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T-cell receptors are one of the many aspects of immunology studied with informatics tools.

Immunoinformatics (sometimes referred to as computational immunology) is a sub-branch of bioinformatics that focuses on the use of data management and computational tools to improve immunological research. The scope of immunoinformatics covers a wide variety of territory, from genomic and proteomic study of the immune system to molecular- and organism-level modeling, putting it in close ties with genome informatics.[1][2]


Immunology researchers like Hans-Georg Rammensee trace the history of immunoinformatics back to the study of theoretical immunology. In June 1987, the Theoretical Immunology Workshop was hosted in Santa Fe, New Mexico to discuss "the topics of immune surveillance, mathematical models of HIV infection, complexities of antigen-antibody systems, immune suppression and tolerance, and idiotypie networks."[3][4][5]

One of the first immunoinformatics efforts to result in a long-term informatics solution was the construction of the IMGT information system in 1989 by the Laboratoire d'ImmunoGénétique Moléculaire (LIGM). Created to "standardize and manage the complexity of the immunogenetics data" coming out of the lab, the information system went on to become an international public reference for genetic and proteomic data related to immunology.[6] In July 1995, IMGT moved to the web, and by 2007 it was handling more than 140,000 requests per month.[7]

In 1999, the SYFPEITHI database for MHC ligands and T-cell epitopes – a component of an antigen or antibody generator – went public.[8]

In 2003, the Anthony Nolan Research Institute and the European Bioinformatics Institute developed the Immuno Polymorphism Database (IPD) "to provide a centralised system for the study of polymorphism in genes of the immune system."[9]

As of January 2015, the Oxford University Press lists over 30 different immunological databases in popular use.[10]


Immunoinformatics can help tackle problems and tasks such as the following[6][1][11][12]:

  • improving the development of biological therapeutics
  • developing and improving analysis and data management solutions for immunogenetics
  • developing and improving allergen and other immunological databases
  • creating better mathematical and computer models of the immune system
  • predicting protein allergenicity for the screening of novel foods before wide-scale release and use
  • the design and development of peptide-based immuno-therapeutic drugs and vaccines


One of the key informatics tools of immunology researchers is computational analysis software and systems, including modeling and simulation software. With modern software, an immunological question can be asked, the question can be interpreted into a computer-based computation or simulation, and the resulting data can then be output and analyzed for meaningful answers. T-cell receptors (TCRs) are molecular proteins vital to the immune response, and their diversity is estimated to ranges from 107 to 1015 different clonotypes, highlighting the need for powerful computational tools to better understand their effects on immune response.[1] Immunological databases are another vital component of immunoinformatics, especially as other computational tools have increasingly produced large quantities of varied data. In 2014 Oxford Journals published a list of more than 30 such popular databases in its journal Nucleic Acids Research.[10]

Other informatics applications assist with text mining[13], information management[14][15], sequence analysis, and molecular interaction analysis. Attempts are also being made to extract interesting and complex patterns from non-structured text documents in the immunological domain, including allergen cross-reactivity information,[13] cancer-associated gene variant data, and immune epitope data.

See also

Further reading

External links


This article reuses an element or two from the Wikipedia article.


  1. 1.0 1.1 1.2 Brusic, Vladimir; Petrovsky, Nikolai; Novartis Foundation (2004). "Immunoinformatics - The new kid in town". Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function. John Wiley & Sons. pp. 3–22. ISBN 9780470090756. Retrieved 20 January 2015. 
  2. Flower, Darren R. (2007). "Immunoinformatics and the In Silico Prediction of Immunogenicity: An Introduction". Immunoinformatics: Predicting Immunogenicity in Silico. Springer Science & Business Media. pp. 1–15. ISBN 9781603271189. 
  3. "Theoretical Immunology, Part One". Westview Press. Archived from the original on 22 April 2016. Retrieved 06 January 2022. 
  4. Rammensee, Hans-Georg; Novartis Foundation (2004). "Chair's introduction". Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function. John Wiley & Sons. pp. 1–2. ISBN 9780470090756. Retrieved 20 January 2015. 
  5. Perelson, Alan S. (Ed.) (1988). Theoretical Immunology: The Proceedings of the Theoretical Immunology Workshop, held June 1987, in Santa Fe, New Mexico. 1 and 2. Addison-Wesley Pub. Co. pp. 811. ISBN 9780201156829. Retrieved 20 January 2015. 
  6. 6.0 6.1 Schönbach, Christian; Ranganathan, Shoba; Brusic, Vladimir (Eds.) (2007). Immunoinformatics. Springer Science & Business Media. pp. 200. ISBN 9780387729671. Retrieved 20 January 2015. 
  7. Lefranc, Marie-Paule; Flower, Darren R. (Ed.) (2007). "IMGT: The International ImmunoGeneTics Information System for Immunoinformatics". Immunoinformatics: Predicting Immunogenicity in Silico. Springer Science & Business Media. pp. 19–42. ISBN 9781603271189. Retrieved 20 January 2015. 
  8. Rammensee, H.; Bachmann, J; Emmerich, N. P.; Bachor, O. A.; Stevanović, S. (November 1999). "SYFPEITHI: Database for MHC ligands and peptide motifs". Immunogenetics 50 (3–4): 213–219. doi:10.1007/s002510050595. PMID 10602881. 
  9. "Immuno Polymorphism Database". EMBL-European Bioinformatics Institute. Retrieved 20 January 2015. 
  10. 10.0 10.1 "2014 NAR Database Summary Paper Category List". Oxford University Press. Retrieved 20 January 2015. 
  11. Tong, J.C.; Tammi, M.T. (2008). "Methods and protocols for the assessment of protein allergenicity and cross-reactivity". Frontiers in Bioscience 13: 4882–4888. doi:10.2741/3047. PMID 18508553. 
  12. Brusic, V.; van Endert, P.; Zeleznikow, J.; Daniel, S.; Hammer, J.; Petrovsky, N. (1999). "A neural network model approach to the study of human TAP transporter". In Silico Biology 1 (2): 109–21. PMID 11471244. 
  13. 13.0 13.1 Miotto, O.; Tan, T.W.; Brusic, V. (2005). "Supporting the curation of biological databases with reusable text mining". Genome Inform 16 (2): 32–44. PMID 16901087. 
  14. McDonald, R.; Scott Winters, R.; Ankuda, C.K. et al. (2006). "An automated procedure to identify biomedical articles that contain cancer-associated gene variants". Human Mutation 27 (9): 957–64. doi:10.1002/humu.20363. PMID 16865690. 
  15. Wang, P.; Morgan, A.A.; Zhang, Q.; Sette, A.; Peters, B. (2007). "Automating document classification for the Immune Epitope Database". BMC Bioinformatics 8: 269. doi:10.1186/1471-2105-8-269. PMC 1965490. PMID 17655769.