Per-sample immunoglobulin germline inference from B cell receptor deep sequencing data

TL;DR

This paper introduces a new method for accurately inferring individual immunoglobulin germline gene sets from B cell receptor sequencing data, addressing inaccuracies caused by incomplete and spurious allele databases.

Contribution

The authors present a novel inference method that improves germline gene set identification over existing approaches, integrated into the partis tool.

Findings

Reduces spurious allele inference compared to traditional alignment methods.

Improves accuracy of B cell receptor sequence annotation.

Available in the partis software package.

Abstract

The collection of immunoglobulin genes in an individual's germline, which gives rise to B cell receptors via recombination, is known to vary significantly across individuals. In humans, for example, each individual has only a fraction of the several hundred known V alleles. Furthermore, the currently-accepted set of known V alleles is both incomplete (particularly for non-European samples), and contains a significant number of spurious alleles. The resulting uncertainty as to which immunoglobulin alleles are present in any given sample results in inaccurate B cell receptor sequence annotations, and in particular inaccurate inferred naive ancestors. In this paper we first show that the currently widespread practice of aligning each sequence to its closest match in the full set of IMGT alleles results in a very large number of spurious alleles that are not in the sample's true set of germline V alleles. We then describe a new method for inferring each individual's germline gene set from deep sequencing data, and show that it improves upon existing methods by making a detailed comparison on a variety of simulated and real data samples. This new method has been integrated into the partis annotation and clonal family inference package, available at https://github.com/psathyrella/partis, and is run by default without affecting overall run time.