Abstract

Significance High-throughput sequencing of B-cell immunoglobulin receptors is providing unprecedented insight into adaptive immunity. A key step in analyzing these data involves assignment of the germline variable (V), diversity (D), and joining (J) gene-segment alleles that comprise each immunoglobulin sequence by matching them against a database of known V(D)J alleles. However, this process will fail for sequences that use previously undetected alleles, whose frequency in the population is unclear. Here we describe TIgGER, a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by a subject, including novel alleles. The application of TIgGER identifies a surprisingly high frequency of novel alleles, highlighting the critical need for this approach.