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The ability to predict and correspondingly manipulate an adaptive immune response would be highly valuable for biotechnology and medicine. To achieve this requires a greater molecular understanding of adaptive immunity.  Recent advances in high-throughput immunoglobulin (antibody) repertoire sequencing (Ig-seq) are enabling highly quantitative analysis of adaptive immune responses. This increased immunological insight has been applied to fields as varied as lymphocyte development and differentiation, immunodiagnostics discovery, vaccine development, cancer immunotherapy, and monoclonal antibody discovery. Here I will present, the computational and experimental methods my group has developed in Ig-seq and their associated applications. For, example we have recently developed a comprehensive error and bias correction method that enables highly accurate Ig-seq. We are also applying machine-learning approaches that enable prediction of the immune status of individual antibody clones. Finally, I will also highlight recent work in our group aimed towards engineering immune cells with genome editing technology, for applications in antibody engineering and cellular immunotherapy.