Bioinformatics in Primary Immunodeficiencies: Towards a “Computational Immunology” Era

Primary immunodeficiencies are diseases caused by defects in the immune system that cause increased susceptibility to infections, autoimmune reactions, and inflammation. Understanding the immune system is essential to developing novel treatments and maintaining general health, but its complexity, heterogeneity, and large datasets generated by research requiring extensive analysis and sophisticated algorithms limit our comprehension. To this end, “Computational immunology” uses bioinformatics tools to transform immunology datasets into computationally accessible information, contributing significantly to the field. Here, we aim to discuss genetic and genomic approaches, such as Sanger and NGS sequencing, respectively, and their scalability towards primary immunodeficiencies’ diagnosis. Likewise, we attempt to provide a concise description of the most frequently applied algorithms for B- and T-cell receptor structural prediction and to also define the integration of multi-omics approaches (including single-cell RNA sequencing, metagenomics, proteomics, and metabolomics) into diagnostics and biomarkers discovery. We will also explore the use of computational immunology for drug discovery, by harnessing case studies of gene therapies, drug repurposing, and protein druggability. Likewise, we highlight the role of artificial intelligence and machine learning in primary immunodeficiency detection, as well as how these disciplines shape the field’s future directions. Finally, we contemplate on current challenges in computational immunology, as an insight into future research directions in this scientific arena.