  Affinity maturation is crucial for improving the binding affinity of
antibodies to antigens. This process is mainly driven by point substitutions
caused by somatic hypermutations of the immunoglobulin gene. It also includes
deletions and insertions of genomic material known as indels. While the
landscape of point substitutions has been extensively studied, a detailed
statistical description of indels is still lacking. Here we present a
probabilistic inference tool to learn the statistics of indels from repertoire
sequencing data, which overcomes the pitfalls and biases of standard annotation
methods. The model includes antibody-specific maturation ages to account for
variable mutational loads in the repertoire. After validation on synthetic
data, we applied our tool to a large dataset of human immunoglobulin heavy
chains. The inferred model allows us to identify universal statistical features
of indels in heavy chains. We report distinct insertion and deletion hotspots,
and show that the distribution of lengths of indels follows a geometric
distribution, which puts constraints on future mechanistic models of the
hypermutation process.
