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

Peres, Ayelet ; Lees, William D. ; Rodriguez, Oscar L. ; Lee, Noah Y. ; Polak, Pazit ; Hope, Ronen ; Kedmi, Meirav ; Collins, Andrew M. ; Ohlin, Mats LU orcid and Kleinstein, Steven H. , et al. (2023) In Nucleic Acids Research 51(16). p.86-86
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... (More)

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).

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nucleic Acids Research
volume
51
issue
16
pages
86 - 86
publisher
Oxford University Press
external identifiers
  • pmid:37548401
  • scopus:85170295336
ISSN
0305-1048
DOI
10.1093/nar/gkad603
language
English
LU publication?
yes
id
5bdb412c-1306-46a4-a115-254c38193b12
date added to LUP
2023-10-24 10:54:31
date last changed
2024-04-19 02:46:54
@article{5bdb412c-1306-46a4-a115-254c38193b12,
  abstract     = {{<p>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).</p>}},
  author       = {{Peres, Ayelet and Lees, William D. and Rodriguez, Oscar L. and Lee, Noah Y. and Polak, Pazit and Hope, Ronen and Kedmi, Meirav and Collins, Andrew M. and Ohlin, Mats and Kleinstein, Steven H. and Watson, Corey T. and Yaari, Gur}},
  issn         = {{0305-1048}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{16}},
  pages        = {{86--86}},
  publisher    = {{Oxford University Press}},
  series       = {{Nucleic Acids Research}},
  title        = {{IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data}},
  url          = {{http://dx.doi.org/10.1093/nar/gkad603}},
  doi          = {{10.1093/nar/gkad603}},
  volume       = {{51}},
  year         = {{2023}},
}