IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data

Ayelet Peres, William D. Lees, Oscar L. Rodriguez, Noah Y. Lee, Pazit Polak, Ronen Hope, Meirav Kedmi, Andrew M. Collins, Mats Ohlin, Steven H. Kleinstein, Corey T. Watson, Gur Yaari*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles, as well as a novel method to infer individual genotypes. We demonstrate the strengths of the two by comparing their outcomes to other genotype inference methods. We validate the genotype approach with independent genomic long-read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET https://bitbucket.org/yaarilab/piglet). To allow researchers to further explore the approach on real data and to adapt it for their uses, we also created an interactive website (https://yaarilab.github.io/IGHV-reference-book).

Original languageEnglish
Pages (from-to)E86
JournalNucleic Acids Research
Volume51
Issue number16
DOIs
StatePublished - 8 Sep 2023

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