Bayesian approach for quantifying selection in high-throughput antibody sequencing datasets.
Affinity maturation is a unique and fascinating process in which low affinity B cells undergo rapid proliferation, somatic hypermutation and selection for binding to a specific antigen, resulting in higher affinity antibodies. Quantifying this antigen driven selection based on mutation patterns observed in antibody DNA sequences can provide insights into the basic biology that underlies physiologic and pathological adaptive immune responses, and may further serve as diagnostic and prognostic markers. High-throughput sequencing approaches make large-scale characterization of B cell antibody repertoires feasible. However, analyzing selection in these large datasets, which can contain millions of sequences, presents fundamental challenges requiring the development of new techniques.
In this talk I will present a new computational framework for bayesian quantification of antigen-driven selection (BASELINe) in antibody sequences. BASELINe provides a more intuitive means to analyze selection by shifting the problem from one of detecting selection to one of quantifying it. The approach also allows, for the first time, comparative analysis between groups of sequences derived from different germlines. We have made this method practical for complete repertoire analysis by making available a highly optimized implementation.