Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR

Abstract

Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), has been shown to be of critical importance for immune responses to pathogens and vaccines. In recent years, B cell and T cell receptor repertoire sequencing (Rep-Seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci in different populations. Here we present a novel algorithm for extra-sensitive and specific variable (V) and joining (J) gene allele inference and genotyping allowing reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput genotyping and novel allele discovery from a wide variety of existing datasets. The developed algorithm is a part of the MiXCR platform (https://mixcr.com) and can be incorporated into any pipeline utilizing upstream processing with MiXCR. We demonstrate the accuracy of this approach using Rep-Seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of heavy chain Rep-Seq data from 428 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain Rep-Seq dataset, representing 134 individuals. This allowed us to assess the genetic diversity of genes within the immunoglobulin heavy chain (IGH) and TCR alpha and beta loci (TRA; TRB) in different populations and demonstrate the connection between antibody repertoire gene usage and the number of allelic variants present in the population. Finally we established a database of allelic variants of V and J genes inferred from Rep-Seq data and their population frequencies with free public access at https://vdj.online.