IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data
(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).
(Less)
- author
- organization
- publishing date
- 2023-09-08
- 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}}, }