathways
• and identify enriched miRNA tissue specific class regulating given genes or pathways

Contact Us
Dr Qinghua Cui, 38 Xueyuan Rd, Department of Biomedical Informatics, Peking University Health Science Center, Beijing 100191, China
Email: cuiqinghua@hsc.pku.edu.cn
Homepage: http://www.cuilab.cn/

Best viewed with Mozilla Firefox 3.0+ or Internet Explorer 7.0+, and note that SVGview plugin must be installed if you use IE to view the pathway figures.

Select a KEGG pathway    ABC transporters Acute myeloid leukemia Adherens junction Adipocytokine signaling pathway Allograft rejection Alzheimer's disease Amyotrophic lateral sclerosis (ALS) Antigen processing and presentation Apoptosis Asthma Autoimmune thyroid disease Axon guidance B cell receptor signaling pathway Basal cell carcinoma Basal transcription factors Base excision repair Bladder cancer Calcium signaling pathway Cardiac muscle contraction Cell adhesion molecules (CAMs) Cell cycle Chemokine signaling pathway Chronic myeloid leukemia Circadian rhythm - mammal Colorectal cancer Complement and coagulation cascades Cytokine-cytokine receptor interaction DNA replication ECM-receptor interaction Endometrial cancer Epithelial cell signaling in Helicobacter pylori infection ErbB signaling pathway Fc epsilon RI signaling pathway Focal adhesion Gap junction Glioma GnRH signaling pathway Graft-versus-host disease Hedgehog signaling pathway Hematopoietic cell lineage Homologous recombination Huntington's disease Insulin signaling pathway Jak-STAT signaling pathway Leukocyte transendothelial migration Long-term depression Long-term potentiation MAPK signaling pathway Maturity onset diabetes of the young Melanogenesis Melanoma Mismatch repair mTOR signaling pathway Natural killer cell mediated cytotoxicity Neuroactive ligand-receptor interaction Non-homologous end-joining Non-small cell lung cancer Notch signaling pathway Nucleotide excision repair Olfactory transduction p53 signaling pathway Pancreatic cancer Parkinson's disease Pathogenic Escherichia coli infection Pathways in cancer Phosphatidylinositol signaling system PPAR signaling pathway Primary immunodeficiency Prostate cancer Proteasome Protein export Regulation of actin cytoskeleton Regulation of autophagy Renal cell carcinoma Renin-angiotensin system Ribosome RNA polymerase Shigellosis Small cell lung cancer SNARE interactions in vesicular transport Systemic lupus erythematosus T cell receptor signaling pathway Taste transduction TGF-beta signaling pathway Thyroid cancer Tight junction Toll-like receptor signaling pathway Type I diabetes mellitus Type II diabetes mellitus Ubiquitin mediated proteolysis Vascular smooth muscle contraction VEGF signaling pathway Vibrio cholerae infection Wnt signaling pathway

select predicted method    TargetScan MicroCosm DIANA-microT-3.0

Single gene analysis All genes analysis

miR2Gene Tutorial

Chengxiang Qiu and Qinghua Cui

November, 2010

miR2Gene is a web server to explore hidden patterns for protein-coding genes through enrichment analysis of their microRNA (miRNA) regulators. miR2Gene first integrated miRNAs into groups according to prior rules, such as function, associated disease, tissue specificity, family, and cluster. miR2Gene then predicted the miRNAs that regulate given protein-coding genes using several algorithms. Next, an enrichment analysis of miRNA regulators in various miRNA sets is performed. Finally, the enriched miRNA sets are identified for given protein-coding genes. miR2Gene can be used for a single gene, multiple genes, or KEGG pathways.

[1]. Log on miR2Gene web-server

Users can log on the miR2Gene web-server at http://www.cuilab.cn/mir2gene, as shown in Figure 1.

Figure 1. The homepage of miR2Gene

[2]. Pattern discovery for protein-coding genes or KEGG pathways

Here, taking a single gene "ABL2" and KEGG pathway "wnt signaling pathway" as examples.

(1) Analysis of single gene. The analysis procedure is as follows:

Figure 2. The analysis procedure of miR2Gene for single gene

1. Click the menu "Single Gene" (red "1" in Figure 2).
2. Enter the gene name or ID (red "2" in Figure 2). Here is "ABL2".
3. Select the gene identifiers (red "3" in Figure 2). Currently, miR2Gene support seven types of gene identifiers. They are Official Gene Symbol, Entrez Gene ID, Ensembl Gene ID, UCSC Gene ID, REFSEQ MRNA ID, and GeneBank Accession Number. Here is “Official Gene Symbol ".
4. Choose a miRNA target prediction algorithm to identify the miRNAs that regulate the given gene (target). Currently, three algorithms (TargetScan, MicroCosm, and DIANA-microT-3.0) are provided in miR2Gene. Here is “TargetScan ".
5. Click button "Run" to submit request to miR2Gene. The result will be shown in a new page.
6. "Click here" and the miRNAs in orange denote the miRNA that regulate the input genes.

(2) Interpretation of the analysis result for single gene.

Figure 3. The Result (part result) of analysis result for gene ABL2.

The results (miRNA sets) are organized into five categories, miRNA cluster, miRNA family, Function, HMDD (miRNA associated disease), and TissueSpecific (Figure 3). The users can rank the results by values of Count (number of matched miRNAs), Percent (percentage of matched miRNAs in corresponding miRNA set), Fold (The real matched number/expected matched number), P-value, Bonferroni (corrected p value by Bonferroni), FDR (corrected p value by FDR). The significantly enriched miRNA sets are putatively associated with the given protein-coding gene. One important point the users should take care is that because the relationship between genes and their miRNA regulators is negative regulation, the discovered function should be reversed in some times. For example, the most significant miRNA set in Functional category is "miRNA tumor suppressors", which means the gene "ABL2" have very strong tendency of oncogene. Indeed, ABL2 is an oncogene.

(3) Analysis of KEGG pathways. The analysis procedure is as follows:

Figure 4. The analysis procedure of miR2Gene for KEGG pathway

1. Click the menu "KEGG Pathway" (red "1" in Figure 4).
2. Select a KEGG pathway. Here we select "Wnt signaling pathway".
3. Select a miRNA target prediction method. Here we select "TargetScan".
4. Choose "Single gene analysis" or "All gene analysis". "Single gene analysis" is to analyze genes in a KEGG pathway one by one and then integrated the results (default). "All gene analysis" is to analyze genes in a KEGG pathway together. That is, miR2Gene takes all the miRNAs that at least regulate one of the pathway genes as the interested miRNAs.
5. Click "Run" to submit the request to miR2Gene. The result will be shown in a new page.

(4) Interpretation of the analysis result for KEGG pathway.

Figure 5. The Result (part result) of analysis result for Wnt signaling pathway.

A new page will be shown for the result of KEGG pathway analysis (Figure 5). The enriched miRNA sets (FDR<=0.05) are listed and organized by the above five categories (red "1" in Figure 5). The number of genes having corresponding enriched miRNA sets in selected pathway is also listed (red "2" in Figure 5). Clicking corresponding miRNA set, the significant genes will be highlighted in the pathway. A function to hide or show all highlighted genes in the pathway is also provided (red "3" in Figure 5). Furthermore, all genes in the pathway are also listed (red "4" in Figure 5). Clicking any of the listed genes will show the significant miRNA sets. After finishing tuning all the parameters, the resulted figure of the pathway will be shown (red "5" in Figure 5), in which, the significant genes are highlighted in yellow color.

[3]. Reference & Contact Information

Qiu et al. miR2Gene: Gene pattern discovery by enrichment analysis of their microRNA regulators. (submitted).

Dr. Qinghua Cui, http://www.cuilab.cn/, cuiqinghua@hsc.pku.edu.cn