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Affinity maturation is crucial for improving the binding affinity ofantibodies to antigens. This process is mainly driven by point substitutionscaused by somatic hypermutations of the immunoglobulin gene. It also includesdeletions and insertions of genomic material known as indels. While thelandscape of point substitutions has been extensively studied, a detailedstatistical description of indels is still lacking. Here we present aprobabilistic inference tool to learn the statistics of indels from repertoiresequencing data, which overcomes the pitfalls and biases of standard annotationmethods. The model includes antibody-specific maturation ages to account forvariable mutational loads in the repertoire. After validation on syntheticdata, we applied our tool to a large dataset of human immunoglobulin heavychains. The inferred model allows us to identify universal statistical featuresof indels in heavy chains. We report distinct insertion and deletion hotspots,and show that the distribution of lengths of indels follows a geometricdistribution, which puts constraints on future mechanistic models of thehypermutation process.

Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies