With the advent of sequencing the entire genomes of humans and animals, there is a huge accumulation of immunologically relevant data. In addition, epidemiological and clinical immunological data can be accessed in publicly available databases (Tomar & De, 2010). To interpret and apply this gold mine of data effectively, researchers need to utilize informatics in a time and cost-effective manner. This has led to the rapid development of immunoinformatics, which stands as a new field interfacing between genomics, computational methods, and experimental immunology (Alberts et al., 2002; Oli et al., 2020). Immunoinformatics makes use of computer-based methods to solve immunological problems. One application is the prediction of B- and T-cell epitopes, thus opening new windows for potential translational research (Fig. 1).

fig1. Applications of immunoinformatics in clinical and genomic medicine

One of the principal objectives of immunoinformatics is to predict immunogenicity and antigenicity (Flower, 2007). It involves the use of software, computational tools, databases for T cell and B cell epitope prediction, vac cine design, protein structure comparisons, MHC characterization and gene analysis, immune system modeling, and network analysis of immune system data (Evans, 2008; Tomar & De, 2010, 2014). NERVE, Vaxign, and RANKPEP are software programs widely used for reverse vaccinology (RV) (Moxon et al., 2019; Reche et al., 2002).

References

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Alberts, B., Johnson, A., Lewis, J., et al. (2002). T cells and MHC proteins. Molecular biology of the cell (4th ed). Garland Science Available from. Available from https://www.ncbi.nlm. nih.gov/books/NBK26926/ Accessed 24.12.20.

Evans, M. C. (2008). Recent advances in immunoinformatics: Application of in silico tools to drug development. Current Opinion in Drug Discovery & Development, 11(2), 233 241.

Flower, D. R. (2007). Immunoinformatics and the in silico prediction of immunogenicity. An introduction. Methods in Molecular Biology., 409, 1 15. Available from https://doi.org/ 10.1007/978-1-60327-118-9_1. Available from 18449989.

Moxon, R., Reche, P. A., & Rappuoli, R. (2019). Editorial: Reverse vaccinology. Frontiers in Immunology, 10, 2776. Available from https://doi.org/10.3389/fimmu.2019.02776. Available from 31849959, PMCID: PMC6901788.

Oli, A. N., Obialor, W. O., Ifeanyichukwu, M. O., Odimegwu, D. C., Okoyeh, J. N., Emechebe, G. O., Adejumo, S. A., & Ibeanu, G. C. (2020). Immunoinformatics and vaccine development: An overview. Immunotargets and Therapy., 9,13 30. Available from https://doi.org/ 10.2147/ITT.S241064, eCollection 2020. Review. PubMed. Available from 32161726.

Reche, P. A., Glutting, J. P., & Reinherz, E. L. (2002). Prediction of MHC class I binding peptides using profile motifs. Human Immunology, 63(9), 701 709. Available from https://doi. org/10.1016/s0198-8859(02)00432-9. Available from 12175724.

Tomar, N., & De, R. K. (2010). Immunoinformatics: An integrated scenario. Immunology, 131 (2), 153 168. Available from https://doi.org/10.1111/j.1365-2567.2010.03330.x, Epub 2010 Aug 16. Available from 20722763

Tomar, N., & De, R. K. (2014). Immunoinformatics: A brief review. Methods in Molecular Biology, 1184, 2 3 55. Available from https://doi.org/10.1007/978-1-4939-1115-8_3. Available from 25048118.