Skip to main content

Computing and Visualizing Gene Function Similarity and Coherence with NaviGO

  • Protocol
  • First Online:
Data Mining for Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1807))

Abstract

Gene ontology (GO) is a controlled vocabulary of gene functions across all species, which is widely used for functional analyses of individual genes and large-scale proteomic studies. NaviGO is a webserver for visualizing and quantifying the relationship and similarity of GO annotations. Here, we walk through functionality of the NaviGO webserver (http://kiharalab.org/web/navigo/) using an example input and explain what can be learned from analysis results. NaviGO has four main functions, accessed from each page of the webserver: “GO Parents,” “GO Set”, “GO Enrichment”, and “Protein Set.” For a given list of GO terms, the “GO Parents” tab visualizes the hierarchical relationship of GO terms, and the “GO Set” tab calculates six functional similarity and association scores and presents results in a network and a multidimensional scaling plot. For a set of proteins and their associated GO terms, the “GO Enrichment” tab calculates protein GO functional enrichment, while the “Protein Set” tab calculates functional association between proteins. The NaviGO source code can be also downloaded and used locally or integrated into other software pipelines.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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. Consortium GO (2013) Gene ontology annotations and resources. Nucleic Acids Res 41(D1):D530–D535

    Article  CAS  Google Scholar 

  2. Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J, Davis A, Dolinski K, Dwight S, Eppig J (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Wei Q, Khan IK, Ding Z, Yerneni S, Kihara D (2017) NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology. BMC Bioinformatics 18(1):177. https://doi.org/10.1186/s12859-017-1600-5

    Article  PubMed  PubMed Central  Google Scholar 

  4. Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S (2009) AmiGO: online access to ontology and annotation data. Bioinformatics 25(2):288–289. https://doi.org/10.1093/bioinformatics/btn615

    Article  PubMed  CAS  Google Scholar 

  5. Binns D, Dimmer E, Huntley R, Barrell D, O'donovan C, Apweiler R (2009) QuickGO: a web-based tool for gene ontology searching. Bioinformatics 25(22):3045–3046

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hawkins T, Luban S, Kihara D (2006) Enhanced automated function prediction using distantly related sequences and contextual association by PFP. Protein Sci 15(6):1550–1556. https://doi.org/10.1110/ps.062153506

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Hawkins T, Chitale M, Luban S, Kihara D (2009) PFP: automated prediction of gene ontology functional annotations with confidence scores using protein sequence data. Proteins 74(3):566–582. https://doi.org/10.1002/prot.22172

    Article  PubMed  CAS  Google Scholar 

  8. Chitale M, Hawkins T, Park C, Kihara D (2009) ESG: extended similarity group method for automated protein function prediction. Bioinformatics 25(14):1739–1745. https://doi.org/10.1093/bioinformatics/btp309

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Khan IK, Qing W, Kihara D (2015) PFP/ESG: automated protein function prediction servers enhanced with gene ontology visualization tool. Bioinformatics 31(2):271–272. https://doi.org/10.1093/bioinformatics/btu646

    Article  PubMed  CAS  Google Scholar 

  10. Pundir S, Martin MJ, O'Donovan C (2017) UniProt protein knowledgebase. Methods Mol Biol 1558:41–55. https://doi.org/10.1007/978-1-4939-6783-4_2

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Dieterle M, Bauer D, Büche C, Krenz M, Schäfer E, Kretsch T (2005) A new type of mutation in phytochrome A causes enhanced light sensitivity and alters the degradation and subcellular partitioning of the photoreceptor. Plant J 41(1):146–161

    Article  CAS  PubMed  Google Scholar 

  12. Nito K, Wong CC, Yates JR, Chory J (2013) Tyrosine phosphorylation regulates the activity of phytochrome photoreceptors. Cell Rep 3(6):1970–1979

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Al-Sady B, Ni W, Kircher S, Schäfer E, Quail PH (2006) Photoactivated phytochrome induces rapid PIF3 phosphorylation prior to proteasome-mediated degradation. Mol Cell 23(3):439–446

    Article  CAS  PubMed  Google Scholar 

  14. Liu X, Chen C-Y, Wang K-C, Luo M, Tai R, Yuan L, Zhao M, Yang S, Tian G, Cui Y (2013) PHYTOCHROME INTERACTING FACTOR3 associates with the histone deacetylase HDA15 in repression of chlorophyll biosynthesis and photosynthesis in etiolated Arabidopsis seedlings. Plant Cell 25(4):1258–1273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ito S, Nakamichi N, Nakamura Y, Niwa Y, Kato T, Murakami M, Kita M, Mizoguchi T, Niinuma K, Yamashino T (2007) Genetic linkages between circadian clock-associated components and phytochrome-dependent red light signal transduction in Arabidopsis thaliana. Plant Cell Physiol 48(7):971–983

    Article  CAS  PubMed  Google Scholar 

  16. Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007

    Google Scholar 

  17. Lin D (1998) An information-theoretic definition of similarity. In: ICML, vol 1998. Citeseer, pp 296–304

    Google Scholar 

  18. Schlicker A, Domingues F, Rahnenführer J, Lengauer T (2006) A new measure for functional similarity of gene products based on gene ontology. BMC Bioinformatics 7:302. https://doi.org/10.1186/1471-2105-7-302

    Article  PubMed  PubMed Central  Google Scholar 

  19. Yerneni S, Khan I, Wei Q, Kihara D (2015) IAS: interaction specific GO term associations for predicting protein-protein interaction networks. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2015.2476809

  20. Chitale M, Palakodety S, Kihara D (2011) Quantification of protein group coherence and pathway assignment using functional association. BMC Bioinformatics 12(1):373

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hawkins T, Chitale M, Kihara D (2010) Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP. Bmc Bioinformatics 11(1):265

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Clack T, Shokry A, Moffet M, Liu P, Faul M, Sharrock RA (2009) Obligate heterodimerization of Arabidopsis phytochromes C and E and interaction with the PIF3 basic helix-loop-helix transcription factor. Plant Cell 21(3):786–799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45(D1):D362–D368. https://doi.org/10.1093/nar/gkw937

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We thank Charles Christoffer for proofreading the manuscript. This work was partly supported by the National Institute of General Medical Sciences of the NIH (R01GM123055) and the National Science Foundation (DBI1262189, IOS1127027, DMS1614777).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daisuke Kihara .

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

Ding, Z., Wei, Q., Kihara, D. (2018). Computing and Visualizing Gene Function Similarity and Coherence with NaviGO. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8561-6_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8560-9

  • Online ISBN: 978-1-4939-8561-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics