Combining mutation and recombination statistics to infer clonal families in antibody repertoires

TL;DR

HILARy is a novel, efficient method that combines mutation and recombination data to accurately identify B-cell clonal families from sequencing datasets, enhancing understanding of immune repertoire evolution.

Contribution

The paper introduces HILARy, a new probabilistic and clustering approach that improves clonal family inference accuracy by leveraging phylogenetic signals and mutation statistics.

Findings

HILARy achieves high inference accuracy across datasets.

Evolutionary statistics are independent of junction length.

A wide range of selection pressures are detected.

Abstract

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.