Skip to main content
SpringerLink
Log in

Construction and Analysis of miRNA Regulatory Networks

  • Protocol
  • First Online:
MicroRNA Target Identification

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1970))

  • 1442 Accesses

Abstract

This chapter is devoted to illustrate the usage of state-of-the-art methodologies for miRNA regulatory network construction and analysis. Advantages in understanding the role of miRNAs in regulating gene expression are increasing the possibility of developing targeted therapies and drugs. This new possibility can be exploited by gaining new knowledge through analyzing interactions between a specific miRNA and a targeted gene.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
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. Flynt AS et al (2008) Biological principles of microRNA-mediated regulation: shared themes amid diversity. Nat Rev Genet 9(11):831–842. https://doi.org/10.1038/nrg2455nn

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gurtan AM et al (2013) The role of miRNAs in regulating gene expression networks. J Mol Biol 425(19):3582–3600. https://doi.org/10.1016/j.jmb.2013.03.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hobert O (2008) Gene regulation by transcription factors and microRNAs. Science 319(5871):1785–1786. https://doi.org/10.1126/science.1151651

    Article  CAS  PubMed  Google Scholar 

  4. Baglioni M, Russo F, Geraci F, Rizzo M, Rainaldi G, Pellegrini M (2015) A new method for discovering disease-specific MiRNA-target regulatory networks. PLoS One 10(4). https://doi.org/10.1371/journal.pone.0122473

    Article  PubMed  PubMed Central  Google Scholar 

  5. Russo F, Belling K, Jensen AB, Scoyni F, Brunak S, Pellegrini M (2017) MicroRNAs, regulatory networks, and comorbidities: decoding complex systems. In: Dalmay T (ed) MicroRNA detection and target identification, Methods in molecular biology, vol 1580. Humana Press, New York, NY, pp 281–295

    Chapter  Google Scholar 

  6. R Core Team (2017) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  7. Cava C, Colaprico A, Bertoli G et al (2017) SpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data. Int J Mol Sci 18(2):274. https://doi.org/10.3390/ijms18020274

    Article  CAS  PubMed Central  Google Scholar 

  8. Pajak M, Simpson TI (2016) miRNAtap: miRNAtap: microRNA targets–aggregated predictions. R package version 112.0

    Google Scholar 

  9. Bonnici V et al (2018) http://arenaidb.ba.itb.cnr.it. Accessed 30 Apr 2018

  10. Fan Y, Siklenka K, Arora SK, Ribeiro P, Kimmins S, Xia J (2016) miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res 44(Web Server issue):W135–W141. https://doi.org/10.1093/nar/gkw288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bisognin A, Sales G, Coppe A, Bortoluzzi S, Romualdi C (2012) MAGIA2: from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits. Nucleic Acids Res 40(Web Server issue):W13–W21. https://doi.org/10.1093/nar/gks460n

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Huber W, Carey VJ, Gentleman R et al (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12(2):115–121. https://doi.org/10.1038/nmeth.3252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. RStudio Team (2015) RStudio: integrated development for R. RStudio, Inc., Boston, MA. URL http://www.rstudio.com/

    Google Scholar 

  14. Alexa A, Rahnenfuhrer J (2016) topGO: enrichment analysis for gene ontology. R package version 2.30.1

    Google Scholar 

  15. Colaprico A, Silva TC, Olsen C et al (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44(8):e71. https://doi.org/10.1093/nar/gkv1507

    Article  CAS  PubMed  Google Scholar 

  16. Rukov JL, Wilentzik R, Jaffe I, Vinther J, Shomron N (2014) Pharmaco-miR: linking microRNAs and drug effects. Brief Bioinform 15(4):648–659. https://doi.org/10.1093/bib/bbs082

    Article  CAS  PubMed  Google Scholar 

  17. Warde-Farley D, Donaldson SL, Comes O et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38(Web Server issue):W214–W220. https://doi.org/10.1093/nar/gkq537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Grillari J, Hackl M, Grillari-Voglauer R (2010) miR-17–92 cluster: ups and downs in cancer and aging. Biogerontology 11(4):501–506. https://doi.org/10.1007/s10522-010-9272-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hossain A, Kuo MT, Saunders GF (2006) Mir-17-5p regulates breast cancer cell proliferation by inhibiting translation of AIB1 mRNA. Mol Cell Biol 26(21):8191–8201. https://doi.org/10.1128/MCB.00242-06

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Agarwal V, Bell GW, Nam J-W, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. elife 4:e05005. https://doi.org/10.7554/eLifhbe.05005

    Article  PubMed Central  Google Scholar 

  21. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S et al (2018) DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res 46(Database issue):D239–D245. https://doi.org/10.1093/nar/gkx1141

    Article  CAS  PubMed  Google Scholar 

  22. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human MicroRNA Targets. PLoS Biol 2(11):e363. https://doi.org/10.1371/journal.pbio.0020363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N (2005) Combinatorial microRNA target predictions. Nat Genet 37(5):495–500. https://doi.org/10.1038/ng1536

    Article  CAS  PubMed  Google Scholar 

  24. Wong N, Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43(D1):D146–D152. https://doi.org/10.1093/nar/gku1104

    Article  CAS  PubMed  Google Scholar 

  25. Carlson M (2017) org.Hs.eg.db: Genome wide annotation for Human. R package version 3.5.0

    Google Scholar 

  26. Ashburner M, Ball CA, The Gene Ontology Consortium et al (2000) Gene Ontology: tool for the unification of biology. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. The Gene Ontology Consortium (2017) Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res 245(Database issue):D331–D338. https://doi.org/10.1093/nar/gkw1108

    Article  CAS  Google Scholar 

  28. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  CAS  PubMed  Google Scholar 

  29. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(Database issue):D353–D361. https://doi.org/10.1093/nar/gkw1092

    Article  CAS  PubMed  Google Scholar 

  30. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44(Database issue):D457–D462. https://doi.org/10.1093/nar/gkv1070

    Article  CAS  PubMed  Google Scholar 

  31. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sales G, Coppe A, Bisognin A, Biasiolo M, Bortoluzzi S, Romualdi C (2010) MAGIA, a web-based tool for miRNA and genes integrated analysis. Nucleic Acids Res 38(Web Server issue):W352–W359. https://doi.org/10.1093/nar/gkq423

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kertesz M et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284. https://doi.org/10.1038/ng2135

    Article  CAS  PubMed  Google Scholar 

  34. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 4(1):44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  35. Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13. https://doi.org/10.1093/nar/gkn923

    Article  CAS  Google Scholar 

  36. Pajak M and Simpson TI (2016) miRNAtap.db: Data for miRNAtap. R package version 0.99.10

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Verona, Verona, Italy

    Antonella Mensi, Vincenzo Bonnici, Simone Caligola & Rosalba Giugno

Authors
  1. Antonella Mensi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincenzo Bonnici
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Caligola
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rosalba Giugno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosalba Giugno .

Editor information

Editors and Affiliations

  1. Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA

    Alessandro Laganà

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Mensi, A., Bonnici, V., Caligola, S., Giugno, R. (2019). Construction and Analysis of miRNA Regulatory Networks. In: Laganà, A. (eds) MicroRNA Target Identification. Methods in Molecular Biology, vol 1970. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9207-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9207-2_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9206-5

  • Online ISBN: 978-1-4939-9207-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation