Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The BioPAX community standard for pathway data sharing

A Corrigendum to this article was published on 10 April 2012

A Corrigendum to this article was published on 07 December 2010

This article has been updated

Abstract

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: BioPAX is a shared language for biological pathways.
Figure 2: BioPAX enables computational data gathering, publication and use of information about biological processes.
Figure 3: The AKT pathway as represented by a traditional method (top left; from http://www.biocarta.com/), a formalized SBGN diagram (left; from http://www.sbgn.org/62) and using the BioPAX language (right).
Figure 4: High-level view of the BioPAX ontology.
Figure 5: Example uses of pathway information in BioPAX format.
Figure 6: The relationship among popular standard formats for pathway information.

Similar content being viewed by others

Change history

  • 07 December 2010

    In the version of this article initially published, the affiliation for Ken Fukuda should be Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan. The error has been corrected in the HTML and PDF versions of the article.

  • 10 April 2012

    In the version of this article initially published, Oliver Reubenacker should have been spelled Oliver Ruebenacker. In addition, the location of the author's affiliation is Farmington, not Storrs. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. Gasteiger, E. et al. ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31, 3784–3788 (2003).

    Article  CAS  Google Scholar 

  2. Nicholson, D.E. The evolution of the IUBMB-Nicholson maps. IUBMB Life 50, 341–344 (2000).

    Article  CAS  Google Scholar 

  3. Demir, E. et al. PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 18, 996–1003 (2002).

    Article  CAS  Google Scholar 

  4. Krull, M. et al. TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res. 34, D546–D551 (2006).

    Article  CAS  Google Scholar 

  5. Fukuda, K. & Takagi, T. Knowledge representation of signal transduction pathways. Bioinformatics 17, 829–837 (2001).

    Article  CAS  Google Scholar 

  6. Davidson, E.H. et al. A genomic regulatory network for development. Science 295, 1669–1678 (2002).

    Article  CAS  Google Scholar 

  7. Kohn, K.W. Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol. Biol. Cell 10, 2703–2734 (1999).

    Article  CAS  Google Scholar 

  8. Matthews, L. et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37, D619–D622 (2009).

    Article  CAS  Google Scholar 

  9. Schaefer, C.F. et al. PID: the Pathway Interaction Database. Nucleic Acids Res. 37, D674–D679 (2009).

    Article  CAS  Google Scholar 

  10. Bader, G.D. & Hogue, C.W. BIND—a data specification for storing and describing biomolecular interactions, molecular complexes and pathways. Bioinformatics 16, 465–477 (2000).

    Article  CAS  Google Scholar 

  11. Kitano, H. A graphical notation for biochemical networks. BIOSILICO 1, 169–176 (2003).

    Article  CAS  Google Scholar 

  12. Gama-Castro, S. et al. RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucleic Acids Res. 36, D120–D124 (2008).

    Article  CAS  Google Scholar 

  13. Mi, H. et al. The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 33, D284–D288 (2005).

    Article  CAS  Google Scholar 

  14. Keseler, I.M. et al. EcoCyc: a comprehensive view of Escherichia coli biology. Nucleic Acids Res. 37, D464–D470 (2009).

    Article  CAS  Google Scholar 

  15. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 38, D473–D479 (2010).

    Article  CAS  Google Scholar 

  16. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32 Database issue, D277–280 (2004).

  17. Bader, G.D., Cary, M.P. & Sander, C. Pathguide: a pathway resource list. Nucleic Acids Res. 34, D504–D506 (2006).

    Article  CAS  Google Scholar 

  18. Huang, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  Google Scholar 

  19. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).

    Article  Google Scholar 

  20. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  21. Karp, P.D. et al. Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief. Bioinform. 11, 40–79 (2010).

    Article  CAS  Google Scholar 

  22. Hu, Z. et al. VisANT 3.0: new modules for pathway visualization, editing, prediction and construction. Nucleic Acids Res. 35, W625–W632 (2007).

    Article  Google Scholar 

  23. Hoffmann, R. et al. Text mining for metabolic pathways, signaling cascades, and protein networks. Sci. STKE 2005, pe21 (2005).

    PubMed  Google Scholar 

  24. Racunas, S.A., Shah, N.H., Albert, I. & Fedoroff, N.V. HyBrow: a prototype system for computer-aided hypothesis evaluation. Bioinformatics 20 Suppl 1, i257–i264 (2004).

    Article  CAS  Google Scholar 

  25. Cary, M.P., Bader, G.D. & Sander, C. Pathway information for systems biology. FEBS Lett. 579, 1815–1820 (2005).

    Article  CAS  Google Scholar 

  26. Vivanco, I. & Sawyers, C.L. The phosphatidylinositol 3-Kinase AKT pathway in human cancer. Nat. Rev. Cancer 2, 489–501 (2002).

    Article  CAS  Google Scholar 

  27. Koh, G., Teong, H.F., Clement, M.V., Hsu, D. & Thiagarajan, P.S. A decompositional approach to parameter estimation in pathway modeling: a case study of the Akt and MAPK pathways and their crosstalk. Bioinformatics 22, e271–e280 (2006).

    Article  CAS  Google Scholar 

  28. Karp, P.D. An ontology for biological function based on molecular interactions. Bioinformatics 16, 269–285 (2000).

    Article  CAS  Google Scholar 

  29. Joshi-Tope, G. et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 33 Database issue, D428–D432 (2005).

    Article  CAS  Google Scholar 

  30. Mi, H., Guo, N., Kejariwal, A. & Thomas, P.D. PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Res. 35, D247–D252 (2007).

    Article  CAS  Google Scholar 

  31. Demir, E. et al. An ontology for collaborative construction and analysis of cellular pathways. Bioinformatics 20, 349–356 (2004).

    Article  CAS  Google Scholar 

  32. Bader, G.D., Betel, D. & Hogue, C.W. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31, 248–250 (2003).

    Article  CAS  Google Scholar 

  33. Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004).

    Article  CAS  Google Scholar 

  34. Chatr-aryamontri, A. et al. MINT: the Molecular INTeraction database. Nucleic Acids Res. 35, D572–D574 (2007).

    Article  CAS  Google Scholar 

  35. Kerrien, S. et al. IntAct—open source resource for molecular interaction data. Nucleic Acids Res. 35, D561–D565 (2007).

    Article  CAS  Google Scholar 

  36. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).

    Article  CAS  Google Scholar 

  37. Matys, V. et al. TRANSFAC(R) and its module TRANSCompel(R): transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108–D110 (2006).

    Article  CAS  Google Scholar 

  38. Kerrien, S. et al. Broadening the horizon—level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biol. 5, 44 (2007).

    Article  Google Scholar 

  39. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  CAS  Google Scholar 

  40. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  41. Eilbeck, K. et al. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol. 6, R44 (2005).

    Article  Google Scholar 

  42. Yamamoto, S., Asanuma, T., Takagi, T. & Fukuda, K.I. The molecule role ontology: an ontology for annotation of signal transduction pathway molecules in the scientific literature. Comp. Funct. Genomics 5, 528–536 (2004).

    Article  CAS  Google Scholar 

  43. Cerami, E.G., Bader, G.D., Gross, B.E. & Sander, C. cPath: open source software for collecting, storing, and querying biological pathways. BMC Bioinformatics 7, 497 (2006).

    Article  Google Scholar 

  44. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

    Article  CAS  Google Scholar 

  45. Efroni, S., Carmel, L., Schaefer, C.G. & Buetow, K.H. Superposition of transcriptional behaviors determines gene state. PLoS ONE 3, e2901 (2008).

    Article  Google Scholar 

  46. Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A.F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1, S233–S240 (2002).

    Article  Google Scholar 

  47. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  48. Wu, G., Feng, X. & Stein, L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 11, R53 (2010).

    Article  Google Scholar 

  49. Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010).

    Article  CAS  Google Scholar 

  50. Isserlin, R. et al. Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps. Proteomics 10, 1316–1327 (2010).

    Article  CAS  Google Scholar 

  51. Moraru, I.I. et al. Virtual Cell modelling and simulation software environment. IET Syst. Biol. 2, 352–362 (2008).

    Article  CAS  Google Scholar 

  52. Hlavacek, W.S. et al. Rules for modeling signal-transduction systems. Sci. STKE 2006, re6 (2006).

    PubMed  Google Scholar 

  53. Pico, A.R. et al. WikiPathways: pathway editing for the people. PLoS Biol. 6, e184 (2008).

    Article  Google Scholar 

  54. Kitano, H., Funahashi, A., Matsuoka, Y. & Oda, K. Using process diagrams for the graphical representation of biological networks. Nat. Biotechnol. 23, 961–966 (2005).

    Article  CAS  Google Scholar 

  55. Lloyd, C.M., Halstead, M.D. & Nielsen, P.F. CellML: its future, present and past. Prog. Biophys. Mol. Biol. 85, 433–450 (2004).

    Article  CAS  Google Scholar 

  56. Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

    Article  CAS  Google Scholar 

  57. Sauro, H.M. et al. Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS 7, 355–372 (2003).

    Article  CAS  Google Scholar 

  58. Hermjakob, H. et al. The HUPO PSI's molecular interaction format—a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183 (2004).

    Article  CAS  Google Scholar 

  59. Racunas, S.A., Shah, N.H. & Fedoroff, N.V. A case study in pathway knowledgebase verification. BMC Bioinformatics 7, 196 (2006).

    Article  Google Scholar 

  60. Laibe, C. & Le Novere, N. MIRIAM Resources: tools to generate and resolve robust cross-references in Systems Biology. BMC Syst. Biol. 1, 58 (2007).

    Article  Google Scholar 

  61. Berners-Lee, T. & Hendler, J. Publishing on the semantic web. Nature 410, 1023–1024 (2001).

    Article  CAS  Google Scholar 

  62. Le Novere, N. et al. The Systems Biology Graphical Notation. Nat. Biotechnol. 27, 735–741 (2009).

    Article  CAS  Google Scholar 

  63. Knublauch, H., Fergerson, R.W., Noy, N.F. & Musen, M.A. The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications. in The Semantic Web–ISWC 2004: Third International Semantic Web Conference, Hiroshima, Japan, November 7-11, 2004: Proceedings (eds. McIlraith, S.A., Dimitris Plexousakis, D. & van Harmelen, F.) 229—243 (Springer, 2004).

  64. Sowa, J.F. Knowledge Representation: Logical, Philosophical, and Computational Foundations (Brooks/Cole, 2000).

  65. Wheeler, D.L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 35, D5–D12 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Funded by the US Department of Energy workshop grant DE-FG02-04ER63931, the caBIG program, the US National Institute of General Medical Sciences workshop grant 1R13GM076939, grant P41HG004118 from the US National Human Genome Research Institute and Genome Canada through the Ontario Genomics Institute (2007-OGI-TD-05) and US National Institutes of Health grant R01GM071962-07. Thanks to many people who contributed to discussions on BioPAX mailing lists, at conferences and at BioPAX workshops, especially A. Ruttenberg and J. Rees.

Author information

Authors and Affiliations

Authors

Contributions

All authors helped develop the BioPAX language, ontology, documentation and examples by participating in workshops or on mailing lists and/or provided data in BioPAX format and/or wrote software that supports BioPAX. See Supplementary Table 5 for a full list of author contributions.

Corresponding author

Correspondence to Gary D Bader.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–5 and Supplementary Fig. 1 (PDF 767 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Demir, E., Cary, M., Paley, S. et al. The BioPAX community standard for pathway data sharing. Nat Biotechnol 28, 935–942 (2010). https://doi.org/10.1038/nbt.1666

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1666

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing