RNA Mapping pp 289-305 | Cite as

miRWalk Database for miRNA–Target Interactions

  • Harsh DweepEmail author
  • Norbert Gretz
  • Carsten Sticht
Part of the Methods in Molecular Biology book series (MIMB, volume 1182)


miRWalk ( is a publicly available comprehensive resource, hosting the predicted as well as the experimentally validated microRNA (miRNA)–target interaction pairs. This database allows obtaining the possible miRNA-binding site predictions within the complete sequence of all known genes of three genomes (human, mouse, and rat). Moreover, it also integrates many novel features such as a comparative platform of miRNA-binding sites resulting from ten different prediction datasets, a holistic view of genetic networks of miRNA–gene pathway, and miRNA–gene–Online Mendelian Inheritance in Man disorder interactions, and unique experimentally validated information (e.g., cell lines, diseases, miRNA processing proteins). In this chapter, we describe a schematic workflow on how one can access the stored information from miRWalk and subsequently summarize its applications.

Key words

miRWalk MicroRNA Promoter Prediction 5′-UTR CDS 3′-UTR Pathways OMIM Validated Application 



This work is funded by the Research Council through Graduiertenkolleg 886 and by the German Federal Ministry of Research and Education through the National Genome Research Network (NGFN-2, Grant no. 01GR 0450).


  1. 1.
    Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297PubMedCrossRefGoogle Scholar
  2. 2.
    Kim DH, Saetrom P, Snove O Jr et al (2008) MicroRNA-directed transcriptional gene silencing in mammalian cells. Proc Natl Acad Sci U S A 105:16230–16235PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Lai EC (2002) Micro RNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation. Nat Genet 30:363–364PubMedCrossRefGoogle Scholar
  4. 4.
    Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Lee RC, Feinbaum RL, Ambros V (1993) The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:843–854PubMedCrossRefGoogle Scholar
  6. 6.
    Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20PubMedCrossRefGoogle Scholar
  7. 7.
    Enright AJ, John B, Gaul U et al (2003) MicroRNA targets in Drosophila. Genome Biol 5:R1PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Krek A, Grun D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500PubMedCrossRefGoogle Scholar
  9. 9.
    Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284PubMedCrossRefGoogle Scholar
  10. 10.
    Miranda KC, Huynh T, Tay Y et al (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126:1203–1217PubMedCrossRefGoogle Scholar
  11. 11.
    Kiriakidou M, Nelson PT, Kouranov A et al (2004) A combined computational-experimental approach predicts human microRNA targets. Genes Dev 18:1165–1178PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Wang X (2008) miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14:1012–1017PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Rehmsmeier M, Steffen P, Hochsmann M et al (2004) Fast and effective prediction of micro RNA/target duplexes. RNA 10:1507–1517PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Vergoulis T, Vlachos IS, Alexiou P et al (2012) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Hsu SD, Lin FM, Wu WY et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39:D163–D169PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Ruepp A, Kowarsch A, Schmidl D et al (2010) PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol 11: R6.Google Scholar
  17. 17.
    Jiang Q, Wang Y, Hao Y et al (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37:D98–D104PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Dweep H, Sticht C, Gretz N (2013) In-silico algorithms for the screening of possible microRNA binding sites and their interactions. Curr Genomics 14:127–136PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Guang S, Bochner AF, Pavelec DM et al (2008) An Argonaute transports siRNAs from the cytoplasm to the nucleus. Science 321:537–541PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Lytle JR, Yario TA, Steitz JA (2007) Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proc Natl Acad Sci U S A 104: 9667–9672PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Place RF, Li LC, Pookot D et al (2008) MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105:1608–1613PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Tay Y, Zhang J, Thomson AM et al (2008) MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature 455:1124–1128PubMedCrossRefGoogle Scholar
  23. 23.
    Rajewsky N (2006) microRNA target predictions in animals. Nat Genet 38:S8–S13PubMedCrossRefGoogle Scholar
  24. 24.
    Dweep H, Sticht C, Pandey P et al (2011) miRWalk – database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44: 839–847PubMedCrossRefGoogle Scholar
  25. 25.
    Kanehisa M, Goto S, Kawashima S et al (2002) The KEGG databases at GenomeNet. Nucleic Acids Res 30:42–46PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Dong J, Jiang G, Asmann YW et al (2010) MicroRNA networks in mouse lung organogenesis. PLoS One 5:e10854PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Ucar A, Vafaizadeh V, Jarry H et al (2010) miR-212 and miR-132 are required for epithelial stromal interactions necessary for mouse mammary gland development. Nat Genet 42: 1101–1108PubMedCrossRefGoogle Scholar
  28. 28.
    Ikemura K, Yamamoto M, Miyazaki S et al (2013) MicroRNA-145 post-transcriptionally regulates the expression and function of P-glycoprotein in intestinal epithelial cells. Mol Pharmacol 83:399–405PubMedCrossRefGoogle Scholar
  29. 29.
    Ho J, Ng KH, Rosen S et al (2008) Podocyte-specific loss of functional microRNAs leads to rapid glomerular and tubular injury. J Am Soc Nephrol 19:2069–2075PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Shi S, Yu L, Chiu C et al (2008) Podocyte-selective deletion of dicer induces proteinuria and glomerulosclerosis. J Am Soc Nephrol 19: 2159–2169PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Dweep H, Sticht C, Kharkar A et al (2013) Parallel analysis of mRNA and microRNA microarray profiles to explore functional regulatory patterns in polycystic kidney disease: using PKD/Mhm rat model. PLoS One 8: e53780PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Cirera-Salinas D, Pauta M, Allen RM et al (2012) Mir-33 regulates cell proliferation and cell cycle progression. Cell Cycle 11:922–933PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Wulfken LM, Moritz R, Ohlmann C et al (2011) MicroRNAs in renal cell carcinoma: diagnostic implications of serum miR-1233 levels. PLoS One 6:e25787PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Zhao C, Huang C, Weng T et al (2012) Computational prediction of MicroRNAs targeting GABA receptors and experimental verification of miR-181, miR-216 and miR-203 targets in GABA-A receptor. BMC Res Notes 5:91PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Papagregoriou G, Erguler K, Dweep H et al (2012) A miR-1207-5p binding site polymorphism abolishes regulation of HBEGF and is associated with disease severity in CFHR5 nephropathy. PLoS ONE 7:e31021PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Felekkis K, Voskarides K, Dweep H et al (2011) Increased number of microRNA target sites in genes encoded in CNV regions. Evidence for an evolutionary genomic interaction. Mol Biol Evol 28:2421–2424PubMedCrossRefGoogle Scholar
  37. 37.
    Durand C, Roeth R, Dweep H et al (2011) Alternative splicing and nonsense-mediated RNA decay contribute to the regulation of SHOX expression. PLoS One 6:e18115PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Bandiera S, Ruberg S, Girard M et al (2011) Nuclear outsourcing of RNA interference components to human mitochondria. PLoS One 6:e20746PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Xu LM, Li JR, Huang Y et al (2012) AutismKB: an evidence-based knowledgebase of autism genetics. Nucleic Acids Res 40:D1016–D1022PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Santamaria C, Muntion S, Roson B et al (2012) Impaired expression of DICER, DROSHA, SBDS and some microRNAs in mesenchymal stromal cells from myelodysplastic syndrome patients. Haematologica 97:1218–1224PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Medical Faculty Mannheim, Medical Research CenterUniversity of HeidelbergMannheimGermany

Personalised recommendations