Skip to main content

Visualization and Analysis of MiRNA–Targets Interactions Networks

  • Protocol
  • First Online:
MicroRNA Profiling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1509))

Abstract

MicroRNAs are a class of small, noncoding RNA molecules of 21–25 nucleotides in length that regulate the gene expression by base-pairing with the target mRNAs, mainly leading to down-regulation or repression of the target genes. MicroRNAs are involved in diverse regulatory pathways in normal and pathological conditions. In this context, it is highly important to identify the targets of specific microRNA in order to understand the mechanism of its regulation and consequently its involvement in disease. However, the microRNA target identification is experimentally laborious and time-consuming. The in silico prediction of microRNA targets is an extremely useful approach because you can identify potential mRNA targets, reduce the number of possibilities and then, validate a few microRNA–mRNA interactions in an in vitro experimental model. In this chapter, we describe, in a simple way, bioinformatics guidelines to use miRWalk database and Cytoscape software for analyzing microRNA–mRNA interactions through their visualization as a network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294:858–862

    Article  CAS  PubMed  Google Scholar 

  2. Lee Y, Jeon K, Lee J-T, Kim S, Kim VN (2002) MicroRNA maturation: stepwise processing and subcellular localization. EMBO J 21:4663–4670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rodriguez A, Griffiths-Jones S, Ashurst JL, Bradley A (2004) Identification of mammalian microRNA host genes and transcription units. Genome Res 14:1902–1910

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kim VN (2005) MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol 6:376–385

    Article  CAS  PubMed  Google Scholar 

  5. Sontheimer EJ (2005) Assembly and function of RNA silencing complexes. Nat Rev Mol Cell Biol 6:127–138

    Article  CAS  PubMed  Google Scholar 

  6. Iwakawa H-O, Tomari Y (2015) The functions of MicroRNAs: mRNA decay and translational repression. Trends Cell Biol 25:651–665

    Article  CAS  PubMed  Google Scholar 

  7. Liu G, Zhang R, Xu J, Wu C-I, Lu X (2015) Functional conservation of both CDS- and 3′-UTR-located microRNA binding sites between species. Mol Biol Evol 32:623–628

    Article  CAS  PubMed  Google Scholar 

  8. Zhou H, Rigoutsos I (2014) MiR-103a-3p targets the 5′ UTR of GPRC5A in pancreatic cells. RNA 20:1431–1439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ardekani AM, Naeini MM (2010) The role of MicroRNAs in human diseases. Avicenna J Med Biotechnol 2:161–179

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Ivey KN, Srivastava D (2015) microRNAs as developmental regulators. Cold Spring Harb Perspect Biol 7:a008144

    Article  CAS  PubMed  Google Scholar 

  11. Abente EJ, Subramanian M, Ramachandran V, Najafi-Shoushtari SH (2015) MicroRNAs in obesity-associated disorders. Arch Biochem Biophys. doi:10.1016/j.abb.2015.09.018

    PubMed  Google Scholar 

  12. Femminella GD, Ferrara N, Rengo G (2015) The emerging role of microRNAs in Alzheimer’s disease. Front Physiol 6:40

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jansson MD, Lund AH (2012) MicroRNA and cancer. Mol Oncol 6:590–610

    Article  CAS  PubMed  Google Scholar 

  14. Lim LP, Glasner ME, Yekta S, Burge CB, Bartel DP (2003) Vertebrate microRNA genes. Science 299:1540

    Article  CAS  PubMed  Google Scholar 

  15. Burgos K, Malenica I, Metpally R, Courtright A, Rakela B, Beach T, Shill H, Adler C, Sabbagh M, Villa S et al (2014) Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer’s and Parkinson's diseases correlate with disease status and features of pathology. PLoS One 9, e94839

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk–database: prediction of possible miRNA binding sites by ‘walking’ the genes of three genomes. J Biomed Inform 44:839–847

    Article  CAS  PubMed  Google Scholar 

  17. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Scardoni G, Petterlini M, Laudanna C (2009) Analyzing biological network parameters with CentiScaPe. Bioinformatics 25:2857–2859

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449

    Article  CAS  PubMed  Google Scholar 

  20. Hirsch EC, Hunot S (2009) Neuroinflammation in Parkinson’s disease: a target for neuroprotection? Lancet Neurol 8:382–397

    Article  CAS  PubMed  Google Scholar 

  21. He F, Balling R (2013) The role of regulatory T cells in neurodegenerative diseases. Wiley Interdiscip Rev Syst Biol Med 5:153–180

    Article  CAS  PubMed  Google Scholar 

  22. Schwartz M, Kipnis J, Rivest S, Prat A (2013) How do immune cells support and shape the brain in health, disease, and aging? J Neurosci 33:17587–17596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Agarwal V, Bell GW, Nam J-W, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4

    Google Scholar 

  24. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human MicroRNA targets. PLoS Biol 2:e363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Simossis VA et al (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics 10:295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K et al (2009) DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res 37:W273–W276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Miranda KC, Huynh T, Tay Y, Ang Y-S, Tam W-L, Thomson AM, Lim B, Rigoutsos I (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126:1203–1217

    Article  CAS  PubMed  Google Scholar 

  28. Kruger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bandyopadhyay S, Mitra R (2009) TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics 25:2625–2631

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by Proyecto Mineduc-UDD PMI 1204 “De la ciencia a la innovación en salud: adopción en la actividad clínica, nacional e internacional, de nuevos productos, procesos y prácticas de clase mundial, basados en investigación científica de la UDD y de terceros.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luis E. León .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this protocol

Cite this protocol

León, L.E., Calligaris, S.D. (2017). Visualization and Analysis of MiRNA–Targets Interactions Networks. In: Rani, S. (eds) MicroRNA Profiling. Methods in Molecular Biology, vol 1509. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6524-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6524-3_19

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6522-9

  • Online ISBN: 978-1-4939-6524-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics