Skip to main content

Reverse Engineering Transcriptional Gene Networks

  • Protocol
  • First Online:
Gene Function Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1101))


The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.

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

Access this chapter

USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Similar content being viewed by others


  1. Belcastro V, Siciliano V, Gregoretti F, Mithbaokar P, Dharmalingam G, Berlingieri S, Iorio F, Oliva G, Polishchuck R, Brunetti-Pierri N et al (2011) Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function. Nucleic Acids Res 39:8677–8688

    Article  PubMed  CAS  Google Scholar 

  2. Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM et al (2011) NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res 39:D1005–D1010

    Article  PubMed  CAS  Google Scholar 

  3. Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, Holloway E, Kolesnykov N, Lilja P, Lukk M et al (2007) ArrayExpress — a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 35:D747–D750

    Article  PubMed  CAS  Google Scholar 

  4. Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M, Emam I, Farne A, Hastings E, Holloway E et al (2011) ArrayExpress update — an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 39:D1002–D1004

    Article  PubMed  CAS  Google Scholar 

  5. Brazma A (2009) Minimum information about a microarray experiment (MIAME) — successes, failures, challenges. Sci World J 9:420–423

    Article  CAS  Google Scholar 

  6. Rayner TF, Rocca-Serra P, Spellman PT, Causton HC, Farne A, Holloway E, Irizarry RA, Liu J, Maier DS, Miller M et al (2006) A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinformatics 7:489

    Article  PubMed  Google Scholar 

  7. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315:972–976

    Article  PubMed  CAS  Google Scholar 

  8. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264

    Article  PubMed  Google Scholar 

  9. Pepper SD, Saunders EK, Edwards LE, Wilson CL, Miller CJ (2007) The utility of MAS5 expression summary and detection call algorithms. BMC Bioinformatics 8:273

    Article  PubMed  Google Scholar 

  10. Liu H, Hussain F, Tan C, Dash M (2002) Discretization: an enabling technique. Data Min Knowl Disc 6:393–423

    Article  Google Scholar 

  11. Ceol A, Chatr Aryamontri A, Licata L, Peluso D, Briganti L, Perfetto L, Castagnoli L, Cesareni G (2010) MINT, the molecular interaction database: 2009 update. Nucleic Acids Res 38:D532–D539

    Article  PubMed  CAS  Google Scholar 

  12. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E et al (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861

    Article  PubMed  CAS  Google Scholar 

  13. Goebel B, Dawy Z, Hagenauer J, Mueller JC (2005) An approximation to the distribution of finite sample size mutual information estimates. IEEE International Conference on Communications, Seoul, South Korea (Vol. 2, p. 11021106). Ieee. doi:10.1109/ICC.2005.1494518.

    Google Scholar 

  14. Langfelder P, Zhang B, Horvath S (2008) Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24:719–720

    Article  PubMed  CAS  Google Scholar 

  15. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550

    Article  PubMed  CAS  Google Scholar 

  16. Cacciottolo M, Belcastro V, Laval S, Bushby K, di Bernardo D, Nigro V (2011) Reverse engineering gene network identifies new dysferlin-interacting proteins. J Biol Chem 286:5404–5413

    Article  PubMed  CAS  Google Scholar 

  17. Huang Y, Laval SH, van Remoortere A, Baudier J, Benaud C, Anderson LV, Straub V, Deelder A, Frants RR, den Dunnen JT et al (2007) AHNAK, a novel component of the dysferlin protein complex, redistributes to the cytoplasm with dysferlin during skeletal muscle regeneration. FASEB J 21:732–742

    Article  PubMed  Google Scholar 

  18. Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H et al (2010) The transcriptional network for mesenchymal transformation of brain tumours. Nature 463:318–325

    Article  PubMed  CAS  Google Scholar 

  19. Kauffmann A, Rayner TF, Parkinson H, Kapushesky M, Lukk M, Brazma A, Huber W (2009) Importing ArrayExpress datasets into R/Bioconductor. Bioinformatics 25:2092–2094

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Belcastro, V., di Bernardo, D. (2014). Reverse Engineering Transcriptional Gene Networks. In: Ochs, M. (eds) Gene Function Analysis. Methods in Molecular Biology, vol 1101. Humana Press, Totowa, NJ.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-720-4

  • Online ISBN: 978-1-62703-721-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics