Icing: Large-scale inference of immunoglobulin clonotypes

Abstract

Immunoglobulin (IG) clonotype identification is a fundamental open question in modern immunology. An accurate description of the IG repertoire is crucial to understand the variety within the immune system of an individual, potentially shedding light on the pathogenetic process. Intrinsic IG heterogeneity makes clonotype inference an extremely challenging task, both from a computational and a biological point of view. Here we present icing, a framework that allows to reconstruct clonal families also in case of highly mutated sequences. icing has a modular structure, and it is designed to be used with large next generation sequencing (NGS) datasets, a technology which allows the characterisation of large-scale IG repertoires. We extensively validated the framework with clustering performance metrics on the results in a simulated case. icing is implemented in Python, and it is publicly available under FreeBSD licence at https://github.com/slipguru/icing.