Skip to main content

Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

  • Protocol
  • First Online:
Computational Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1819))

Abstract

Spatial analysis of functional enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of interest, and provides an intuitive visual representation of their relative positioning within the network. In doing so, SAFE determines which functions cocluster in a network, which parts of the network they are associated with and how they are potentially related to one another.

Here, I provide a detailed stepwise description of how to perform a SAFE analysis. As an example, I use SAFE to annotate the genome-scale genetic interaction similarity network from Saccharomyces cerevisiae with Gene Ontology (GO) biological process terms. In addition, I show how integrating GO with chemical genomic data in SAFE can recapitulate known modes of action of chemical compounds and potentially identify novel drug mechanisms.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
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

References

  1. Baryshnikova A (2016) Systematic functional annotation and visualization of biological networks. Cell Syst 2(6):412–421. https://doi.org/10.1016/j.cels.2016.04.014

    Article  CAS  PubMed  Google Scholar 

  2. Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JL, Toufighi K, Mostafavi S, Prinz J, St Onge RP, VanderSluis B, Makhnevych T, Vizeacoumar FJ, Alizadeh S, Bahr S, Brost RL, Chen Y, Cokol M, Deshpande R, Li Z, Lin ZY, Liang W, Marback M, Paw J, San Luis BJ, Shuteriqi E, Tong AH, van Dyk N, Wallace IM, Whitney JA, Weirauch MT, Zhong G, Zhu H, Houry WA, Brudno M, Ragibizadeh S, Papp B, Pal C, Roth FP, Giaever G, Nislow C, Troyanskaya OG, Bussey H, Bader GD, Gingras AC, Morris QD, Kim PM, Kaiser CA, Myers CL, Andrews BJ, Boone C (2010) The genetic landscape of a cell. Science 327(5964):425–431. https://doi.org/10.1126/science.1180823

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. DeRossi C, Vacaru A, Rafiq R, Cinaroglu A, Imrie D, Nayar S, Baryshnikova A, Milev MP, Stanga D, Kadakia D, Gao N, Chu J, Freeze HH, Lehrman MA, Sacher M, Sadler KC (2016) trappc11 is required for protein glycosylation in zebrafish and humans. Mol Biol Cell 27(8):1220–1234. https://doi.org/10.1091/mbc.E15-08-0557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ho B, Baryshnikova A, Brown GW (2017) Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome. bioRxiv. https://doi.org/10.1101/104919

  5. Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C, Tan G, Wang W, Usaj M, Hanchard J, Lee SD, Pelechano V, Styles EB, Billmann M, van Leeuwen J, van Dyk N, Lin ZY, Kuzmin E, Nelson J, Piotrowski JS, Srikumar T, Bahr S, Chen Y, Deshpande R, Kurat CF, Li SC, Li Z, Usaj MM, Okada H, Pascoe N, San Luis BJ, Sharifpoor S, Shuteriqi E, Simpkins SW, Snider J, Suresh HG, Tan Y, Zhu H, Malod-Dognin N, Janjic V, Przulj N, Troyanskaya OG, Stagljar I, Xia T, Ohya Y, Gingras AC, Raught B, Boutros M, Steinmetz LM, Moore CL, Rosebrock AP, Caudy AA, Myers CL, Andrews B, Boone C (2016) A global genetic interaction network maps a wiring diagram of cellular function. Science 353(6306). https://doi.org/10.1126/science.aaf1420

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hoepfner D, Helliwell SB, Sadlish H, Schuierer S, Filipuzzi I, Brachat S, Bhullar B, Plikat U, Abraham Y, Altorfer M, Aust T, Baeriswyl L, Cerino R, Chang L, Estoppey D, Eichenberger J, Frederiksen M, Hartmann N, Hohendahl A, Knapp B, Krastel P, Melin N, Nigsch F, Oakeley EJ, Petitjean V, Petersen F, Riedl R, Schmitt EK, Staedtler F, Studer C, Tallarico JA, Wetzel S, Fishman MC, Porter JA, Movva NR (2014) High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions. Microbiol Res 169(2-3):107–120. https://doi.org/10.1016/j.micres.2013.11.004

    Article  CAS  PubMed  Google Scholar 

  7. Cytoscape.org (2016) Cytoscape user manual. http://wiki.cytoscape.org/Cytoscape_3/ UserManual -Cytoscape_3.2BAC8 -UserManual.2BAC8-Navigation_Layout. Automatic_Layout_Algorithms. Accessed 6 Feb 2016

  8. Kamada T, Kawai S (1989) An algorithm for drawing general undirected graphs. Inf Process Lett 31(1):7–15

    Article  Google Scholar 

  9. 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(11):2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B, Arkin AP, Astromoff A, El-Bakkoury M, Bangham R, Benito R, Brachat S, Campanaro S, Curtiss M, Davis K, Deutschbauer A, Entian KD, Flaherty P, Foury F, Garfinkel DJ, Gerstein M, Gotte D, Guldener U, Hegemann JH, Hempel S, Herman Z, Jaramillo DF, Kelly DE, Kelly SL, Kotter P, LaBonte D, Lamb DC, Lan N, Liang H, Liao H, Liu L, Luo C, Lussier M, Mao R, Menard P, Ooi SL, Revuelta JL, Roberts CJ, Rose M, Ross-Macdonald P, Scherens B, Schimmack G, Shafer B, Shoemaker DD, Sookhai-Mahadeo S, Storms RK, Strathern JN, Valle G, Voet M, Volckaert G, Wang CY, Ward TR, Wilhelmy J, Winzeler EA, Yang Y, Yen G, Youngman E, Yu K, Bussey H, Boeke JD, Snyder M, Philippsen P, Davis RW, Johnston M (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418(6896):387–391. https://doi.org/10.1038/nature00935

    Article  CAS  PubMed  Google Scholar 

  11. Smith AM, Ammar R, Nislow C, Giaever G (2010) A survey of yeast genomic assays for drug and target discovery. Pharmacol Ther 127(2):156–164. https://doi.org/10.1016/j.pharmthera.2010.04.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ho CH, Piotrowski J, Dixon SJ, Baryshnikova A, Costanzo M, Boone C (2011) Combining functional genomics and chemical biology to identify targets of bioactive compounds. Curr Opin Chem Biol 15(1):66–78. https://doi.org/10.1016/j.cbpa.2010.10.023

    Article  CAS  PubMed  Google Scholar 

  13. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103(23):8577–8582. https://doi.org/10.1073/pnas.0601602103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mitra K, Carvunis AR, Ramesh SK, Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14(10):719–732. https://doi.org/10.1038/nrg3552

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Baryshnikova A (2016) Systematic functional annotation and visualization of biological networks. bioRxiv. https://doi.org/10.1101/030551

  16. Mo ML, Palsson BO, Herrgard MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 3:37. https://doi.org/10.1186/1752-0509-3-37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hartigan JA, Hartigan PM (1985) The dip test of unimodality. Ann Stat 13(1):70–84

    Article  Google Scholar 

  18. Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

  19. Yager R, Filev D (1994) Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst 2(3):209–219

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anastasia Baryshnikova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Baryshnikova, A. (2018). Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks. In: von Stechow, L., Santos Delgado, A. (eds) Computational Cell Biology. Methods in Molecular Biology, vol 1819. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8618-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8618-7_12

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8617-0

  • Online ISBN: 978-1-4939-8618-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics