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

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...

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Autores principales: Peres, Ayelet, Lees, William D, Rodriguez, Oscar L, Lee, Noah Y, Polak, Pazit, Hope, Ronen, Kedmi, Meirav, Collins, Andrew M, Ohlin, Mats, Kleinstein, Steven H, Watson, Corey T, Yaari, Gur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484671/
https://www.ncbi.nlm.nih.gov/pubmed/37548401
http://dx.doi.org/10.1093/nar/gkad603
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author Peres, Ayelet
Lees, William D
Rodriguez, Oscar L
Lee, Noah Y
Polak, Pazit
Hope, Ronen
Kedmi, Meirav
Collins, Andrew M
Ohlin, Mats
Kleinstein, Steven H
Watson, Corey T
Yaari, Gur
author_facet Peres, Ayelet
Lees, William D
Rodriguez, Oscar L
Lee, Noah Y
Polak, Pazit
Hope, Ronen
Kedmi, Meirav
Collins, Andrew M
Ohlin, Mats
Kleinstein, Steven H
Watson, Corey T
Yaari, Gur
author_sort Peres, Ayelet
collection PubMed
description 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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spelling pubmed-104846712023-09-08 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 Kleinstein, Steven H Watson, Corey T Yaari, Gur Nucleic Acids Res Methods 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). Oxford University Press 2023-08-07 /pmc/articles/PMC10484671/ /pubmed/37548401 http://dx.doi.org/10.1093/nar/gkad603 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods
Peres, Ayelet
Lees, William D
Rodriguez, Oscar L
Lee, Noah Y
Polak, Pazit
Hope, Ronen
Kedmi, Meirav
Collins, Andrew M
Ohlin, Mats
Kleinstein, Steven H
Watson, Corey T
Yaari, Gur
IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
title IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
title_full IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
title_fullStr IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
title_full_unstemmed IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
title_short IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
title_sort ighv allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484671/
https://www.ncbi.nlm.nih.gov/pubmed/37548401
http://dx.doi.org/10.1093/nar/gkad603
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