IMGT®, the international ImMunoGeneTics information system®, http://www.imgt.org/, is at the forefront of the immunogenetics and immunoinformatics fields with more than 30 years of experience. IMGT® makes available databases and tools to the scientific community pertaining to the adaptive immune response, based on the IMGT-ONTOLOGY. We focus on the recent features of the IMGT® databases, tools, reference directories and web resources, within the three main axes of IMGT® research and development. Axis I consists in understanding the adaptive immune response, by deciphering the identification and characterization of the immunoglobulin (IG) and T cell receptor (TR) genes in jawed vertebrates. It is the starting point of the two other axes, namely the analysis and exploration of the expressed IG and TR repertoires based on comparison with IMGT reference directories in normal and pathological situations (Axis II) and the analysis of amino acid changes and functions of 2D and 3D structures of antibody and TR engineering (Axis III).

Nowadays, Deep Learning is taking the world by a storm, known as a technology that makes use of Artificial Neural Networks to automatically extrapolate knowledge from a training data set, then uses this knowledge to give predictions for unseen samples. This data driven paradigm gained a widespread adoption in many disciplines, from handwriting recognition, driving an autonomous car to cracking the 50-year-old protein folding problem. With this review, we shed some light on the concepts of Deep Learning and provide some visualizations, skim over the different architectures such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and touch upon the modern architectures such as Transformers and BERT. We also provide various examples targeting the genomics field, reference utilities, libraries useful for newcomers and disseminate our feedback.

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